Handbook of Neuroscience for the Behavioral Sciences, 2 Volume Set

Preface

in the neurosciences and behavioral sciences and informs about relevant theory, methods, and research in these two increasingly synergistic disciplines. The Handbook is designed to make neuroscience accessible to psychologists and other behavioral scientists with minimal background in biology or neuroscience, while at the same time offering information, constructs and approaches that will enhance the knowledge, teaching and research of active investigators at the intersection of psychology and neuroscience. In addition, the Handbook is designed to provide an accessible background in and to highlight currently active areas within the behavioral sciences for neuroscientists, who may have a minimal background in behavioral sciences. To accomplish the dual purposes of exposing behavioral scientists to neuroscience and neuroscientists to psychology, we have adopted a unique organization in this two volume Handbook. The first volume includes a Foundations section that features a set of chapters that provide brief introductions to major questions and approaches at distinct levels of analysis. These include topics such as the logical basis of integrative neuroscience, developmental processes, comparative approaches, biological rhythms, neuropharmacology, neuroendocrinology, neuroimmunology, neuroanatomy, neuropsychology, and functional neuroimaging. These chapters are overviews that provide the reader with a basic conceptual orientation to the types of approaches and measures available and how they can be applied and interpreted. These chapters provide the basic background to allow the reader to comprehend and evaluate the subsequent chapters of the handbook, as well as the broader neuroscience literature. The subsequent sections are comprised of groups of chapters organized around major psychological themes. In Volume 1, these include: Sensation and Perception, Attention and Cognition, and Learning and Memory. This organization carries over to Volume 2, with major sections being: Motivation and Emotion, Social Processes, Psychological Disorders and Health and Aging.

The notion that 100 billion neurons give rise to human behavior proved daunting up through the twentieth century because neuroscientists were limited by existing technologies to studying the properties of single neurons or small groups of neurons. Characterizing simple neural circuits has led to an understanding of a variety of sensory processes, such as the initial stages in vision, and relatively simple motor processes, such as the generation of locomotion patterns. However, unraveling the neural substrates of more complex behaviors, such as the ability of an animal to navigate in its environment, to pay attention to relevant events in its surroundings, to perceive and communicate mental states including the beliefs and desires of others and to form and maintain interpersonal and group relationships remains one of the major challenges for the neurosciences in the twenty-first century. In contrast to more elementary behaviors, these complex behavioral processes depend on interactions within elaborate networks extending across distinct brain structures. Elucidating the neural bases of complex behaviors, therefore, may require sophisticated approaches and methods that have only recently, or have yet to be, developed. These include the ability to record electrical brain activity with multi-electrode arrays in freely behaving animals or humans, neuroimaging methods that can noninvasively monitor brain activity, and an increasing cornucopia of technologies from molecular biology and genetics that allow investigators to analyze the cellular bases of behaviors. These approaches are not only revealing the underlying neurobiology of behavior, but are establishing the foundation for an understanding for the biological bases of a variety of physical and mental health problems. As a result of these developments, the neurosciences are reshaping the landscape of the behavioral sciences, and the behavioral sciences are of increasing importance to the neurosciences, especially for the rapidly expanding investigations into the highest level functions of the brain. The Handbook of Neurosciences for the Behavioral Sciences provides an introduction to graduate students and scholars xi

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xii

Preface

Throughout the Handbook, the goal has been to integrate information across disciplines and levels of organization/ analyses, from the cellular/molecular to systems to behavioral/social levels. The organization of the Handbook thus avoids artificial dichotomies such as lower level vs. higher level processes, or neuroscience vs. behavioral science sections. Coverage of motor systems, for example, is included in the section on Attention and Cognition and discussion of somatovisceral function is included in the context of Motivation and Emotion, providing the reader with the broadest and most interdisciplinary perspectives on a given topic. Cross referencing across chapters has been emphasized, to underscore the fact that a neuroscientific perspective may illuminate connections across areas of study in psychology, where none may have traditionally been recognized.

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The intended audience for the Handbook is broad, including graduate students, psychologists and other behavioral scientists who seek knowledge and understanding about neuroscience. It is a resource for active behavioral neuroscientists as well as those with minimal background in the biological sciences. It should also be of interest to neuroscientists who want an introduction to contemporary psychological issues, presented in a neuroscientific context. None of this would be possible without the tremendous efforts and high quality of the chapter authors. We thank them all for their contributions and we hope you will find this Handbook of value in understanding the behavioral neurosciences. GARY G. BERNTSON JOHN T. CACIOPPO

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Chapter 1

Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference JOHN T. CACIOPPO AND GARY G. BERNTSON

Charles Darwin (1873) concluded his seminal treatise on the expression of emotions by noting the adaptive value of the structures and behaviors that are observed in humans and the utility of comparative studies to discern the origin of these structures and behaviors. Darwin’s emphasis on the biological and functional underpinnings of behavior influenced William James (1890), who opined as follows:

is associated with the behavioral element, but it does not address whether the behavioral element is caused by the neural element (Cacioppo & Tassinary, 1990). The second approach is illustrated by the case of a patient who had suffered a neurosyphilitic lesion in the front part of his brain and was being attended by the physician Paul Pierre Broca. The patient was known as “ Tan” because this was the only word he was left able to speak, but in other regards his mental processes and behavior appeared relatively normal. In a postmortem autopsy, Broca determined that Tan’s lesion was in the posterior third of the inferior frontal gyrus. This region became known as Broca’s area and was surmised to be the speech center of the brain based on the changes in behavior associated with damage to this region. This case illustrates the methodological approach of comparing differences in or manipulating neural elements to investigate their effects on cognition, emotion, or behavior—that is, the study of psychological or behavioral processes () as a function of neural processes (). In Bayesian terms, this can be specified as P(/). Stating it in this way highlights a limitation of this approach: It provides evidence that a neural element is sufficient to influence a behavioral element, but it does not address whether the neural element is necessary. Thus, these two approaches provide unique nonredundant information (Sarter, Bernston, & Cacioppo, 1996). For much of the twentieth century, the notion that 100 billion neurons gave rise to the human mind and behavior proved daunting, especially when one tried to say anything specific about this feat. To make this problem tractable, neuroscientists initially studied simple circuits and behaviors. The notion, illustrated for instance by Sherrington’s (1906) work on the integrative action of the nervous system based on his studies of spinal cord reflexes, was that fundamental principles governing the operation of neural circuits and how such mechanisms relate to behavior could be understood as well in simpler systems,

A science of the mind must reduce such complex manifestations {of behavior} to their elements. A science of the brain must point out the functions of its elements. A science of the relations of the mind and brain must show how the elementary ingredients of the former correspond to the elementary functions of the latter. (p. 28)

Several celebrated clinical cases of the nineteenth century illustrate, at least at a gross level, two distinct approaches to investigation of these elements and relationships. An Italian named Bertino suffered a head injury that left his frontal lobes partially exposed (Raichle, 2000). Angelo Mosso (1881), an Italian physiologist, observed a sudden increase in the magnitude of pulsations over the frontal lobes with the ringing of local church bells and the chiming of a clock signaling the time for required prayer. Based on these observations, Mosso posited that changes in blood flow were associated with changes in cognition. To test this hypothesis, Mosso asked Bertino to multiply 8 by 12, a task that was accompanied by an increase in brain pulsation. These observations set the stage for contemporary functional brain mapping using hemodynamic measurements (Raichle, 2000), and illustrate the general approach of manipulating cognition, emotion, or behavior to determine their effects on neural functions—() as a function of psychological or behavioral processes (). In Bayesian terms, this can be specified as P(/). Stating it in this way highlights a limitation of this approach: It provides evidence that a neural element or mechanism 3

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

assuming that the difference between simple and complex systems could be extrapolated from an established set of theories. The organization and function of simpler circuits can inform and be informed by the study of the behavior of the more complex circuits and organisms of which they are a part, but complex circuits cannot be explained on the basis of simpler ones alone. For instance, the phenotypic expression (e.g., behavior) of strains of mice with specific genes inactivated (i.e., knockout mice) has been known to depend on the genetic background (e.g., Gerlai, 1996); the effects of the social context, in contrast, were thought to be unimportant. Crabbe, Wahlsten, and Dudek (1999) demonstrated that the specific behavioral effects associated with a given knockout could vary dramatically across environmental contexts (e.g., experimenters, testing environments, laboratories). When these authors expanded the traditional approach to include multilevel integrative analyses of genetics, neural processes, and behavior, they observed new patterns of data that were not predictable based on what was known about the component elements. Stephen Jay Gould (1985) noted, “We often think, naively, that missing data are the primary impediments to intellectual progress—just find the right facts and all problems will dissipate. But barriers are often deeper and more abstract in thought” (p. 151). This observation is especially true in the investigation of complex systems, where patterns of data rather than single data points are important to grasp. Human behavior (including mental behavior) is the most complex system science has investigated. In this endeavor, as in all complex sciences, theory and empirical investigation must proceed together, each informing the other. As William James (1890) suggested, the need for theoretical models applies to psychological and behavioral elements (), biological and neural elements (), and the relations between the former and the latter. Whether manipulating or measuring neural mechanisms, or both, mapping these changes to behavior requires that tasks be well designed, which is to say the functional component processes must be well specified by some behavioral theory and body of empirical work. Theory and research in the neurosciences, however, are just as crucial to discerning patterns of integrative relations. Being inattentive to the properties, constraints, and operating possibilities of the neural mechanisms that underlie cognition, emotion, and behavior is inefficient, at best, because one misses the opportunity to build more plausible theories and to focus efforts more precisely on the constructs and interpretations that need to be considered. The resulting theories within and bridging across the behavioral sciences and the neurosciences are themselves only evolving approximations, but they provide an important means for advancing

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understanding of the complex neural and behavioral mechanisms at work. The evolution of these theories is promoted by the open theoretical dialogue between the neurosciences and behavioral sciences. This Handbook is designed to make theory and research in the neurosciences accessible to psychologists and other behavioral scientists while also highlighting for neuroscientists currently active areas within the behavioral sciences and discussing information, constructs, and approaches at the intersection of the behavioral sciences and the neurosciences. Complex states tend to be multiply determined, and human behavior is certainly no exception. Nineteenthcentury neurologist John Hughlings Jackson emphasized the hierarchical structure of the central nervous system and the re-representation of functions at multiple levels of this neuraxis (Jackson, 1884/1958). Subsequent neuroanatomical investigations have revealed this structure to be more heterarchical than hierarchical (see Berntson et al., this volume), but Jackson’s point remains that information is processed concurrently at multiple levels of the organization within the nervous system. Primitive protective responses to aversive stimuli, for instance, exist at the level of the spinal cord, as in stereotypic flexor withdrawal responses to nociceptive stimulation. These protective reactions are expanded and embellished at higher levels of the nervous system (Berntson, Boysen, & Cacioppo, 1993). The evolutionary development of higher neural systems, such as the limbic system, endowed organisms with an expanded behavioral repertoire including escape reactions, aggressive responses, and even the ability to anticipate and avoid aversive encounters. Humans were not the first bipedal creatures or the first to use tools, but humans, apparently uniquely, contemplate the history of the earth, the reach of the universe, the origin of the species, the genetic blueprint of life, and the physical basis of their own unique mental existence. These feats are the result of evolution endowing humans not only with primitive, lower level adaptive reactions but also with the unmatched information-processing capacities of the cerebrum. At progressively higher levels of neural organization, there is a general expansion in the range and relational complexity of representational operations and contextual controls and in the breadth and flexibility of discriminative and adaptive responses (Berntson et al., 1993). The adaptive flexibility of higher level neural systems comes at a cost, however, given the finite information-processing capacity of neural circuits. Greater flexibility means a less rigid relationship between inputs and outputs; a greater range of information that must be integrated; and a slower, more serial-like mode of processing. Consequently, the evolutionary layering of higher processing levels onto lower substrates has adaptive advantage in that lower

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Mapping across Levels of Organization 5

and more efficient processing levels may continue to be expressed. The processes expressed at lower and higher levels of the neuraxis can also interact in myriad ways, as illustrated by the diabetic who can overcome the withdrawal reflex when self-injecting insulin and the professional golfer who chokes when putting on the final green with a major championship at stake. Statistical models now exist that include stochastic error terms at various hierarchical levels of aggregation that are applicable to the data matrices that span biological and behavioral levels of organization. Our goal here is not to review these statistical models (cf. Weinstein, Vaupel, & Wachter, 2008) but to provide a more generic discussion of the conceptual issues that arise when mapping between the neural and behavioral domains. The mappings of elements across levels of organization may begin as associations but must move to mechanisms to explicate the neural basis of psychological and behavioral processes. This progression can be hindered by inattention to the logic underlying inferences about the functional import of neural structures or processes simply because one is dealing with observable biological events, as doing so can yield simple and restricted descriptions of empirical relationships or erroneous interpretations of these relationships. In the remainder of this chapter, therefore, we review logical issues involved in mapping constructs across levels of organization.

MAPPING ACROSS LEVELS OF ORGANIZATION One of the simplest methods of mapping across neural () and behavioral () levels of organization is the correlative approach. There are notable success stories to illustrate this approach. For instance, Suomi and colleagues found that peer-raised monkeys, compared to monkeys raised by their biological mothers, exhibit more aggression and less grooming behaviors, and typically remain at the bottom of the social hierarchy (Suomi, 1999). These monkeys are further characterized by lower cerebrospinal fluid concentrations of 5-hydroxyindoleacetic acid (5-HIAA, a serotonin metabolite) than their mother-reared counterparts. Impulsive rhesus monkeys in the wild are also characterized by high cerebrospinal fluid 5-HIAA concentration levels and the behavioral tripartite of aggressiveness, infrequent grooming, and low standing in the social hierarchy (cf. Suomi, 1999). These correlational studies have contributed to productive research on serotonin transporter gene (5-HTT) polymorphisms, environmental influences, and behavior in monkeys and humans. Of course, not every association identified in a correlative approach proves robust or informative. The associations

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uncovered in correlative research, especially atheoretical correlative investigations, run the risk of yielding false discoveries (i.e., nonreplicable associations), and this risk increases with the number of possible associations that are examined. False discovery rate techniques have been developed to help mitigate this problem, but these techniques do not eliminate the problem (cf. Munafo et al., 2003). The development and adoption of false discovery rate methods represents an advance in dealing with Type I error rates, however, because the cost of near-zero false discovery rates is a high false-negative rate. The ratio of the number of missed small discoveries to false discoveries can be substantially greater than 1 in studies of complex behavioral outcomes, and small associations can carry large theoretical ramifications. Therefore, the cost of missing important but small associations (Type II errors) can sometimes be greater than the cost of a Type I error. Independent replication, therefore, is of paramount importance. In addition, bioinformatics tools and multivariate techniques permit a reduction in the number of measures to more meaningful functional sets of measures. For instance, microarray studies can now be performed on hundreds of thousands of gene transcripts, but the upstream transcription control pathways are typically of greater interest than the individual gene expressions in these studies. Cole, Yan, Galic, Arevalo, and Zack (2005) introduced the Transcription Element Listening System (TELiS), which combines sequence-based analysis of gene regulatory regions with statistical prevalence analyses to identify transcription factor binding motifs that are overrepresented among the promoters of up- or downregulated genes. Cortisol can regulate a wide variety of physiological processes via nuclear hormone receptor–mediated control of gene transcription. Cortisol activation of the glucocorticoid receptor exerts broad anti-inflammatory effects by inhibiting pro-inflammatory signaling pathways. In longitudinal research on middle-aged and older adults, we found that perceptions of social isolation predict higher morning rises in cortisol the following day (Adam, Hawkley, Kudielko, & Cacioppo, 2006). Social isolation is also associated with increased risk of inflammation-mediated diseases. One possible explanation for inflammation-related disease in individuals with high cortisol levels involves impaired glucocorticoid receptor–mediated signal transduction that prevents the cellular genome from effectively “hearing” the anti-inflammatory signal sent by circulating glucocorticoids (Cole et al., 2007). Consistent with this hypothesis, a systematic examination of genome-wide transcriptional alterations in circulating leukocytes using TELiS showed increased expression of genes carrying pro-inflammatory elements and decreased expression of genes carrying anti-inflammatory glucocorticoid response

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

elements in lonely relative to nonlonely middle-aged adults (Cole et al., 2007). Impaired transcription of glucocorticoid response genes and increased activity of pro-inflammatory transcription control pathways provide a functional genomic explanation for elevated risk of inflammatory disease in individuals who chronically perceive high levels of social isolation. In sum, a strength of the correlative approach is often in identifying associations that might be replicable and worthy of further study rather than in post hoc hypothesis testing. An important goal of scientific theory is to describe the causal interrelationships among factors, thereby explicating the mechanism responsible for an association. The correlative approach may generate elements (e.g., genes, neurophysiological circuits, cognitive processes) or contextual moderators that are candidates for this causal mechanism. The correlative approach may not indicate the nature of the specificity of the association across levels of organization. For convenience, consider the constructs or measures at each level of organization as elements within a domain or set, as William James (1890) suggested. The mapping between elements across such sets can take one of the following forms (see Figure 1.1): • A one-to-one relation, such that an element in one set or level of organization is associated with one and only one element in another set, and vice versa. An example of a one-to-one relation is neurons in V1 tuned to a given stimulus feature such as line orientation (see Hubel & Wiesel, 2005). • A one-to-many relation, meaning that an element of interest in one set is associated with multiple elements in another set. An example is visual perception, which proceeds along ventral and dorsal streams (see Chapter 11). • A many-to-one relation, meaning that two or more elements in one set are associated with one element in another set. (This differs from a one-to-many relation only when the order of the mapping across levels of organization—e.g., behavioral [e.g., cognitive] to biological—is specified.) An example is the finding that a particular movement or the observation of that particular movement activates the same neurons (termed mirror neurons) in area F5 of the monkey brain (see Chapter 16). • A many-to-many relation, meaning two or more elements in one set are associated with the same (or an overlapping) subset of elements in another set. Faces and objects may differ in terms of the region of maximal response in functional magnetic resonance imaging studies, but both faces and objects are associated with activation of multiple regions in the ventral temporal lobe (e.g., fusiform gyrus, parahippocampal gyrus; Haxby et al., 2001).

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• A null relation, meaning there is no association between the specified element in a neural set and those observed in a behavioral set. Even though all elements in  map into one or more elements in , not all elements in  map into elements in , and a particular element of interest in  may not map into the set of elements known or measured in . Association studies involving elements with a one-toone relation (absent confoundings and measurement error) produce high correlations, whereas association studies involving elements characterized by a null relation yield an essentially zero correlation. The strength of the association between elements across levels of organization can vary a great deal, however, for one-to-one, one-to-many, and many-to-one mappings, and a many-to-many mapping between two elements across levels of organization can produce correlation coefficients that are quite small, making them difficult to distinguish from a null relation unless the sample size is large or one or more elements are manipulated. Thus, the initial establishment of an association between elements across levels of organization through a correlative approach is typically not sufficient to determine the specificity of the mapping.

Psychological

Biological

One-to-One

One-to-Many

Many-to-One

Many-to-Many

Null

Figure 1.1 Possible relationships between elements in two adjacent levels of organization (domains). For illustrative purposes, these domains have been labeled Psychological and Biological.

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Toward Stronger Inferences in the Interpretation of Brain–Behavior Relationships 7

Why might it be important to go beyond thinking of associations to considering the nature of the relationship between elements at different levels of organization? First, it is important if one is to move efficiently from association to the specification of mechanisms. Second, the nature of the mappings between elements at different levels of organization determines the limits of interpretation one can draw about an association. Consider research in which a biological measure (e.g., activation of the anterior cingulate cortex as measured by fMRI) is shown to covary with a behavioral task (e.g., lying; Langleben et al., 2002). This established association may then be used to justify an interpretation of differences in the neural element (i.e., activation of the anterior cingulate) as evidence of differences in the behavioral element (i.e., lying). This form of inference can be problematic, however. Even if one knew that variations in lying were associated with corresponding variations in anterior cingulate activity, inferring lying based on anterior cingulate activity ignores the possibility that other antecedent conditions could also produce variations in anterior cingulate activity. That is, it ignores the specificity of the association or mapping to the construct about which one would like to draw the inference. Such errors, in turn, can slow theoretical development. It is tempting to suggest that these issues do not apply to genetics (or brain processes) because there is no doubt that they play a causal role in the production of complex behaviors. To say that genes are causal is not equivalent, however, to specifying which gene or set of genes is associated with and causal in a particular phenotypic expression or, for that matter, to specifying the mechanism by which associated genes might influence a particular phenotype. Gottesman and Gould (2003) suggested that the number of genes involved in a phenotype is directly related to both the complexity of the phenotype and the difficulty of genetic analysis (see also Butcher, Kennedy, & Plomin, 2006). Although difficult to discern, such causal linkages will be more easily resolved if attention is paid to the implications of the many-to-many mapping problem. The mapping between elements across levels of organization may become more complex (e.g., many-to-many) as the number of intervening levels of organization increases. The exception to this statement is when mappings among elements across adjacent levels of organization is one-to-one, but such mappings are atypical. Accordingly, the likelihood of complex and potentially obscure mappings increases as one fails to consider intervening levels of organization. Admittedly, it is not always obvious which of several levels of organization might be “adjacent,” except perhaps when level of organization refers to a temporal rather than

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spatial scope. This caveat that mapping across levels of organization may be fostered by the incremental mapping of elements between proximal levels nevertheless may have heuristic value. For instance, endophenotypes such as neurocognitive deficits have proven to be valuable explanatory constructs between genes and psychiatric diseases (e.g., Gottesman & Gould, 2003; Nuechterlein, Robbins, & Einat, 2005), and in theory the same situation should apply to any mapping that goes from neural elements to complex behaviors. For this reason, we focus here on the mappings between two adjacent levels of organization. The issues raised about the mappings between adjacent levels of organization can be extended to any number of adjacent levels of organization.

TOWARD STRONGER INFERENCES IN THE INTERPRETATION OF BRAIN–BEHAVIOR RELATIONSHIPS There is an intuitive appeal to the view that a proper understanding of the neural substrates of cognition and behavior may be couched in terms of the selective activation of regions of the brain during particular behavioral tasks. Although progress has been made in this regard, the manner in which many inferences are drawn about the behavioral significance of localized brain activity is more complex than is sometimes assumed. A major goal of neuroscientific studies of behavior can be expressed as   f(). That is, ultimately one wishes to specify the biological mechanisms responsible for various behavioral (including mental) phenomena. Contrasting tasks that are thought to differ in only one or more cognitive or behavioral operations () between the associated neural events () are sometimes interpreted as showing that neural structure (or process)  is associated with behavioral operation . These data are also sometimes treated as revealing much the same information that would have been obtained had neural structure (or process)  been stimulated or ablated and a consequent change in behavioral function  observed. This form of interpretation reflects the explicit assumption that there is a fundamental localizability of specific behavioral operations and the implicit assumption that there is an isomorphism between  and . Such observations may represent a starting point for a series of internally consistent propositions that lead to a general conclusion, but problems with the logic of this approach can lead this conclusion astray (Cacioppo & Tassinary, 1990). In particular, research has shown that when a behavioral element varies when a neural element is manipulated (or vice versa), this does not necessarily imply the existence of an isomorphism between these elements (Sarter, et al., 1996).

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Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

For instance, causal hypotheses regarding a specific neural structure or process () underlying a cognitive or behavioral operation () are of the form   f(). They necessarily imply that  is always followed by  but do not necessarily imply that  is always preceded by . This is what it means for  to have multiple (parallel) determinants. Furthermore, brain events () may be of interest to the extent that they index a cognitive operation or state so that the inferences are of the form   f() rather than not   (not ). Thus, one important aim of neural measurements can be specified by the conditional probability of  given , or P(/)  1. The typical structure of investigations in which neural measurements are made, in contrast, can be written as the conditional probability of  given , or P(/)  x. For example, brain imaging techniques provide information about  as a function of , but the conditional probabilities P(/) and P(/) are not equivalent unless there is a 1:1 relationship between brain structure  and cognitive function . Most of the early discoveries of functional locationism that have had lasting impact involved the ablation or direct electrical stimulation of specific nuclei (i.e., P[/]), whereas the evidence for Gall’s localization theory was based primarily on relating cranial features to extreme behaviors (i.e., P[/]), which was interpreted to mean that individuals with these cranial features were destined toward these behaviors (i.e., P[/]). This latter interpretation does not follow from the form of the data on which it was based because P(/) does not equal, or even approximate, P(/) unless there is an isomorphism between  and . Careful attention to the structure of inference, therefore, may contribute to a more productive dialogue between neuroscientists and behavioral scientists. Approaches such as stimulation and ablation studies and brain imaging research provide complementary rather than redundant information about the relationship between brain structures (or events) and cognitive functions. This is because stimulation and ablation studies bear on the relationship P(/), whereas brain imaging studies provide information about P(/). Despite the formal parallelism between these expressions, there is a fundamental asymmetry in the heuristic power of studies aimed at the demonstration of P(/) versus P(/). The causal role of  in process  can be examined in a straightforward fashion by direct experimental manipulation of . The loss of cognitive function by inactivation of neural processes can serve to establish  as necessary for function . Moreover, addressing a more complex avenue of research, facilitation of cognitive functions by electrical or neurochemical brain activation can further establish that  is a sufficient condition for function .

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THE IMPORTANCE OF SPECIFYING CONTEXT: TAXONOMY OF MAPPINGS Tests, assays, or measured biological responses (i.e., a physiological event that exceeds some decision threshold) have two different but related sets of characteristics. Analytic sensitivity is the ability to consistently detect very low levels of the target analyte. Stated another way, sensitivity is the true-positive rate. Considering all true (T) and false (F) outcomes of the test, for both positive (P; analyte present) and negative (N; analyte absent) conditions, sensitivity  TP/(TP  FN). In contrast, analytic specificity refers to the ability of a test to selectively detect only the target analyte and not others; specificity  TN/(TN  FP). For instance, blood sugar levels will vary in a predictable fashion for several hours after one ingests a dosage of glucose. Deviations from the normative values in blood sugar level across time mark a possible problem in metabolism because the blood glucose tolerance test (a procedure for mapping the glucose–blood sugar association) is sensitive and specific as long as the appropriate testing procedures are followed (e.g., fasting prior to the test) to eliminate the other known influences on the observed blood sugar excursions over the course of the test. This illustrates how a mapping between elements in  and  can be simplified by paying attention to potential confounding and contextual factors, by which we mean specifically P(not /) and P(not /). Furthermore, the diagnostic value of any given  (i.e., a measured response, as defined by some decision criteria such as corrected p  .05) as a measure of  depends not only on the sensitivity and specificity of the measured response but on the base rates for true positives and for true negatives. The sensitivity, defined quantitatively above, represents the true detection probability, and the specificity represents 1 minus the false detection probability. The chance that the measured response correctly indexes the targeted state is called the positive predictive value (PPV) and equals the fraction of detections that are true hits, that is, PPV  TP/(TP  FP). In contrast, the chance that the absence of this measured response (termed a negative screen in medicine) is correct is called the negative predictive value (NPV), where NPV  TN/(TN  FN). The properties of sensitivity and specificity and the PPV and NPV, of course, depend in part on the elements involved in the mapping. For instance, the adrenocortical hormone cortisol is released by the adrenal cortex under conditions of stress and hence is considered a stress hormone and is often used as a marker of stress. Assays with high sensitivity and specificity for cortisol are available and can be used to measure this hormone in plasma, urine, or saliva; these assays may provide an accurate measure

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The Importance of Specifying Context: Taxonomy of Mappings

The scientist is usually looking for invariance whether he knows it or not. Whenever he discovers a functional relation between two variables his next question follows naturally: under what conditions does it hold? In other words, under what transformation is the relation invariant? The quest for invariant relations is essentially the aspiration toward generality, and in psychology, as in physics, the principles that have wide application are those we prize. (p. 20)

Is the mapping between two elements across levels of organization universally generalizable, or is it moderated by other factors? If it is generalizable without qualification, then the association requires no attention to characteristics of the context or sample population; that is, the mapping has external validity. If external validity is absent, then the reason for this becomes a theoretically interesting question regarding P(not-/) or P(not-/). Invariant associations were once assumed, but statistical methods are now sufficiently developed to test for potential moderators (e.g., Baron & Kenny, 1986), and increasing attention is being paid to the operation of moderator variables. A taxonomy of associations between elements across levels of organization is summarized in Figure 1.2. The initial step is often to establish that variations in an element in

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Context-Bound

Context-Free

Maker

Invariant

Outcome

Concomitant

Specificity

One-to-One

Generality

Many-to-One Many-to-Many

of adrenocortical activity. This represents a proximal mapping, as the adrenal cortex is the primary source of cortisol, although other factors (e.g., clearance) can also affect the measure. When cortisol is used as a marker of stress, however, the sensitivity, specificity, PPV, and NPV may all be quite different. In this case the mapping is more distal, as there are several mediating links between stress and cortisol secretion. Although potent stressors generally yield a cortisol response, the sensitivity of a cortisol assay for stress is considerably lower, as minor stresses may not trigger a measurable cortisol response. That is, there may be many more false negatives in the equation sensitivity  TP/(TP  FN). Moreover, the selectivity of a cortisol assay for stress is also considerably lower, as other variables may also impact cortisol release. Cortisol levels vary across the day and with activity, among other variables, so there may be more false positives in the equation specificity  TN/(TN  FP). The PPV and NPV depend on the threshold used to define a response, but it should be obvious that these values, too, will be lowered by poor sensitivity and specificity. Consequently, the utility of a cortisol assay for stress may be limited to more significant stressors, and to enhance specificity, extraneous variables that can impact cortisol must be taken into account statistically. Another dimension is the generality of the mapping. In his influential Handbook of Experimental Psychology, S. S. Stevens (1951) advised the following:

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Figure 1.2 Taxonomy of mappings among elements between adjacent levels of organization.

one domain are associated with variations in an element in another, thereby establishing an association. An outcome is defined as a mapping in which multiple elements at one level of organization (e.g., biological) are related to an element at another level of organization (e.g., behavioral), and this many-to-one mapping may change across contexts. Initial association studies typically do not address issues of specificity or generality, and the treatment of such associations as invariants is premature. An invariant relationship refers to a universal isomorphic (one-to-one) mapping between elements across levels or organization (see Figure 1.2). Invariant mappings permit the inference of an element at one level of organization based on the measurement of its isomorphic element at another. A marker is defined as a one-to-one, nonuniversal (e.g., context-dependent) relationship between elements across levels of organization (see Figure 1.2). Many medical diagnostic tests that have sensitivity and specificity only if explicit procedures are followed to eliminate other influences are examples of markers. Inferences based on markers are similar to those for invariants as long as all other elements involved in the mapping are either experimentally or statistically controlled. Finally, a concomitant refers to a many-to-one but universal association between elements across levels of organization and is similar to outcomes, except that the latter is not universal. Outcome and concomitant mappings enable strong inferences to be drawn about theoretical constructs based only on hypothetico-deductive logic (Platt, 1964). Specifically, when two theoretical models differ in predictions regarding one or more outcomes or concomitants, then the logic of the experimental design allows theoretical

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10

Integrative Neuroscience for the Behavioral Sciences: Implications for Inductive Inference

inferences to be drawn about elements at one level of organization based on the measured elements in another. When a new effect or association is found not to generalize to specific contexts or individuals, concerns are typically expressed about the methodological differences between the studies. Such a finding raises several important questions, including whether the original association is replicable; and, if replicable, whether the diminution in effect size attributable to measurement issues (e.g., reliability, construct validity) or to the operation of a moderator variable. The latter is an important theoretical question. It is for this reason that careful attention to the psychometric properties of all measures, regardless of their level of organization, to ensure their reliability and validity (including construct validity) is especially important in the design and analysis of neuroscientific studies of behavioral processes.

SUMMARY Given the complexity of neural and behavioral processes, attention to the form of scientific reasoning can be especially important. Many behavioral processes are multiply determined. To the extent that this is the case, investigators who assume rather than establish an invariant relationship between elements in the behavioral and neural domains are at risk for predictably faulty interpretations. That is, the sensitivity and specificity of the mapping of neural elements into behavioral levels of organization may be context dependent, and paying attention to these issues improves the quality of inductive inferences. Interdisciplinary research that crosses neural and behavioral levels of organization raises issues about how might one productively think about concepts, hypotheses, theories, theoretical conflicts, and theoretical tests across levels of organization. Abstract constructs such as those developed by behavioral scientists provide a means of understanding highly complex activity without needing to specify each individual action of the simplest components, thereby providing an efficient means of describing the behavior of a complex system (e.g., working memory). Chemists who work with the periodic table on a daily basis nevertheless use recipes rather than the periodic table to cook, not because food preparation cannot be reduced to chemical expressions but because it is not cognitively efficient to do so. Reductionism, in fact, is one of several approaches for bettering science based on the value of data derived from distinct levels of organization to constrain and inspire the interpretation of data derived from other levels of organization. In reductionism, the whole is as important to study as are the parts, for only in examining the interplay across levels of organization can the underlying principles and mechanisms be ascertained.

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Our goal in this chapter has been to outline a simple model to aid in thinking about elements from different levels of organization and the inferences drawn from observations of these elements. Contemporary work has demonstrated that theory and methods in the neurosciences can constrain and inspire behavioral hypotheses, foster experimental tests of otherwise indistinguishable theoretical explanations, and increase the comprehensiveness and relevance of behavioral theories. Several principles further suggest that comprehensive theories of behavior will be advanced by the joint consideration of multilevel integrative analyses that span neural and behavioral processes. One that we have discussed is the principle of multiple determinism, which specifies that a target event at one level of organization, but especially at molar or abstract (e.g., cognitive or behavioral) levels of organization, can have multiple antecedents within or across levels of organization. A corollary to this principle, the corollary of proximity, is that the mapping between elements across levels of organization becomes more complex (e.g., many-to-many) as the number of intervening levels of organization increases. An important implication of this corollary is that the likelihood of complex and potentially obscure mappings increases as one skips levels of organization. The principle of nonadditive determinism specifies that properties of the whole are not always readily predictable from the properties of the parts. For instance, the behavior of nonhuman primates following the administration of amphetamine or placebo can appear similar unless each primate’s position in the social hierarchy is considered. When this behavioral factor is taken into account, amphetamine is found to increase dominant behavior in primates high in the social hierarchy and to increase submissive behavior in primates low in the social hierarchy. Thus, the effects of physiological changes on behavior can appear unreliable until the analysis is extended across levels of organization. A strictly physiological (or behavioral) analysis, regardless of the sophistication of the measurement technology, may not reveal the orderly relationship that exists. The emergence of an orderly pattern of data when spanning levels of organization is one of the unique opportunities of neuroscientific investigations of behavior. Finally, the principle of reciprocal determinism specifies that there can be mutual influences between microscopic (e.g., biological) and macroscopic (e.g., social) factors in determining behavior. For example, not only has the level of testosterone in nonhuman male primates been shown to promote sexual behavior, but the availability of receptive females influences the level of testosterone in nonhuman primates. These principles illustrate that the mechanisms underlying mind and behavior may not be fully explained

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References 11

by a biological or a behavioral approach alone but rather may require a multilevel integrative analysis. The contributions to this volume are designed with this in mind.

Haxby, J. V., Gobbini, M. I., Furey, M. L., Ishai, A., Schouten, J. L., & Pietrini, P. (2001, September 28). Distributed and overlapping representations of face and objects in ventral temporal cortex. Science, 293, 2425–2430. Hubel, D. H., & Wiesel, T. N. (2005). Brain and visual perception: The story of a 25-year collaboration. Oxford, England: Oxford University Press.

REFERENCES Adam, E. K., Hawkley, L. C., Kudielka, B. M., & Cacioppo, J. T. (2006). Day-to-day dynamics of experience-cortisol associations in a population-based sample of older adults. Proceedings of the National Academy of Sciences, 103, 17058–17063. Baron, R. M., & Kenny, D. A. (1986). The moderator-mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51, 1173–1182. Berntson, G. G., Boysen, S. T., & Cacioppo, J. T. (1993). Neurobehavioral organization and the cardinal principle of evaluative bivalence. Annals of the New York Academy of Sciences, 702, 75–102. Butcher, L. M., Kennedy, J. K. J., & Plomin, R. (2006). Generalist genes and cognitive neuroscience. Current Opinion in Neurobiology, 16, 1–7. Cacioppo, J. T., & Tassinary, L. G. (1990). Inferring psychological significance from physiological signals. American Psychologist, 45, 16–28. Cole, S. W., Hawkley, L. C., Arevalo, J. M., Sung, C. Y., Rose, R. M., & Cacioppo, J. T. (2007). Social regulation of gene expression in human leukocytes. Genome Biology, 8(9), R189.

James, W. (1890). The principles of psychology (Vol. 1). New York: Holt. Langleben, D. D., Schroeder, L., Maldjian, J. A., Gur, R. C., McDonald, S., Ragland, J. D., et al. (2002). Brain activity during simulated deception: An event-related functional magnetic resonance study. Neuroimage, 15, 727–732. Mosso, A. (1881). Ueber den Kreislauf des Blutes im menschlichen Gehirn [About the circulation of the blood in the human brain]. Leipzig, Germany: Veit. Munafo, M. R., Clark, T. G., Moore, L. R., Payne, E., Walton, R., & Fint, J. (2003). Genetic polymorphisms and personality in healthy adults: A systematic review and meta-analysis. Molecular Psychiatry, 8, 471–484. Nuechterlein, K. H., Robbins, T. W., & Einat, H. (2005). Distinguishing separable domains of cognition in human and animal studies: What separations are optimal for targeting interventions? Schizophrenia Bulletin, 31, 870–874. Platt, J. R. (1964, October 16). Strong inference. Science, 146, 347–353.

Cole, S. W., Yan, W., Galic, Z., Arevalo, J., & Zack, J. A. (2005). Expressionbased monitoring of transcription factor activity: The TELiS database. Bioinformatics, 21, 803–810.

Raichle, M. E. (2000). A brief history of human functional brain mapping. In A. W. Toga & J. C. Mazziotta (Eds.), Brain mapping: The systems (pp. 33–77). San Diego, CA: Academic Press.

Crabbe, J. C., Wahlsten, D., & Dudek, B. C. (1999, June 4). Genetics of mouse behavior: Interactions with laboratory environment. Science, 284, 1670–1672.

Sarter, M., Berntson, G. G., & Cacioppo, J. T. (1996). Brain imaging and cognitive neuroscience: Towards strong inference in attributing function to structure. American Psychologist, 51, 13–21.

Darwin, C. (1873). The expression of the emotions in man and animals. New York: Appleton.

Sherrington, C. S. (1906). The integrative action of the nervous system. New York: Scribner ’s.

Gerlai, R. (1996). Gene-targeting studies of mammalian behavior: Is it the mutation or the background genotype? Trends in Neurosciences, 19, 177–181.

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Gottesman, I. I., & Gould, T. D. (2003). The endophenotype concept in psychiatry: Etymology and strategic intentions. American Journal of Psychiatry, 160, 636–645. Gould, S. J. (1985). The flamingo’s smile: Reflections in natural history. New York: Norton.

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Jackson, J. H. (1958). Evolution and dissolution of the nervous system (Croonian Lectures). In J. Taylor (Ed.), Selected writings of John Hughlings Jackson. New York: Basic Books. (Original work published 1884.) Vol 2, pp. 3–92.

Suomi, S. (1999). Attachment in rhesus monkey. In J. Cassidy & P. Shaver (Eds.), Handbook of attachment: Theory, research, and clinical applications (pp. 181–197). New York: Guilford Press. Weinstein, M., Vaupel, J. W., & Wachter, K. W. (2008). Biosocial surveys. Washington, DC: The National Academies Press.

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Chapter 2

Developmental Neuroscience MYRON A. HOFER

revealed how changes in the expression patterns of genes provide a common basis for both developmental and evolutionary changes (S. Carroll, 2005). Developmental biology (as embryology) had long been isolated from evolutionary biology, and most scientists thought that differences between species were accounted for by differences between those species’ genes. But with the advent of rapid gene sequencing in the past few years, it is now appreciated that differences between species lie in how a common set of highly conserved genes is regulated during development to produce the extraordinary differences in animal body plans and behaviors ranging from those of flies and worms to humans. Evolution is now viewed as taking place through changes in the processes and course of development, and development, in turn, is now seen as a major source of novelty and of variation between individuals upon which selection can act in the course of evolution. Rapid progress in the molecular genetic mechanisms of early development in the past few years has revealed an unexpected plasticity in the regulation of gene expression that enables a relatively few conserved cellular processes to be linked together in a variety of potentially adaptive patterns in response to genetic mutation or to environmental change. This “facilitated” variation (Kirschner & Gerhart, 2005) is capable of generating the useful novelties that random mutation and selection by themselves have seemed far less capable of producing in the course of evolution. New findings have also shown that the plasticity of behavior development in response to environmental interactions and genetic change (mutation, recombination) is capable of generating novel forms of adaptive variation upon which selection can act. By exposing previously hidden (genomically silenced) genes to selection and by genetic “accommodation” through selection acting at multiple genetic sites over generations, novelties that were at first environmentally induced can gradually become independent of their initiating environments (West-Eberhard, 2003). This focus on changes in gene regulation as a central mechanism of development has led to a wave of new

The development of an adult organism from a single cell is one of the most familiar processes of nature. It has been studied by scientists for more than two centuries without the emergence of a generally accepted explanatory theory. Ironically, evolution, the other great historical process in biology, has been far more difficult to study, yet Darwin’s simple but powerful theory has guided scientists for 150 years. One reflection of the lack of an agreed-on set of developmental principles has been the persistence, in scientific as well as lay circles, of the seemingly endless nature versus nurture debate. Another is the gap that exists between the language, methods, and concepts used by scientists studying development at the psychological, behavioral, and the cellular/molecular levels. Less discussed than these issues, but more fundamental, has been the uncertainty about how evolution and development are related. The topic was opened with Ernest Haeckel’s (1892) resilient nineteenth-century formulation that “ontogeny recapitulates phylogeny” (pp. 422–544) that was finally laid to rest in 1977 with the publication of Ontogeny and Phylogeny by Stephen Jay Gould. In the past few decades, developmental psychobiology and, more recently, developmental neuroscience have made considerable progress in advancing an interdisciplinary approach to the study of development. New methods for the study of early behavior in animal model systems and in human fetuses have begun to build connections between events and concepts at the cellular/molecular, physiological, behavioral, and psychological levels. For it is in early development that one can best see how each of these levels of biological function emerge in sequence and come to work together.

EVOLUTION AND DEVELOPMENT In the past few years a new field, evolutionary developmental biology, or evo-devo, has emerged as new methods of functional genetic analysis and manipulation have 12

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Evolution and Development 13

discoveries in the area of “epigenetic” processes involving the remodeling of chromatin, the protein matrix surrounding, supporting, and controlling the access of molecular regulatory signals to the DNA strands within chromosomes (Baylin & Schuebel, 2007). These molecular rearrangements, which take place through simple chemical processes such as methylation and acetylation, provide, for the first time, a direct cellular/molecular link between the outside environment and the genes of the organism during development, a concrete and specifiable locus for the much-sought-after gene–environment interaction. Developmental Selection: The Evolution of Development The emphasis in evo-devo is on the proposed role of development in evolution, but what implications does this new evolutionary role have for the concept of development? What competitive advantages did the evolution of multicellular development convey? What selective pressures have shaped development during its own evolution, and what can these tell scientists about the adaptive functions that they should expect modern-day developmental processes to carry out? The question of how and when development evolved is essentially the question of the origins of multicellularity. It leads to one of the last remaining mysteries in the fossil record of evolution, the Cambrian explosion (Gould, 1989; Knoll, 2003). In this abrupt change in the number and variety of marine fossils, examples of all major animal groups or phyla that exist today (e.g., vertebrates, mollusks,

Cell division

Sexual recombination

arthropods, worms) appeared abruptly approximately 500 million years ago. For the more than 3 billion years before that, only fossils of single-celled organisms have been found. Modern-day representatives of these ancient organisms, such as algae and amoebae, seem little changed in form since that time, but they have been found to possess all the characteristics of developing cells in multicellular animals (see Figure 2.1) such as rapid multiplication, migration, adhesion, the capacity for sexual as well as asexual reproduction, and the capacity for differentiation (e.g., from an amoeba to a flagellate form). These adaptive cellular/molecular mechanisms evolved in response to “signal” molecules in their environments that accompanied or produced changing conditions. What is missing in these protozoa is the organization of these cellular/molecular processes into a linear series of events involving many such cells so as to produce structures that grow in size, shape, and complexity of form and function (see Figure 2.2). This is called development, and it must have emerged very rapidly (in geologic time), as it was crucial for the appearance of multicellular life. The prerequisite for this emergence seems to have been a gradual increase in the complexity of the genome of unicellular animals. Evidence for this lies in the progressive appearance of unicellular lineages whose living members showed larger and larger numbers of introns (the noncoding segments of DNA that facilitate “shuffling” of gene positions along the DNA strand), a multiplication of promoters of gene activation, and the appearance of novel regulatory elements leading to a greater degree of interaction between genes. These genomic changes allowed for

Adhesion Migration

Sporulation

Apoptosis

Differentiation

Molecular signals

Environmental signals

Figure 2.1 Protozoan precursors of developmental processes. Note: Unicellular organisms evolved nearly all of the cellular/molecular mechanisms needed for multicellular development over the 3 billion years of life prior to the Cambrian explosion of diverse multicellular organisms

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500 million years ago. These processes were selected for their capacity to enhance the adaptive capacity of these single-cell organisms to changes in the surrounding environment.

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Developmental Neuroscience

Note: The rapid evolution of metazoan (multicellular) organisms took place through a reorganization of the various individual cellular/molecular mechanisms of unicellular organisms (represented on the left; see also Figure 2.1) into the integrated, linear processes of development, a process driven by the selective advantage of larger and more complex multicellular

organisms capable of creating and exploiting novel ecological niches. During evolution, cells continued to be regulated by signal molecules in the intercellular environment of the developing metazoan organism, as they had been in unicellular organisms. A return to single cells marks the beginning of the next multicellular generation of metazoans, depicted on the far right.

the rapid emergence of a novel system of genetic control for all multicellular animals in which only a limited set of genes within each cell is expressed at any one time while the vast majority are silenced. Genes in the fertilized egg, or zygote, express signal proteins that diffuse into the intercellular space and then into neighboring cells, where they act as transcription factors regulating the genetic control of growth and function as well as the expression of a further wave of different intercellular signal proteins. The timing, amount, and structure of these waves of signal proteins create a virtual cascade of intercellular communication, and with it a complex pattern of growth and function over time. These developmental cascades specify and organize the construction of a multicellular organism and its complex array of functions, including behavior. Signal molecules in the intercellular environment continue to act to regulate the timing and nature of cellular changes, as they had in unicellular organisms. The development of all multicellular organisms is strikingly similar in its early stages. An egg, fertilized by one other smaller cell, divides repeatedly, forming a ball of similarly appearing cells, the zygote. Gradually, the zygote becomes a blastocyst, with two layers of cells: the endoderm inside and the ectoderm outside. These two sheets of cells become bent and folded, developing a radial symmetry. The development of the most primitive multicellular animals, such as sponges, hydra, and jellyfish, stops here. Behaviorally, these simple creatures show primarily local cellular “irritability” and generalized “flowing” body movements coordinated by nerve nets. But in the metazoan animals of the Cambrian period, an invagination of the hollow blastocyst occurs and a third layer of

cells, the mesoderm, forms between the two other layers. These layers bend to form shapes with symmetry in three planes, and they expand to form groups and sheets of cells that interact with groups of different cells deriving from the other layers. As these cells change in form and location, they differentiate into radically different cell types in different regions to become organs with multiple different functions. One of those organs, the nervous system, becomes the basis for the evolving complexity of animal behavior that so interests neuroscientists. There are good reasons to think that the selective advantage responsible for the evolution of this developmental plan for multicellular life was that it made possible the construction of novel, much larger, and more complex animals. These animals not only were able to use the previously dominant unicellular species as food but were capable of finding and even creating new ecological niches: territories, food sources, and ways of life that bacteria and protozoa were incapable of inhabiting, defending, or utilizing. Bonner (1993) defined development as the growth phase of the life cycle and demonstrated that its duration (and complexity) is highly correlated with the size (and complexity) of any given life form, ranging from bacteria to whales, each enabled to exploit larger and more complex niches. This view of development as construction is widely accepted and has too often led to an assumption that all developmental mechanisms are organized to carry out a single function: the building of a successful adult. The rapid evolution of development, however, was shaped not only by the selective advantages provided by the size and functional complexity of the adults that were constructed. Other developmental processes and functions

Figure 2.2 The evolution of development.

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Evolution and Development 15

had to be selected in order to adapt immature forms to the changing environments they inhabited as embryos, larvae, fetuses, newborns, and weanlings on their path to adulthood. The unique characteristics of larval forms in amphibians, and of intrauterine/placental structures and early nursing interactions in mammals, are examples of the “ontogenetic adaptations” that play such a prominent role in the development of animals that one can study today (Oppenheim, 1984). Furthermore, as the processes mediating the developmental functions of construction and adaptation evolved, they were also being shaped and modified by selection for their “evolvability” (R. Carroll, 2002). That is, processes that created potentially useful variations in the course of development and others that promoted the replication of a successful variant developmental path in the next generation were differentially selected. In the same way that genetic mechanisms for variation (recombination and mutation) and for heritability (the copying of DNA during cell division) were shaped by selection in the evolution of single cells, so a set of developmental mechanisms for “facilitating” variation and for transmitting successful variant paths to the next generation was shaped by selection in the evolution of development in multicellular organisms. Thus, the developmental processes that scientists study today have been organized and shaped in their evolution by the four components of what I have called developmental selection: construction, adaptation, variation, and inheritance (Bateson, Hofer, Oppenheim, & Wiedenmayer, 2007; Hofer, 2005). As with many other features of organisms, developmental processes have been co-selected; that is, processes selected for their contribution to the construction and ontogenetic adaptation of young animals have also been shaped by selection for their heritability and their capacity for variation (see Figure 2.3). For example, the embedding of embryos and fetuses in the internal environments of their mothers, and the sustained close physical interactions of newborns and infants with their parents (Rosenblum & Moltz, 1983), gain a new significance and interpretation if one thinks of them in terms of developmental selection. These features of development can now be viewed as inherited, transgenerational environments that act as a protective scaffolding and matrix for the construction of a descendant in the next generation and as a source of potentially useful variation in the future. In this way, long and complex early developmental pathways can be organized in a linear fashion, regulated by interactions with the previous generation so as to be re-created, with further variations, in the next generation. The usefulness of the concept of developmental selection, it seems to me, is that it defines a set of functions that developmental processes are organized to carry out.

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Figure 2.3 Developmental selection. Note. During evolution, developmental processes are co-selected not only for (a) construction of an adult, but also for their capacity to (b) adapt the immature organism to its age-specific environments, (c) produce potentially useful variations in the course and/or outcome of development, and (d) facilitate the inheritance of successful variants in the next generation.

Knowing the ultimate (i.e., evolutionary) functions of developmental processes should be useful for forming new hypotheses about how a given process works. For example, in the field of sociobiology, the evolutionary principles of kin selection led to the discovery of new processes at work within the parent–offspring relationship, such as parent– offspring conflict. In the case of development, asking how specific events and processes contribute to age-specific adaptation, variation, and inheritance of a selected developmental path in the next generation should increase understanding of development considerably beyond the current approach of asking questions that are limited to the function of constructing an adult. Examples are given in the sections on attachment and the regulation of development. Levels of Organization Developmental neuroscience involves a journey in time across levels of organization from the molecular genetics of an embryonic cell to the mental experiences of a conscious mind (see Figure 2.4). The gap that exists between biology and psychology is slowly narrowing as new biological methods have become increasingly able to approach the brain systems underlying more and more complex cognitive and emotional processes. In the study of early behavior development, the conceptual models of psychologists and the working models of biologists converge, as they are being applied to the same behavioral phenomena. Observations and insights from these different levels of organization are beginning to contribute to one another ’s understanding, as I illustrate with regard to the area of early attachment in the next section. Evolutionary principles offer a conceptual common ground that can be shared by both neuroscience and psychology,

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Developmental Neuroscience

Size Complexity

Time/Age Figure 2.4 Levels of organization in developmental neuroscience. Note: During development, new properties emerge at each level that are not reducible to lower-level events. A full understanding that extends across levels requires repeated efforts at “translation” as well as extensive knowledge of events at each level. Early development affords significant simplification of this process at all levels.

providing answers to questions about how the human mind and brain have come into being and why they have their present forms. The historical nature of both development and evolution bridges the gap that exists between the reductionist emphasis of the molecular/cellular neurosciences and the holistic emphasis on inner experience and meaning that is the central focus of some branches of psychology. Early human development traverses a series of levels of scale and organization (as illustrated in Figure 2.4) from the molecular and intercellular interactions of the embryo, to the integrated systems and behavior of the fetus, to the emerging cognitive and affective capacities of the child, and finally to the inner experience that even language cannot fully describe. The biological, behavioral, and psychological processes at work at those levels of organization seem very different. But the new properties that emerge at higher levels arise from the combined operation of simpler component processes taking place at the lower level. Understanding those transitions, and the emergence of new properties at higher levels, is one of the central questions for research in early animal and human development as well as for attempts to integrate neuroscience and psychology in general. In early development, behavior provides a crucial link between the levels of brain systems and of psychological constructs, as is illustrated in the following sections.

THE EARLY DEVELOPMENT OF ATTACHMENT In this section I outline how a strategy of attempting to understand the component processes underlying the psychological constructs created in studying early human

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attachment can provide new and potentially useful ways of thinking in a more general way about development as it occurs at multiple levels of organization. The idea of “early attachment” exists in a number of forms in people’s ways of speaking and thinking. In its most general sense, the phrase refers to a set of behaviors observed in infants and the feelings and thought processes (conscious and/ or unconscious) that we suppose infants have with their mothers or caretakers, based on our own experiences and memories and the psychological concepts we have formed for ourselves or learned from others (see also Volume 2, Chapters 36–42 and Chapters 47–49). Within this range of use of the word attachment, several different schools of thought have coalesced within psychology. Common to all, however, are three themes: (1) some sort of emotional tie or bond that is inferred to develop between the infant and his or her caretaker that keeps the infant physically close, (2) a series of responses to separation that constitute the infant’s emotional response to interruption or rupture of that bond, and (3) the existence of different patterns or qualities of the interaction between infants and mothers that persist over time and lead to longterm effects of this experience on the social and emotional functioning of offspring throughout life, even extending to a repetition of specific patterns of mothering by daughters in the next generation. These observations and the psychological concepts of attachment have been extremely useful in human developmental psychology, but they leave a number of observations unexplained and questions unanswered. Furthermore, when my colleagues and I found close similarities in the behavior and separation responses of a far less evolved mammal, the laboratory rat pup, this suggested that we were missing a deeper layer of biological processes underlying the psychological concepts of attachment theory. The unanswered questions left open by developmental attachment theory are posed in the sections that follow. The answers that came from our laboratory research illustrate how evolutionary developmental theory and the concept of developmental selection help to better explain the nature and functions of early attachment at the psychological as well as the biological levels. Deconstructing the First Attachment Bond Infants of mammalian species that are born in an immature state, such as the human and the laboratory rat, face a daunting task. They must find a way to identify, remember, and prefer their own mother, and they must use these capacities to reorganize their simple motor repertoires, long adapted to the uterine environment, so as to be able to approach, remain close to, and orient themselves to their mothers.

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The Early Development of Attachment

Until recently it was assumed that these bonding processes were well beyond the capacities of newborn mammals (except in precocial species such as the sheep) and that the closeness of the relationship depended almost entirely on maternal behavior until well into the nursing period (Bowlby, 1982; Kraemer, 1992; Volume 2, Chapter 48). It was supposed that an attachment bond builds up slowly in the weeks or days after birth through repeated mother– infant interactions, starting with stereotyped reflexes in the newborn. But the past decade has produced a number of studies revealing evidence of earlier and earlier learning, extending even into the prenatal period, as is described here. In addition, coordinated motor acts have been demonstrated experimentally in fetuses in response to specific stimuli that will not be encountered until after birth. Thus, the solutions for the infant’s tasks appear to be found much earlier than previously thought and appear to take place through novel developmental processes that had not been imagined until recently. These developmental processes clearly function as age-specific adaptations to the unique environments of early development, playing only a supportive role in growth of size and complexity, for many of these behaviors (e.g., nursing) have been shown not to be precursors of later behaviors. Prenatal Origins The first strong evidence for fetal learning came from studies on early voice recognition in humans, in which it was found that babies recognize and prefer their own mother ’s voice, even when tested within hours after birth (De Casper & Fifer, 1980). Bill Fifer continued these studies in our department using an ingenious device through which newborns can choose between two tape-recorded voices by sucking at different rates on a pacifier rigged to control an audiotape player (Fifer & Moon, 1995). He has found that newborn infants, in the first hours after birth, prefer human voices to silence, female voices to male, their native language to another language, and their own mother to another mother reading the same Dr. Seuss story. In order to obtain more direct evidence for the prenatal origins of these preferences (rather than very rapid postnatal learning), Fifer filtered the high-frequency components from the tapes to make the mother ’s voice resemble recordings of maternal voice by hydrophone placed within the amniotic space of pregnant women. This altered recording, in which the words were virtually unrecognizable to adults, was preferred to the standard mother ’s voice by newborns in the first hours after birth, a preference that tended to wane in the second and third postnatal days. Furthermore, there is evidence that newborns prefer familiar rhythmic phrase sequences to which they have been repeatedly exposed prenatally when pregnant mothers daily read out loud a specific text in a quiet place (De Casper & Spence, 1988).

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In a striking interspecies similarity, rat pups were shown to discriminate and prefer their own dams’ amniotic fluid to that of another dam when offered a choice in a head-turning task (Hepper, 1987). Newborn pups were also shown to require amniotic fluid on a teat in order to find and attach to it for their first nursing attempt (Blass, 1990). Robinson and Smotherman (1995) directly tested the hypothesis that pups begin to learn about their mothers’ scent in utero. They were able to demonstrate one trial taste aversion learning and classical conditioning in late-term rat fetuses using intraoral cannula infusions and perioral stimulation. Taste aversions learned in utero were expressed in the free-feeding responses of weanling rats nearly 3 weeks later. The authors went on to determine that aversive responses to vibrissa stimulation were attenuated or blocked by intraoral milk infusion, a prenatal “comfort” effect they found to be mediated by a central kappa-opioid receptor system. These forms of fetal learning, involving maternal voice in humans and amniotic fluid in rodents, appear to play an adaptive role in preparing the infant for its first extrauterine encounter with its mother. They are thus the earliest origins as yet found for attachment to the mother. The spontaneous motor acts needed for an attachment system also appear to be developing prior to birth. Rat fetuses engage in a number of spontaneous behaviors in utero, including curls, stretches, and trunk and limb movements. These acts were observed to increase markedly in frequency with progressive removal of intrauterine space constraints as pups were observed first through the uterine wall, then through the thin amniotic sac, and finally unrestrained in a warm saline bath (Smotherman & Robinson, 1986). When newborn pups are observed prior to their first nursing bout they resemble exteriorized fetuses, until the mother lowers her ventrum over them. Their behavior then changes rapidly over the first few nursing bouts into a complex repertoire as described in the next section. How Newborns Approach and Orient to Their Mothers When newborn pups in their first extrauterine experience are stimulated gently by soft surfaces from above, as when the mother hovers over them, they show a surprisingly vigorous repertoire of behaviors (Polan & Hofer, 1999). These include the spontaneous curling and stretching seen prenatally but also locomotor movement toward the suspended surface, directed wriggling, audible vocalizations, and, most strikingly, turning upside down toward the surface above them. Evidently these behaviors propel the pup into close contact with the ventrum, maintaining it in proximity and keeping it oriented toward the surface. They thus appear to be very early attachment behaviors. In a series of experiments my colleague and I found that these are not

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stereotyped reflex acts but organized responses that are graded according to the number of maternal-like stimulus modalities present on the surface presented experimentally (e.g., texture, warmth, odor; Polan & Hofer, 1999). Furthermore, they were enhanced by periods of prior maternal deprivation, suggesting the rapid development of a motivational component. We found that by 2 days of age, pups discriminate their own mother ’s odor and prefer it to equally familiar nest odors (Polan & Hofer, 1998). Hepper (1987) showed that by the first postnatal week, pups discriminate and prefer their own mother, father, and siblings to other lactating females, males, or agemates. Recent work in humans inspired by these findings in lower animals has shown that human newborns, too, are capable of slowly locomoting across the bare surface of their mother ’s abdomen and locating the breast scented with amniotic fluid in preference to the untreated breast (Varendi, Porter, & Winberg, 1996). Although newborns are attracted to natural breast odors even before the first nursing bout (Makin & Porter, 1989), amniotic fluid can override this effect. Apparently, human newborns are not as helpless as previously thought but possess approach and orienting behaviors that anticipate the recognized onset of specific maternal comfort responses at 6 to 8 months. These events and processes can best be understood in terms of the four primary functions of development (construction, adaptation, inheritance, and variation). More and more complex behavior patterns are constructed, beginning with simple local movements and progressing through organized graded responses to specific stimuli. Then with a unique rapid learning process, behavior becomes organized into an adaptive repertoire of nursing-related behaviors. These developmental steps function in part as transient adaptations first to the confines of the uterus and then to the requirements of staying close, finding a nipple, and initiating sucking. One can also see how the inherited developmental environment of the uterus and the maternal nest supply a matrix and a template for the formation and organization of species-specific mother–infant interaction patterns. Thus, the developmental environments and events of the uterine cavity and maternal ventrum function to ensure that the same developmental path will be repeated in the next generation (i.e., inherited). As differences occur in the kinds and intensities of maternal stimulation of pups or in other aspects of the interaction, variation from one generation to the next will also take place. It is in the process of fulfilling these functions of development (adaptation, inheritance, and variation) that the organization and construction of more complex repertoires of behavior takes place. Looking for novel development processes that fulfill the functions of variation and inheritance should lead to a deeper understanding of how development works.

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A Novel Postnatal Learning System Bowlby (1969) was uncertain about exactly how a behavioral attachment system or bond developed in slowmaturing mammals and hypothesized that some learning mechanism must exist that is similar to the phenomenon of “imprinting” in birds, made famous by Konrad Lorenz (1996). Scientists are now beginning to gain an understanding of how such a specific proximity–maintenance system develops in animals and humans at the levels of basic learning mechanisms and the brain systems mediating them. Regina Sullivan and Steven Brake in our lab discovered that within 2 to 3 days of birth, neonatal rat pups were capable of learning to discriminate, prefer, approach, and maintain proximity to an odor that had been associated with forms of stimulation that naturally occurred within the early mother– infant interaction (e.g., milk or stroking; Sullivan, Hofer, & Brake, 1986). Random presentations of the two stimuli had no such effect, a control procedure that identified the change in behavior as being due to associative conditioning and not some nonspecific effect of repeated stimulation. Because the learning required only two or three paired presentations and because the preference was retained for many days, it seemed to qualify as the long-sought “imprinting-like process” that is likely central to attachment in slow-developing mammals. Indeed, a human analogue of this process was found by Sullivan, who showed that when human newborns were presented with a novel odor and were then rubbed repeatedly along their torsos to simulate maternal care, the next day they became activated and turned their head preferentially toward that odor (Sullivan et al., 1991). This suggests that rapid learning of orientation to olfactory cues is an evolutionarily conserved process in mammalian newborns. Early attachment-related odors appear to retain value into adulthood, although the role of the odor in modifying behavior appears to change with development. Work done independently in the labs of Celia Moore (Moore, Jordan, & Wong, 1996) and Elliot Blass (Fillion & Blass, 1986) has demonstrated that adult male rats showed evidence of enhanced sexual performance when exposed to females scented with the artificial odors with which their mothers had been scented during the male rats’ infancy. Aversive Learning of the Attachment Bond Clinical observations have taught that not only does attachment occur to supportive caretakers, but children can endure considerable pain and even injury while becoming strongly attached to an abusive caretaker. Although it may initially appear to be counterproductive from an evolutionary perspective to form and maintain an attachment to an abusive caretaker, it may be better for a slow-developing mammalian infant to have a bad caretaker than none at all.

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The Early Development of Attachment

This aspect of human attachment is also represented in the infant rat. We found that during the first postnatal week, a surprisingly broad spectrum of stimuli can function as reinforcers to produce an odor preference in rat pups (Sullivan, Brake, Hofer, & Williams, 1986; Sullivan, Hofer, et al., 1986). These stimuli range from apparently rewarding ones such as milk and access to the mother (Alberts & May, 1984; Brake, Sager, Sullivan, & Hofer, 1982; Pedersen, Williams, & Blass, 1982; Wilson & Sullivan, 1994) to apparently aversive ones such as moderate shock and tailpinch (Camp & Rudy, 1988; Sullivan, Hofer, et al., 1986), stimuli that elicit immediate escape responses from the pups. It should be noted that threshold to shock (Stehouwer & Campbell, 1978) and the pup’s behavioral response (Emerich, Scalzo, Enters, Spear, & Spear, 1985) do not change between the ages of 9 and 11 days. As pups mature and reach an age when leaving the nest becomes more likely, olfactory learning comes to more closely resemble learning in adults. Specifically, odor aversions are easily learned by 2-week-olds, and acquisition of odor preferences is limited to odors paired with stimuli of positive value (Sullivan & Wilson, 1995). Thus, the learning that underlies early attachment develops through a transiently adaptive “paradoxical” phase during which positive associations take place in response to a very broad range of contingent events (including painful stimulation) while pups are confined to the nest. It becomes more selective at a time in development when pups begin leaving the nest and encountering novel odors not associated with the mother. Brain Substrates for Two Kinds of Early Attachment Learning Early rapid aversive learning has been traced to focal odor-specific areas in the olfactory bulb by Sullivan and Donald Wilson. Certain cell types alter their firing rates in response to a specific odor as a result of learning experience (Sullivan, Wilson, Wong, Correa, & Leon, 1990; Wilson & Sullivan, 1994). This altered firing rate is the result of activation of norepinephrine pathways leading from the locus coeruleus. Indeed, behavioral learning can be driven by electrical stimulation of the locus or norepinephrine injection in the olfactory bulb in association with the novel odor, without the association of any maternal stimuli with the novel odor. The period during which aversive learning of position associations takes place ends about 10 days postnatal, but this “sensitive period” can be extended by a few repeated brief associations of odor with shock each day until weaning begins about a week later. The period from 12 to 15 days of age is an interesting one because during this time, if the mother is present during the association of odor and aversive stimulation, preference

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learning takes place, but in the mother ’s absence, the odor is subsequently avoided. Recent studies of neural and hormonal substrates has helped to explain this unusual developmental pattern (Moriceau, Wilson, Levine, & Sullivan, 2006). As illustrated in Figure 2.5, the primary brain substrate activated by the later developing avoidance learning in 12- to 15-day-old pups with the mother absent was the amygdala, not the locus coeruleus and olfactory bulb. However, in the mother ’s presence, there was no amygdala activation. Instead, positive association learning took place, mediated by the locus coeruleus. Corticosterone levels were previously known to be reduced by maternal presence at this age. Moriceau and Sullivan administered increased corticosterone to the pups learning in the mother ’s presence and less corticosterone to those learning in the mother ’s absence. The results showed that corticosterone levels mediated the maternal “switch” between the formation of positive and negative association learning and did so by switching the neural substrates mediating the response between the locus coeruleus/olfactory bulb and the amygdala. It is relatively easy to see how these seemingly complex paths of development in early systems for learning are likely to have evolved. Only outside the nest, and in the absence of the mother, is it crucial to learn an avoidance response to all painful stimuli. In interactions with the mother, learning to avoid her as a result of painful or uncomfortable associated conditions would likely lead to far more dangerous circumstances. The specialized environmental contingencies of early life have selected, during evolution, a sequence of the development of learning that would be hard to explain except within the concept of developmental selection. The next steps in this research will be to learn which genetic, experiential, and developmental processes were involved in the creation of these developmental paths during evolution and how they

Early

Corticosterone levels low

Late

Corticosterone levels high

Figure 2.5 Summary of transition between two learning systems and their neural substrates in the formation of early attachment in rat pups. Note. Early ⫽ birth to 10 days of age. Late ⫽ 11 to 15 days. Differences in properties and known mechanisms of the two forms of learning can be shifted in time across the 10-day transition point according to corticosterone levels and/or presence or absence of mother during learning.

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became integrated into the developmental sequences and mechanisms this work has revealed. Parallel Processes in Human Maternal Attachment Learning Successful mother–infant interactions require the reciprocal responding of both individuals in the mother–infant dyad. Human mothers rapidly learn about their baby’s characteristics and can identify their baby’s cry, odor, and facial features within hours of the baby’s birth (Eidelman & Kaitz, 1992; Kaitz, Lapidot, & Bronner, 1992; Porter, Cernoch, & McLaughlin, 1983). An animal model for this rapid learning by mothers has received considerable attention (Brennen & Kaverne, 1997; Fleming, O’Day, & Kraemer, 1999). Indeed, there are interesting parallels between the early attachment behavior of infants and the attachment behavior of the newly parturient mother. In rats and sheep, a temporally restricted period of postpartum olfactory learning in the mother involving norepinephrine facilitates the mother ’s learning about her young (Levy, Gervais, Kindermann, Orgeur, & Piketty, 1990; Moffat, Suh, & Fleming, 1993). It is possible that mammalian mothers and their young use similar neural circuitry to form their reciprocal attachments, both abusive and normal.

EARLY SEPARATION AS LOSS OF REGULATION In Bowlby (1969, 1982) and Harlow’s (1958) work, as well as in the clinical observations of Anna Freud and Dorothy Burlingham (1943) a generation before, it was maternal separation that revealed the existence of an “animal tie” between mother and infant and a deeper layer of processes beneath the apparently simple interactions of mother and infant. Bowlby (1969, 1982) viewed these processes as primarily psychological. The behavioral and physiological responses of the infant to separation, in their conception, were a consequence of the rupture of a psychological bond that was formed as part of an integrated psychophysiological organization that Bowlby called the attachment system. More recent research, however, has revealed a network of simple behavioral and biological processes that underlie this and other psychological concepts used to understand early human social relationships (see Volume 2, Chapter 47). The Separation Cry One of the best known responses to maternal separation is the infant’s separation cry, a behavior that occurs in a wide variety of species (Lester & Boukydis, 1985; Newman,

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1998). In the rat, this call is in the ultrasonic range (40 kHz) and appears in the first or second postnatal day. Pharmacological studies in a number of labs (reviewed in Hofer, 1996) have shown that the ultrasonic vocalization (USV) response to isolation is attenuated or blocked in a dose-dependent manner by clinically effective anxiolytics that act at benzodiazepine and serotonin receptors. Conversely, USV rates are increased by compounds known to be anxiogenic in humans, such as benzodiazepine receptor inverse agonists (beta-carboline, FG 1742) and GABA-A receptor ligands such as pentylenetetrazol (Brunelli & Hofer, 2001; Miczek, Tornatsky, & Vivian, 1991). Within serotonin and opioid systems, receptor subtypes known to have opposing effects on experimental anxiety in adult rats and humans also have opposing effects on infant USV calling rates (see Figure 2.6). Neuroanatomical studies in infant rats have shown that stimulation of the periaquaductal grey area produces USV calls, and chemical lesions of this area prevent calling (Goodwin & Barr, 1998). The more distal motor pathway is through nucleus ambiguus and both laryngeal branches of the vagus nerve. The engagement of higher centers known to be involved in cats and primates suggests a neural substrate for isolation calls involving primarily the hypothalamus, amygdala, thalamus, and hippocampus, and cingulate cortex, brain areas known to be involved in adult human and animal anxiety responses (Newman, 1998). This evidence strongly suggests that separation produces an early affective state resembling anxiety in rat pups, one that is expressed by the rate of infant calling. This calling behavior, and its inferred underlying affective state, develops as a communication system between mother and pup. The evolution of such a response is clarified by the finding that infant rat USV is a powerful stimulus for the lactating rat, capable of causing her to interrupt an ongoing nursing bout, initiate searching outside the nest, and direct her search toward the source of the calls (Smotherman, Bell, Hershberger, & Coover, 1978). The mother ’s retrieval response to the pup’s vocal signals then results in renewed contact between pup and mother. This contact, in turn, quiets the pup (as represented along the bottom right of Figure 2.6). This entire behavioral system fades out as infants cease to vocalize when isolated during the weaning period, showing that it represents a developmental ontogenetic adaptation. In the psychological concept of attachment, vocal separation and comfort responses are conceptualized as emotional expressions of interruption and reestablishment of a social bond. Such a formulation would predict that because pups recognize their own mothers by the mothers’ scents (as previously described), pups made acutely anosmic would fail to show a comfort response. But anosmic pups

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Early Separation as Loss of Regulation

Figure 2.6 The regulatory control system for infant ultrasonic vocalization. Note. BZi ⫽ benzodiazepine inverse agonist; CRH ⫽ corticotropinreleasing hormone; DA ⫽ dopamine; 5-HT ⫽ 5-hydroxytryptamine (serotonin); GABA ⫽ gamma-amino butyric acid; NA ⫽ noradrenaline; NMDA ⫽ N-methyl-D-aspartic acid; OT ⫽ oxytocin; VP ⫽ vasopressin. Moving counterclockwise from the far right in the diagram, interactions of a rat pup with its mother (proximity, warmth, etc.) act over multiple

show comfort responses that are virtually unaffected by the loss of their capacity to recognize their mothers in this way (Hofer & Shair, 1991). Instead Harry Shair, Susan Brunelli, and I have found multiple regulators of infant USV within the contact between mother and pup: warmth, tactile stimuli, and milk as well as her scent (Hofer, 1996). Provision of stimulation in these modalities separately (e.g., artificial fur lacking warmth or scent) and then progressively combining modalities elicited graded responses. The full “comfort” quieting response was elicited only when all modalities were presented together, and maximum calling rates occurred when all were withdrawn at once. In essence, we found parallel regulatory systems involving different sensory modalities (see Figure 2.6). These functioned in a cumulative or additive way, with the rate of infant calling reflecting the sum total of effective regulatory stimuli present at any given point in time. These processes at the behavioral/biological level underlie the psychological concept of separation anxiety. They do not supplant it. They operate at a different level of organization; the psychological concepts can be thought of as emerging from the lower-level component processes. Searching for What Was Lost Experiments in our laboratory have shown that infant rats also have more complex and lasting responses to maternal

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pathways (olfactory, thermal, etc.) to activate infant sensory systems (center top) and then brain neurotransmitter receptor systems (in box on the left). Those receptors in the (⫹) column, when activated, increase calling rate (ultrasonic vocalization), and those in the (–) column suppress calling. Maternal separation (lower center) results in a rapid burst of calling that gradually subsides. Continuing counterclockwise from the center of the diagram, ultrasonic vocalization by the pup stimulates maternal retrieval, licking, and so on, reinstating contact and maternal interactions (far right), thus closing the circle.

separation, similar to primates in a number of different physiological and behavioral systems. A number of years ago colleagues and I found slower developing changes following maternal separation, similar to those of Bowlby’s (1969, pp. 27–28) “despair” phase (see Figure 2.7A). This was not an integrated psychophysiological response as Bowlby had supposed but the result of a novel mechanism (see Figure 2.7B). As separation continued beyond the initial vocal response, each of the individual systems of the infant rat responded to the loss of one or another of the components of the infant’s previous interaction with its mother. Providing one of these components to a separated pup (e.g., maternal warmth) maintained the level of brain biogenic amine function underlying the pup’s general activity level for up to 3 days (Stone, Bonnet, & Hofer, 1976) but had no effect on other systems. For example, the pup’s cardiac rate continued to fall to 60% of its normal level over 24 hours regardless of whether supplemental heat was provided (Hofer, 1971). We found that the heart rate, normally maintained by sympathetic autonomic tone, was regulated by provision of milk acting on receptors in the lining of the pup’s stomach (Hofer & Weiner, 1975). With loss of the maternal milk supply, sympathetic tone fell and cardiac rate was reduced by 40% in 12 to 18 hours. By studying a number of additional systems—such as those controlling sleep–wake states, activity level, sucking pattern, and blood pressure (Brake et al., 1982; Hofer, 1975,

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Developmental Neuroscience

(A)

(B)

Growth Hormone

Activity Heart Rate REM Sleep

Figure 2.7 Two schematic representations of the dynamics of early separation. Note. A: as conceptualized in the framework of attachment theory by John Bowlby and B: as found to result from loss of regulatory interactions within the mother–infant relationship.

1976; Shear, Brunelli, & Hofer, 1983)—we found different components of the mother–infant interaction such as olfaction, taste, touch, warmth, and texture that normally either upregulated or downregulated each of these functions (or, in the case of sleep states, regulated rhythmic patterning). Thus, we concluded that in maternal separation, all of these regulatory components of the mother–infant interaction are withdrawn at once (see Figure 2.7). This widespread loss creates a pattern of increases or decreases in level of function of the infant’s systems, depending on whether the particular system had been up- or downregulated prior to separation by specific components of the previous mother– infant interaction. We called these components hidden regulators because they were not evident when one simply observed the ongoing mother–infant relationship. These studies revealed a novel mechanism for the infant response to separation, an event that previously could be

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conceptualized only at the psychological level, as an emotional response. In younger infants this was conceived to be the result of rupture of the bond between mother and infant, and in older children the stress resulting from perception of the loss of the mother ’s emotional, physical, and nutritional support. At the biological level of organization we found that separation resulted in the withdrawal or loss of a number of different physical interactions between mother and infant that normally regulated infant physiology and behavior on an ongoing basis. Thus, we found that infant and mother compose a partial fusion of two individual homeostatic units into a single common homeostatic organization. When separated, each returns to its individual set points at lower or higher levels of function. The regulation of maternal milk letdown by infant sucking is a wellknown example of a hidden regulatory system present in all adult female mammals. When the suckling interaction

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Early Separation as Loss of Regulation

is interrupted, the mother ’s milk production and periodic letdown become greatly reduced. The observations of psychologists on the evident changes in mental state of infants during separation and the psychological constructs used to explain them remain valid and useful. However, the processes at the biological level described here can enlarge psychological understanding and support newer psychological concepts of emotional and perceptual regulation. It is not that rat pups respond to loss of regulatory processes, whereas humans respond to emotions of love, sadness, anger, and grief. Human infants, as they mature, can respond at the level of complex affective responses and symbols as well as at the level of regulatory interactions. And rat pups respond at the level of an affective state expressed through separation calling as described previously. Even adult humans continue to respond in important ways at the sensorimotor– physiologic level in their social interactions (Hofer, 1984; Stern & McClintock, 1998). Examples include the role of social interactions in entraining sleep–wake and menstrual rhythms, the disorganizing effects of sensory deprivation, and the remarkable effects of social support on the course of medical illness. This extended homeostatic system of mother and infant represents an aspect of mammalian early development similar to the intertwined nature of the fetus and mother, essentially an extension of the symbiosis recognized in the intrauterine period of development. By applying the concept of developmental selection, it is evident that these evolved adaptations function to provide a living environment that is inherited from one generation to the next to serve as a guiding matrix, enabling a specific early developmental course to be repeated (see Figure 2.8). Variations in maternal behavior are capable of producing variations in infant physiology and behavior through the regulatory processes described. These variations can be immediately adaptive. For example, the major thermoregulatory, cardiovascular, and behavioral responses of pups separated for a period of hours slow metabolism, shunt blood away from the digestive tract to the heart and brain, and profoundly reduce spontaneous behavior, inducing a hibernation-like state that promotes survival until the mother returns. Furthermore, variations in infant development that are repeated over generations (e.g., because of increased maternal foraging demands during climate change creating long absences) become targets for selection and can become increasingly efficient and finally genetically fixed, producing an alternative infant phenotype. In this way one can better understand why these widespread and complex regulatory process evolved to become a feature of mammalian early development, serving the developmental functions of inheritance and variation as well as construction and adaptation.

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Hidden Regulators of Early Development Other investigators, using this approach, have since discovered other maternal regulatory systems of the same sort. For example, Saul Schanberg, Cynthia Kuhn, and colleagues found that separation of the dam from rat pups produced a rapid (30-min) fall in the pup’s growth hormone levels, and vigorous tactile stroking of maternally separated pups (mimicking maternal licking) prevented the fall in growth hormone (Kuhn & Schanberg, 1991). Brain substrates for this effect were then investigated, and it now appears that growth hormone levels are normally maintained by maternal licking, acting through serotonin (5HT) 2A and 2C receptor modulation of the balance between growthhormone-releasing factor and somatostatin that together act on the anterior pituitary release of growth hormone (Katz, Nathan, Kuhn, & Schanberg, 1996). The withdrawal of maternal licking by separation allows growth-hormonereleasing factor to fall and somatostatin to rise, resulting in a precipitous fall in growth hormone and cessation of growth processes generally for several days. However, a parallel process in prematurely born human infants showed longer-term regulation. There are several biological similarities between this maternal deprivation effect in rats and the growth retardation that occurs in some variants of human reactive attachment disorders of infancy. Applying this new knowledge about the regulation of growth hormone to lowbirth-weight, prematurely born babies, Tiffany Field and coworkers (1986) joined the Schanberg group. They used a combination of stroking and limb movement, administered 3 times a day for 15 min each time and continuing throughout the babies’ 2 weeks of hospitalization. This intervention increased weight gain, head circumference, and behavior development test scores in relation to a randomly chosen control group, with earlier discharge from the intensive care unit and other enhanced maturational effects discernible 6 months later. Clearly, early regulators are also effective in humans, and over time periods as long as weeks to months. As experimenters began to realize that infants’ separation responses revealed a network of individual regulatory processes within the mother–infant interaction, an important implication of this finding emerged: These ongoing regulatory interactions could, over the long term, act to shape the development of an infant’s brain and behavior throughout the preweaning period when mother and infant remained in close proximity. And when maternal behavior changed in response to changes in her environment, this could change the course of her offspring’s development. We could now think of the mother–infant interaction as a long-term regulator of development, with variations in

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Developmental Neuroscience

the intensity and patterning of mother–infant interactions gradually shaping the development of behavior and physiology. These processes go far beyond the adaptive evolutionary role of attachment as a protection against predators proposed by Bowlby (1969). They also go beyond the role described in the previous section of a flexible adaptive response of the infant to the environmental conditions that caused maternal separation. Here, development is regulated along a different path for a substantial period of time. With the mother absent for even longer time periods, perhaps never to return, and without another effective source of regular stimulation and caretaking, an overall slowing of growth occurs to the point that a smaller adolescent and adult with lower nutritional needs will develop. Subsequent research has shown long-term effects on offspring even from briefer and less-extreme mother–infant experiences.

LONG-TERM MATERNAL REGULATION OF DEVELOPMENT In clinical work and in attachment theory, the psychological construct of an enduring mental representation is generally used to denote an internal working model that is formed early in the infant’s developing mind, through his or her particular interaction with parents or consistent caretakers. This conceptual model helps to organize and explain how the nature of peoples’ later relationships and responses to stress seem to be shaped by their experiences as infants and children and even transmitted to their offspring in the next generation through particular patterns of mothering behavior expressive of the mother ’s mental representation of how mothers behave toward their infants. In research with a simple mammalian working model system, we found evidence of similar long-lasting and transgenerational effects that can be attributed to the processes of the maternal regulatory interactions described previously. For example, Sigurd Ackerman, Herbert Weiner, and I found that permanent maternal separation of juvenile rat pups early in the weaning period (early weaning) produced a greatly increased vulnerability to stress-produced gastric ulcer in adolescence and early adulthood (Skolnick, Ackerman, Hofer, & Weiner, 1980). To our great surprise, we found that this effect persisted in the next generation in the normally reared offspring of mothers that had been separated early as infants. We also found that, as adult mothers, early-weaned females spent less time and interacted less with their infants. However, a cross-fostering study revealed that this was not the result of the reduced maternal behaviors of the early weaned mothers. For if the pups of normally reared mothers were substituted for the

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early-weaned mother ’s own at birth, these cross-fostered pups were not vulnerable as adolescents. Instead, the pups born to early-weaned mothers but reared by normally reared mothers inherited the vulnerability. This suggested an unknown intrauterine or germ cell line transmission mechanism. In a different study on the development of hypertension in a strain of rat that had been selected over generations for high blood pressure (SHR), Michael Myers, Susan Brunelli, and I found that naturally occurring variation in 3 out of the 12 maternal behaviors we observed in the hypertensive and control strains (WKY) of inbred (genetically homogenous) rats did indeed appear to regulate the development of the physiological trait in the next generation: the levels of blood pressure in their offspring. Because the animals in each strain were genetically identical, and because the levels of maternal behaviors were significantly correlated with the magnitude of effect on adult offspring blood pressure both within and between strains, it seemed very likely that it was the variation in maternal behaviors acting on the pups that produced these long-term effects (Myers, Brunelli, Squire, Shindeldecker, & Hofer, 1989). The findings of these two studies led us to realize that both the hidden postnatal maternal regulators that we had discovered in our separation studies and a different class of early-weaning-induced prenatal or germ line influences could have long-term regulatory effects on later development, even into the next generation. In their implications for human development, they appeared to represent a level of biological developmental processes that underlie and coexist with the psychological processes inferred to mediate the lasting effects clinically observed between early relationships and their later mental representations in patients (Hofer, 1980, 2005). However, the biology of these effects was most unusual and difficult to explain in terms of the familiar processes studied in developmental psychobiology at the time. In addition, the evolutionary basis for such long-term developmental effects was unclear. Now, with a new understanding of the role of development in evolution, one can begin to make some sense of them (see Figure 2.8). The concept of developmental selection states that developmental processes have been selected, in part, to function as creators of potentially useful variation and to provide developmental mechanisms of inheritance necessary for selection of the most successful of these variations. The regulatory processes hidden within early mother–infant interactions have adaptive value for the infant within the unique environment of young mammals, but they also can function to regulate the early course of development. Because development consists of series of cascades of gene regulation patterns, an early diversion is capable of setting the pattern of downstream

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Long-Term Maternal Regulation of Development

Figure 2.8 Overlapping life cycles provide a template for inheritance and a matrix for creating variation throughout early development in humans and other mammals. Note. In a single generation, the origin of germ cells (lower left) takes place during the early embryonic period of the mother while she is in the grandmother ’s womb. A number of years later, these germ cells unite at

regulation onto different paths. This can extend the early effects of variations in maternal behavior on infants into adolescence and even adulthood. Moreover, because some of the neural substrates for adult behavior, such as maternal behavior, are present even in infancy, maternal regulation that affects early gene expression in these neural precursors can likewise be modified, with the result that when the systems mature, their patterns of function will be different. In the case of maternal behavior, this inherited environment can result in transmission (inheritance) of an early experience effect from one generation to the next. As evolution continues, if an environmental or social change persists over several generations and can be transmitted across generations by maternally driven developmental processes, then this developmental variation will gradually become genetically modified by repeated selection. Variants in the structure of genes (alleles) responsible for expression of the transcription factors that activate the cascades of gene expression patterns will be selected to the extent that survival and reproductive success is enhanced in these variant individuals. In this way, new and even more adaptive developmental paths will be gradually created over generations because of this “genetic accommodation”

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conception, and development continues within the mother ’s womb and throughout a long period of postnatal proximity and interactions with the mother, grandparents, and other caretakers. Development has been shown to be affected by such extended influences as the age of the father prior to conception and the proximity of grandparents.

of developmentally created novelties. Often an environmental or maternal behavior variable will be maintained by selection as a “trigger” for one or another developmental path, creating alternative phenotypes to be expressed by a singe genotype, greatly enhancing the range of adaptability of the evolving species. The plausibility of this developmental–evolutionary scenario is supported by the widespread existence of alternative developmental phenotypes (reviewed in WestEberhard, 2003) in organisms ranging from phage viruses to humans. In our experiments described above, we revealed the existence of such alternative developmental paths. In the early weaning experiments, we found that the susceptibility to gastric erosions was greatly increased in adolescent and young adults, but older adults were actually less vulnerable than normally reared animals. The whole life trajectory of the trait had been shifted by the social/ environmental trigger of early weaning. Physiologically, the response to early weaning may have been adaptive. Instead of sleeping throughout most of the 24-hr immobilization stress used to induce gastric ulcer as the normally weaned animals did, the early-weaned juveniles remained awake. This altered response could prevent them from

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being surprised by predators in the absence of protection by their mother. The physiological price paid was the risk of gastric ulceration if immobilization was prolonged. Early weaning seems to have also produced long-term changes that altered the uterine environment mothers provided to their young (possibly a hormonal or epigenetic change) that induced the alternative developmental phenotype in their fetuses, through a different mechanism, as a “predictive” response pre-adapting offspring for the expected environment in the next generation. Here, as in the section on maternal regulation of early development, the mother is revealed as a potential transducer of environmental change effects on future generations. Changes in the environment have long been demonstrated to affect maternal behavior, and it is highly adaptive if their young can begin to adapt to changed conditions early in their development (enabling them to become effectively preadapted as adults). Such preadaptations have been studied for several decades by ecologists and evolutionary biologists and are referred to simply as maternal effects (Mousseau & Fox, 1998). A well-known example in insects is that the location in which the female lays her eggs differs according to the season. Females lay their eggs in warmer locations in response to changes in the light in the fall. This reduces the time required before hatching and thus increases survival of the young before the onset of winter. Epigenetic Mechanisms of Long-Term Maternal Regulation as Preadaptation The work of Michael Meaney and his colleagues over the past decade has greatly increased understanding of the biological processes at work in these lasting effects of early relationships, as described in Cameron et al. (2005). These researchers discovered that normal variation among mothers in a colony in the same maternal behaviors implicated in our earlier studies (Myers et al, 1989) of offspring blood pressure development (the level of maternal licking and grooming of pups) systematically modified (regulated) the development of a number of different traits in adult offspring (i.e., adrenocortical stress response, behavioral fear response, measures of learning, and sexual and maternal behavior in adult offspring). Furthermore, these different phenotypes can be produced in offspring as a result of adverse environmental events occurring in the parental generation, such as repeated immobilization of pregnant females. This stress was shown to affect maternal behavior, resulting in physiological and behavioral changes in offspring that preadapted them to more challenging environments (Champagne & Meaney, 2006). The cellular/molecular mechanisms mediating these complex transgenerational effects of variations in maternal

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care have been analyzed in detail by the Meaney group (Weaver et al., 2007). These studies have revealed a novel layer of long-term epigenetic modification of gene expression in offspring caused by maternal care differences acting on the molecular configuration of the chromatin matrix surrounding certain specific genes in the offspring, permanently altering their expression levels and causing longterm effects. Revealing the Structure of Alternative Developmental Pathways In all of these studies just described, alternative developmental pathways appear to be potentially available within the genetic potential of a given strain of rat. One or another of these can be expressed, depending on certain specific social/environmental eliciting or triggering experiences. It occurred to Susan Brunelli and me that such alternative developmental paths or trajectories might also be revealed by repeated selection for high levels of a particular trait in infancy. If the trait were part of a genetically organized and extended developmental pathway, the later stages of that pathway, and any associated traits, should be revealed in adults of the selected line after a number of generations of repeated selection based on the level of the infantile trait. The trait we chose was the infant separation call described previously (see “The Separation Cry”), and we picked a differential selection procedure for the laboratory that was based on certain evolutionary considerations. It is known that sensory and perceptual adaptations to infant USV have evolved in female rodents, for example, an auditory frequency response threshold tuned to the exact frequency of infant USV (45 kHz) that enables the mother ’s sensitive and specific search, retrieval, and caregiving responses (Ehret, 1992). But the infant’s isolation call can also be used by predators to locate the infant. Not surprisingly, predator odors dramatically suppress USV in isolated pups, a specific fear response (Takahashi, 1992). The evolution of the infant separation cry has involved what is known as an evolutionary trade-off, a ratio of risk to benefit that is thought to have shaped many behaviors. In this case, the theory predicts that in environments with many predators, infants that show less separation-induced vocalization will gradually increase in the population. However, when nest disruption and scattering of pups occurs frequently (e.g., through flooding) and fewer predators exist, high rates of isolation calling would be advantageous. To explore some of these hypothetical evolutionary processes and the role of development in them, we have been conducting an experimental model of evolution in the laboratory. We selectively bred adult rats that had shown relatively high or relatively low rates of USV responses to

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27

separation as 10-day-old infants (Brunelli & Hofer, 2007). We found that in as few as five generations, two distinct lines emerged that differed widely on this infantile trait. Cross-fostering showed no evidence that being reared from birth by a mother from the other line changed the level of isolation calling (Brunelli, Vinocur, Soo-Hoo, & Hofer, 1997). Clearly, the selected trait has a strong genetic basis. But because there have been no systematic studies of selective breeding for an infant behavior trait (that we have been able to discover), we did not know how repeated selection might affect the developmental course of separation calling rates in descendents. Furthermore, we wondered whether other traits related to vocalization or to an underlying early anxiety state might also be affected through their genetic, physiological, or behavioral links to the systems controlling the infants’ vocal response to separation. We found that infants’ calling during isolation was elevated in high-line pups over a randomly bred control line starting as early as the response develops at 3 days of age, whereas low-line pups had already decreased their calling rates below controls at this early age (Hofer, Brunelli, & Shair, 2001). The greatest difference between high and low lines was at the age of repeated selection, 10 days postnatal. Response differences were much less evident at 14 days, and the three lines converged as the response ceased to occur in all weanling pups at 18 to 20 days postnatal. Thus, selection resulted in high-line pups showing a marked increase in the already high rates of newborn pups and maintaining this level up to and including the age of selection. However, the low-line pups showed the opposite, a more rapid decline than normal from 3 to 10 days of age. In short, selection at 10 days of age appeared to be acting on the whole developmental trajectory of the vocal response to separation, shifting it in time: either delaying

or hastening the normal gradual decline in isolation calling with age. In more recent studies, as summarized in Figure 2.9, Brunelli found a number of other behaviors and physiological responses at different ages in the high and low lines that had been altered by selection at 10 days of age (Brunelli & Hofer, 2007). Those differences appeared to form two coherent groups of traits. In high-line juveniles tested in isolation at 18 days (when isolation calling no longer occurs), both defecation/urination and (sympathetically mediated) heart rate acceleration were greater than in controls. As adolescents, rough-and-tumble play behavior and the short high-frequency vocalizations that accompany these interactions were reduced in the high-line compared to randomly bred controls in the first few play bouts. Highline adults were significantly slower to emerge into an open test arena and avoided the center region more completely than the low lines. In addition, high-line adults showed a much more passive response to the Porsolt swim test, a pattern associated with depression-like states in rodents. The low-line rats, as they developed into juveniles, showed the greatest heart rate acceleration of the three lines during isolation testing and a much-delayed return to baseline due to major vagal withdrawal. Low-line adolescents were deficient on all play behaviors on all days of testing and emitted the fewest play calls. As adults, the lowline animals were quicker to emerge into the open area, explored its center more than the highs, and were more active in the swim test. When confronted with an unfamiliar male, 70% of low-line males engaged in aggressive behavior compared to 30% of randomly bred controls. These groups of traits suggest a characterization of the high line as anxious and passive, whereas the lows were exploratory, active, and aggressive. Apparently, selection

Figure 2.9 Summary of developmental effects created in two lines of rats by repeated selection, over more than 25 generations, for an infant anxiety trait: either high or low levels of isolationinduced ultrasonic vocalization (USV).

are widely used as representing close equivalents in animals to these two human affective states. Plus signs (⫹) denote increased levels of behaviors, and minus signs (–) denote decreased levels.

Note.“Depression-like” and “anxiety-like” refer to behavioral responses observed in tests that have been validated pharmacologically and that

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for high and low rates of separation calling in infancy, as would occur during evolution under different ecological conditions, selects for a lifelong developmental path involving associated traits. Two alternative phenotypes, or temperaments (as illustrated in Figure 2.9; see also Volume 1, Chapter 15), were created by repeated selection acting at a single time point early in development, suggesting that a genetic potential exists for more than one organized developmental path. A pup could be set on one or another path when alleles for associated traits that enhanced one or another level of the infantile separation calling trait are gradually accumulated by repeated selection. These results resemble the studies from the Meaney group (reviewed in Cameron et al., 2005) in which early environmental change and different maternal behavior patterns had long-term effects on specific patterns of associated traits in adult offspring. Thus, the developmental structures for two or more behavioral phenotypes with adaptive potential are present and can be realized either through selection over 15 or 20 generations, or in a more rapid but shorter-lived transgenerational response to certain types of stress from the parental to the offspring generations.

SUMMARY Scientists’ understanding of development has reached a new phase with the rapid advances in molecular genetics that for the first time allow them to see in detail how development takes place through changes in gene expression over time. These advances have led to an integration of evolutionary and developmental biologies (evo-devo) based in large part on the insight that novel forms are generated in evolution as much through variation in the regulation of genes during development as by changes in gene frequency in populations through natural selection. It is generally agreed that there is still no general theory of development comparable to evolutionary theory (Bateson et al., 2007), but scientists do have a new understanding of the relationship between those two great historical processes of biology. Multicellular development has its origin in the Cambrian explosion of major animal groups (phyla) appearing suddenly in the fossil record 500 million years ago. During and after this major transition in evolution, developmental processes were selected that promoted the construction of larger and more complex organisms capable of inhabiting and exploiting new ecological niches. But these construction processes were also shaped by selection for their capacity to create ontogenetic adaptations that enabled survival of early developmental forms in their own unique and transient environments. Furthermore, developmental

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processes that enabled novel and potentially useful variations in developmental paths that enabled the re-creation of successful paths in the next generation were also selected for their capacity to facilitate the evolution of successful multicellular forms. Identifying these evolutionary selection pressures for construction, ontogenetic adaptation, variation, and inheritance (developmental selection) provides an understanding of the functions being carried out by developmental processes, similar to the understanding gained through identifying the evolutionary functions of stress responses or of social behaviors. In this chapter, I have used the principles of developmental selection in discussing what has been learned recently about the development of early attachment: its prenatal origins, approach and contact maintenance systems, learning of specific maternal discrimination and preference, novel mechanisms for separation responses, and hidden regulators of early development. Finally, I have described the evolution and development of long-term effects of early attachment and the resulting role of mother–infant interaction in the shaping of alternative pathways of development and in the formation of temperament. Another theme of this chapter has been the concept of levels of organization, a way of thinking that is extremely useful in trying to understand how events and processes that are studied by neuroscientists in cells and in brain circuits are related to the observed behavior and inner experiences studied by psychologists. I have tried to illustrate how psychological constructs such as the bond, emotional responses to separation, mental representations and long-term developmental effects deriving from parent–infant interactions can be better understood by learning more about the component neurobiological processes that underlie and have given rise to the psychological constructs and theory. Both the evolutionary developmental approach and the research revealing the component developmental processes of attachment are in the early stages of their own development; there is a great deal still to learn about them. Perhaps the most important gap in this understanding of development is in the area of the self-organizing processes that hold together and guide the seemingly endless series of cascades and networks of gene regulation that underlie development at its deepest level. This “construction” component of development not only embodies as-yet unknown principles of self-organization but is clearly supported, directed, and organized also from the “outside,” by the environment inherited from the previous generation, in ways scientists are only beginning to understand. This environmental matrix begins with the early life of germ cells developing within the parents when they are still embryos, and with the signal proteins and transcription

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References 29

factors formed in the maternal egg prior to fertilization. It continues through the long-term maternal effects exerted by the symbiotic relationship of the embryonic, fetal, and infant stages of development all the way to the “given” properties of the outside environment within which the young must grow up. In addition to these general areas in which scientists know far too little, specific new biological processes are being discovered that experts never knew existed, such as the controls over gene regulation maintained within the genome itself (e.g., the intricate regulation of DNA transcription and translation by the protein “skeleton” of chromatin). Lastly, many mechanisms for variation have evolved within all of these developmental processes as a result of their having enhanced the opportunities for selection. These processes for facilitating variation are just beginning to be studied. The perspective I see is that there is no end of exciting opportunities for research, that the approaches for investigating developmental neuroscience suggested in this chapter are only a beginning, and that these approaches will soon be modified in interesting ways, perhaps by some of the readers of this chapter.

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Chapter 3

Comparative Cognition and Neuroscience CHARLES T. SNOWDON AND KATHERINE A. CRONIN

all, insects, fish, birds, and mammals. Presumably, in environmental conditions where color vision would benefit the reproductive success of individuals, individuals possessing mutations leading to color vision would be more likely to survive and would leave more surviving offspring with the color vision mutation. Many cognitive and behavioral phenomena appear as examples of convergent evolution: families as the basic reproductive unit in many birds, marmosets, tamarins, titi monkeys, and humans; enhanced spatial memory in seed-caching birds and rodents; vocal learning in birds, marine mammals, and humans, and so forth. In this chapter, we first discuss the methods and cautions of comparative studies and then focus on a selection of cognitive phenomena for which there are good comparative data and at least some information on the neural bases. We include social processes among the phenomena that we

Developmental processes are typically viewed from the perspective of an individual’s history, but these processes can also be viewed from the broader historical context of evolution by natural selection. The study of the evolution of cognition and its neural correlates involves the use of a comparative method: the study of multiple species that differ in one factor that may influence the selective pressures on the trait of interest. Factors investigated may include phylogeny (e.g., birds vs. mammals, or apes vs. monkeys), social structure or social organization (e.g., multimale, multifemale groups vs. families), mating system (e.g., monogamy vs. polygamy), group size (e.g., pair with offspring vs. many individuals), foraging behavior (e.g., generalist vs. specialist, or clumped vs. distributed food resources), or ranging behavior (e.g., territorial vs. migratory). Thus, someone interested in the evolutionary origins of language might compare the abilities of humans with those of other great apes. Someone interested in spatial memory might compare two closely related species, one that stores food in many locations and recovers the food later and another that does not store food. The typical view of evolutionary processes is that they diverge. As new species form and become reproductively isolated, they diverge from one another. From this perspective, those species that have shared ancestry and are similar on most variables but differ on some critical one (e.g., monogamous vs. polygamous mating systems in some closely related rodent species) are the comparisons of greatest interest. However, an additional view of evolution is one of convergence (see Figure 3.1). Two quite distantly related species may face similar ecological problems and may independently reach similar solutions. One example is color vision, which has independently appeared in some, but not

Trait variation B

C

D

E

Time

A

Figure 3.1 Convergent and divergent evolution. Letters represent species on this hypothetical phylogenetic tree. The less the horizontal distance between species, the more similar the species are on the trait of interest. Species D and species E are more closely related to each other than are species B and species C. However, species D and species E are less similar on the trait of interest and are an example of divergent evolution, whereas species B and species C are more similar on the trait of interest and are an example of convergent evolution.

We thank Bridget Pieper and Carla Boe-Nesbit for critical comments on the manuscript and Andrew Fox for critical feedback on neural mechanisms of cooperation. Our research was supported by U.S. Public Health Service Grant MH035215 and a Hilldale Professorship from the University of Wisconsin, Madison. KAC was supported by a National Science Foundation Graduate Fellowship. 32

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c03.indd 32

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Methods and Cautions 33

call cognition. Thus, we review cooperation and prosocial behavior, social learning, spatial memory, and pair bonding. An obvious omission is comparative work on bird song learning and its neural mechanisms, which is covered in Chapter 45.

METHODS AND CAUTIONS Choosing Species for Comparison The selection of appropriate species for comparison depends on the nature of the question being addressed. However, often species are chosen on the basis of phylogenetic similarity alone. For example, chimpanzees (Pan troglodytes) and bonobos (Pan paniscus) are the closest relatives of humans, but these apes diverged from humans more than 4.5 million years ago. Behaviorally, chimpanzees differ significantly from bonobos by being more competitive and aggressive, and both species differ from humans in significant ways. Nonetheless, because both species have relatively large brains that are similar in form to those of humans, these species may be good models for comparisons with humans involving some cognitive abilities. However, they may not be good models for comparisons with some human social and emotional processes because the social environments of these species differ greatly from our own. Comparative models might also be chosen from species that are quite different but share some interesting commonality. For example, human fathers often appear to display competence in caring for and bonding with their infants, something rarely, if ever, observed in other great apes. However, fathers in several New World primate species and in some rodents and many birds exhibit spontaneous care of infants and form long-lasting bonds, making these species potentially better candidates than great apes and Old World primates for studying paternal behavior (Snowdon & Ziegler, 2007). As we show here, species with extensive coordination of infant care between different group members are more likely to exhibit social learning (Coussi-Korbel & Fragaszy, 1995) and cooperative behavior (Cronin, Kurian, & Snowdon, 2005). Ecological factors affecting foraging behavior can also lead to differences in rapidity of social learning (Lefebvre & Palameta, 1988). As we also show, social factors or mating systems can lead closely related species, such as monogamous versus polygynous mice and voles, to have very different behavior, hormones, and neural organizations than we would predict from phylogeny alone. Important comparisons may also be made within species. Call and Tomasello (1996) and Thompson, Oden, and Boysen (1997) have shown differences in performance on social learning and second-order analogical reasoning tasks between so-called enculturated chimpanzees that have had extensive

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human interactions during development compared with chimpanzees that have been reared naturally by their own species. It is not clear what aspects of enculturation are important for high-level cognitive function, but the ability to interact comfortably with the humans who are testing the animals may lead these chimps to exhibit better performance than those with less of a history of interaction with humans. Differences within a single species can also inform researchers of developmental influences on cognition. Seasonal differences may also produce behavioral and neural variation within a species or even an individual. Male birds sing under the influence of testosterone stimulated by increasing day length. Furthermore, changes in the size and neural complexity of brain areas track seasonal differences, with consequent changes in rates of singing or song structure and complexity (Alger & Riters, 2006; Nottebohm, 1981; G. T. Smith, Brenowitz, Beecher, & Wingfield, 1997; see Chapter 4 for more details). No Scala Naturae Hodos and Campbell (1969) argued against the concept of scala naturae, a popular misconception that behavioral changes follow a natural phylogenetic scale from earlier evolved species exhibiting simpler behavior to more complex and more recently evolved species necessarily exhibiting more complex behavior. You still can find psychology texts talking about invertebrates having less complex behavior than fish, which in turn are simpler than birds, which are simpler than mammals. The reality of evolution is much messier than a simple linear hierarchy implies. Honeybees have a complex communication system to indicate the location of nectar (Von Frisch, 1950). Salmon remember the odors of their home streams over several years of ocean living and can return to their natal streams to spawn (Hasler, 1966). Clark’s nutcrackers (Nucifraga columbiana) of the U.S. Southwest can store up to 30,000 seeds each fall and can remember the location of enough of these seeds to survive the winter (Balda & Kamil, 1992). Cotton-top tamarins (Saguinus oedipus; Cronin & Snowdon, 2008; Hauser, Chen, Chen, & Chuang, 2003) and common marmosets (Callithrix jacchus; Burkart, Fehr, Efferson, & van Schaik, 2007) spontaneously display cooperative and reciprocal behaviors that a human parent would admire in his or her children, yet chimpanzees display little evidence of similar positive social behavior (Jensen, Hare, Call, & Tomasello, 2006; Silk et al., 2005; Vonk et al., 2008; see following discussion). Vonk and Povinelli (2006) argued that evolution creates no presumption about the phylogenetic distribution of psychological systems among closely related species, and yet, even among presumably sophisticated primatologists who might be expected to have a more subtle appreciation of the

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processes of evolution, we often encounter the idea that primates are somehow more special than other mammals and that apes have cognitive skills surpassing all others. This leads to the frequent dismissal of nonprimate species with complex social structures such as hyenas, wolves, and other social carnivores as well as species more difficult to study, such as marine mammals. In these species one can find evidence of group-specific differences in communication and behavior that might be called culture if seen in apes, as well as coordination of complex social behavior, such as teaching in meerkats (Thornton & McAuliffe, 2006) and communal hunting in hyenas (Drea & Frank, 2003). There is often evidence of clear relationships between aspects of body size, brain size, and cognition. The patterns of allometric relationships may often illuminate interesting exceptions from a strict phylogenetic progression. For example, Figure 3.2 plots body size and encephalization quotient (EQ) for several primate species grouped by phylogeny. There is little variation across New World monkeys, Old World monkeys, apes, and extinct hominoids in brain size as a proportion of body size. However, the two genera with the highest EQs are squirrel monkeys and capuchin monkeys, both New World primates, and one of the genera with the lowest EQ is the gorilla. Note also that modern humans have a significantly greater EQ than any other primate species. A modern reincarnation of the scala naturae is research that uses allometry of brain size to explain the evolution of language. Dunbar (reviewed in Dunbar, 2003) has shown that among terrestrial Old World primates there is a correlation between group size and neocortex size. This suggests that the cognitive demands of social complexity were driving forces in the evolution of brain size (the social

brain hypothesis). Dunbar ’s measure of social complexity was the number of grooming relationships that are possible within a group, as grooming is an essential component of maintaining social relationships in many primates. As group size increases linearly, the number of potential dyadic and higher order relationships increases exponentially. According to Dunbar, typical human groups average 150 in a social cohort, too large to allow individual relationships to be maintained by grooming. Thus, humans have evolved language, using gossip as a proxy for social grooming. In fact, there does appear to be a relationship between typical group size and neocortex volume among the species selected. But is the description of the relationship between group size, social complexity, and brain size adequate? Figure 3.3A shows New World primates that diverged from the ape lineage earlier than Old World monkeys. The monkeys with the largest brains and the largest groups (muriquis, Brachyteles spp.) almost never groom one another (Strier, 1997), whereas the primates with brains with less neocortical volume and the smallest group sizes (marmosets and tamarins) groom extensively. One field study of common marmosets reported adult pairs grooming each other more than 20% of the day (Lazaro-Perea, de Fatima Arruda, & Snowdon, 2004). Figure 3.3B shows that for both New World primates and apes there is a linear relationship between EQ and group size; so the basic relationship between group size and brain size still holds, but not for grooming time. These findings argue against a clear relationship between group size, grooming, social complexity, and the evolution of the neocortex. One lesson from this is that limiting the selection and range of species studied can potentially lead to different

8 New World primates

Encephalization Quotient

7

Old World primates Apes

6

Extinct hominoids 5

Modern humans

4 3 2 1 0 0.1

1

10 Body Weight

Figure 3.2 Encephalization quotient (EQ) plotted against log body weight for several genera of primates. Note the variation within each group of primates (Saimiri and Cebus as New World primates have higher EQ’s than all other

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100

1000

nonhuman primates. Gorillas, furthest to the right, have a small EQ relative to all great apes and most monkeys. Only modern humans have an EQ that is not in the range of other species. (Drawn from data in Aiello & Dean, 1990)

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Methods and Cautions 35 (A)

25

Time Grooming (%)

20

15

10

5

0 0

(B)

20

40

60 Group Size

80

60 Group Size

80

100

120

3.5

Encephalization Quotient

3 2.5 2 1.5 1 0.5 0 0

20

40

conclusions. Another lesson is that brain size is not necessarily synonymous with complexity. As a nonprimate corollary of this, G. T. Smith et al. (1997) found that although the sizes of brain areas related to song production changed with the seasons in song sparrows, some of the change was due to changes in average neuron size and neuron density rather than in the number of neurons. Complexity must be carefully defined, and we should not assume that differences in size must necessarily be related to differences in complexity. Anthropomorphism and Anthropocentrism Many of the problems with the scala naturae view among scientists otherwise quite sophisticated about natural selection and evolution may be due to both anthropomorphism (ascribing human traits to nonhuman animals) and anthropocentrism (seeing cognition in other species through the lens of human abilities). When species are more similar to humans, it may be difficult to avoid ascribing humanlike cognition to these species. Even when we try hard to

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100

120

Figure 3.3 The linear regression from New World primates is shown by a solid line, for Old World primates by a dotted line and for Apes by a dashed line. A: Grooming time as a function of group size in selected genera of New World primates, Old World primates and apes. Regression lines for Old World primates and apes have a positive slope, whereas New World primates show a negative relationship between grooming time and group size. (Data from Dunbar, 1991) B: Encephalization quotient as a function of group size in the same genera showing a positive relationship between brain size and group size for New World primates and apes and no clear trend for Old World primates. EQ data from Aiello and Dean (1990) and group size data from Dunbar (1991).

avoid being anthropomorphic, it is nearly impossible to avoid being anthropocentric. This has led to the idea that increasingly complex social life (as measured by mean group size) must be accompanied by increasingly complex cognitive and social skills (Barrett, Henzi, & Rendall, 2007). As mentioned, social group size may not be a good index of brain complexity, and many species far removed from humans have displayed complex cognition on one or more dimensions in the context of their environment. Furthermore, it may be a mistake to assume that all of human behavior is governed by complex cognitive processes. Many of us engage in automatic behaviors (routines in cooking, driving to work, semantically meaningless phrases used in greetings, etc.). We may be overrating our own cognitive ability simply because we are the species doing the rating. Cognitive complexity must be seen through the lens of the species being studied. None of this is to deny the very real abilities of humans but instead to urge scientists to be more modest when evaluating human abilities (and those of other great apes) compared with those of other species.

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Problem of Plasticity Comparing species on different aspects of cognition is one way to gain a deeper understanding of cognitive processes and cognitive evolution, yet animals, human and nonhuman, are notoriously plastic and are able to modify behavior. For example, ecological differences between captive and wild environments may lead to different behavior within the same species. Leavens, Hopkins, and Bard (2005) have shown that pointing behavior is observed readily in captive chimpanzees interacting with humans to obtain food, whereas pointing has never been observed in the wild. Unlike wild tamarins, captive-born cotton-top tamarins do not exhibit fear when exposed to snakes, a natural predator (Campbell & Snowdon, in press; Hayes & Snowdon, 1990). Captive-born tamarins responded with equal arousal to playbacks of calls of some natural predators and vegetarian howler monkeys, suggesting that initial responses to predator calls may be flexible and may be related to certain acoustic properties rather than innate predator recognition (Friant, Campbell, & Snowdon, 2008). Captive tamarins spontaneously produced alarm calls when fed a familiar food that had been adulterated with pepper, although they had never alarm called at that food previously (Snowdon & Boe, 2003). They also gave mobbing vocalizations toward a caretaker acting as though coming to catch a monkey, despite their lack of arousal toward natural predators (Campbell & Snowdon, 2007). These results on captive chimpanzees and tamarins suggest that captivity represents an ecological niche to which animals may adapt, leading to changes in behavioral contexts different from those in which their wild conspecifics would show similar behavior. Withinspecies comparisons of individuals living under different social or environmental conditions can identify the environmental (as opposed to phylogenetic and genetic) variables that can affect cognitive processing and behavioral expression. This contextual flexibility may paradoxically utilize simpler neural processes than those hypothesized for contextually inflexible cognitive processes. For example, with respect to communication between animals, Owren and Rendall (1997) have argued for flexibility in the contexts in which signals are used and how they are responded to by others. They offered a simple model of affective conditioning by which individuals learn the relationships between signals and affective state. As environments change, organisms can acquire new signal–meaning correspondences. Saffran, Aslin, and Newport (with human infants; 1996) and Hauser, Newport, and Aslin (with cotton-top tamarins; 2001) have demonstrated that human babies and monkeys can learn about statistical regularities in auditory input, a simple but rapid

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way in which organisms can acquire contingencies between sounds. Thus, an animal in a novel context (e.g., captivity) can quickly learn to associate alarm calls with caretakers or veterinarians rather than leopards and snakes, or can learn to eat foods different from those found in the wild and still give appropriate food calls. These basic processes of conditioning provide a general way to learn rapidly about associations of signals and events in the environment without reliance on a host of hypothetical modality and context-specific modules that have been shaped by evolution (Tooby & Cosmides, 1990). Successful adaptation is more likely to occur when organisms can respond rapidly to changes in environment than rely on hard-wired species-specific stimulus–response connections. Two striking examples of plasticity come from work on rodents. For the first, Marler and colleagues studied two species of mice (Table 3.1). The California mouse (Peromyscus californicus) is one of the few species known from field studies to be both socially and genetically monogamous (Ribble, 1991). Males defend their mates and play an important role in infant care, with infant survival being affected by fathers (Gubernick & Tefari, 2000). The white-footed mouse (P. leucopus) is a close relative but is polygamous. These mice do not form pair bonds, and individual males and females may mate with many others. Males provide little, if any, infant care and do not show territorial defense. Bester-Meredith and Marler (2001) successfully cross-fostered these mice and showed that California mice reared by white-footed mice exhibited reduced paternal care and reduced territorial defense behavior, becoming more similar to their foster species. Furthermore, the patterns of distribution of vasopressin (a neuropeptide hormone related to aggression) staining in the brains of cross-fostered California mice more closely approximated the distribution patterns seen in white-footed mice than those seen in their own species. Within California mice, sires that were retrieved less as young retrieved their pups less often when they became adults (Bester-Meredith & Marler, 2001). An experimental increase in the amount of retrievals experienced by California mice offspring led to decreased attack latencies in both males and females and greater vasopressin immunoreactivity in the dorsal bed nucleus of the stria terminalis (Frazier, Trainor, Cravens, Whitney, & Marler, 2006). Cross-fostering (CF) to another species can change not only behavior but also brain organization and brain function. The specific nature of the crossfostering experience that leads to behavioral and neural changes is fairly minimal. In the second example, the work of Meaney and collaborators has shown that variations in maternal licking and grooming in rats (Rattus norvegicus) are related to variations in stress reactivity, with greater amounts of licking

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Social Learning 37 TABLE 3.1 Effects of cross-fostering (CF) on behavior and arginine vasopressin (AVP) brain staining in mice. Species CrossFostered

Territorial Aggression

Neutral Arena Aggression

Paternal Care

AVPImmuno Staining

Whitefooted mouse

No effect

Control < CF

No effect

No effect

California mouse

Control ⬎ CF

No effect

Control ⬎ CF

Control ⬎ CF

Note. From “Paternal Behavior and Aggression: Endocrine Mechanisms and Non-Genomic Transmission of Behavior,” by C. A. Marler, J. K. Bester-Meredith, and B. C. Trainor, 2003, Advances in the Study of Behavior, 32, pp. 263–323. Adapted with permission. Elsevier Science 2003.

and grooming reducing stress reactivity of the pups when they are adults. Furthermore, when offspring become adults they show similar licking and grooming patterns as their mothers. Cross-fostering studies showed these effects to be nongenomic but purely a result of the early grooming and licking received (Francis, Diorio, Liu, & Meaney, 1999). High levels of maternal licking and grooming translated into differences in hippocampal glucocorticoid receptor messenger RNA expression (Liu et al., 1997) as well as increased synapse formation in the hippocampus, increased expression of N-methyl-D-aspartic acid receptors, increased cholinergic innervation of the hippocampus, and enhanced spatial learning and memory (Liu, Diorio, Day, Francis, & Meaney, 2000). Furthermore, high levels of early maternal care are associated with differences in responsiveness of oxytocin receptors to stimulation by estrogen (Champagne, Diorio, Sharma, & Meaney, 2001). That such small but significant differences in early rearing can have profound effects on behavior and brain function in several species should caution us about thinking about behavior as being determined solely by the direct effects of natural selection. Organisms are not static entities but respond flexibly and dynamically to their environments.

SOCIAL LEARNING Social learning (or socially mediated learning) has been studied in a wide range of species. Social learning occurs when the acquisition of behavior is influenced by the activities of other individuals, either directly or indirectly (Box, 1984). Much research has focused on the mechanisms that enable social learning, such as an observer ’s attention being drawn to a particular object as a result of another individual interacting with that object (stimulus enhancement;

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Spence, 1937), or the increased likelihood of performing a familiar motor action after seeing it performed by another individual (response facilitation; Byrne, 1994). More than seven mechanisms that enable social learning have been identified (Whiten & Ham, 1992). One mechanism, imitation, has received the most attention and has been categorized as the most challenging and unique form of social learning (Byrne, 1999). Some have argued that imitation requires theory of mind, or the ability to conceive of the intentions of others (Premack & Woodruff, 1978; see also Stone, this volume). Because theory of mind is widely thought to be an ability restricted to humans and potentially other great apes (Byrne, 1999), it has been argued that imitation is impossible for other taxa (Whiten & Ham, 1992). Miklosi (1999) reported that although some studies have provided data in support of imitative abilities in other species, others have quickly criticized and reinterpreted the results. The prevalence of imitative ability across taxa, and the cognitive mechanisms that underlie imitation, will likely be a hotly debated issue for some time, especially because some researchers with beliefs in scala naturae will cling to the uniqueness of imitation for humans and great apes. In order to evaluate the social learning capability or tendency of a group or species, researchers typically employ one of three methodologies. The first is simply exposing an individual trained to perform a skill to one or more naive individuals and then measuring whether the skill is expressed by the formerly naive individual(s). The second, transmission or diffusion chains, uses the same idea but measures whether the formerly naive individual subsequently transfers the acquired behavior to another naive individual (e.g., Galef & Allen, 1995). The third is the dual action task, in which there are two functionally equivalent methods available for solving a task. Individuals observe one or the other method and then are tested on whether their performance matches the method employed by the demonstrator (e.g., Humle & Snowdon, 2008). Social learning can permit behavioral calibration to the unpredictable properties of an environment with a degree of specificity not permitted by genetically coded information (Galef & Laland, 2005). However, it is a common misconception to assume that social learning is always more advantageous than individual learning, and indiscriminate copying of surrounding individuals is unlikely to be a stable strategy in all species (Boyd & Richerson, 1988; Laland, 2004). Theoretical models have demonstrated that social learning is more effective than individual learning when two conditions are met: (1) the cost of individually acquiring accurate information is high, and (2) knowledgeable individuals are present that have experienced the same environment (Boyd & Richerson, 1988). Therefore,

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we would expect that even closely related species might display different propensities to acquire information socially if they have evolved in different environments. Laland has expanded on these models to argue that natural selection should have favored heuristics dictating when individuals should acquire information socially, and from whom the information should be obtained. Decades ago, Klopfer (1959, 1961) proposed two hypotheses to account for interspecific variation in social learning. He reasoned that solitary species should demonstrate less social learning than gregarious species, and species with conservative foraging strategies should exhibit less social learning than species with opportunistic foraging styles. In fact, most attempts to predict or explain interspecific differences in social learning to date have been based on differences in either sociality or feeding ecology. Influence of Sociality on Social Learning Coussi-Korbel and Fragaszy (1995) presented a model relating social learning to social organization. They argued that behavioral coordination in time and/or space is common to all forms of social learning and that the extent to which behavioral coordination is expressed predicts social learning. An example of behavioral coordination in time (but not space) is a flock of birds feeding simultaneously while the nearest neighbors are at some distance. An example of behavioral coordination in space (but not time) is when an individual approaches a location where a conspecific was previously active but is no longer present. It is only behavioral coordination in both time and space that requires physical proximity between individuals. Differences in the amount of spatial proximity sought out and tolerated by conspecifics vary greatly across species and can often be related to larger social constructs such as dominance structures or mating systems. In despotic species, proximity between conspecifics, particularly near food or desirable items, will be infrequent. In more egalitarian species, however, proximity between conspecifics will be more common. Therefore, the social context in which an individual is immersed is likely to influence opportunities for, and subsequent expression of, social learning. However, it is not always the case that hierarchically organized species demonstrate less social learning. It appears that when the social hierarchy promotes close, constant monitoring of other individuals and their behavior, the probability for social learning may increase because of the increased attention paid to conspecifics. When the attention structure brought about by the hierarchy promotes social learning, it should do so in a heterogeneous fashion (Coussi-Korbel & Fragaszy, 1995). That is, not all

c03.indd Sec2:38

individuals will acquire the socially learned behavior, but rather those that are positioned relatively lower within the hierarchy will be more likely to attend to a demonstrator that is relatively more highly ranked and to subsequently express the modeled behavior. Some studies have made explicit attempts to relate social organization to social learning by performing interspecific comparisons of phylogenetically similar species. Cambefort (1981) compared the discovery and propagation of a feeding skill in chacma baboons (Papio ursinus) and vervet monkeys (Cercopithecus aethiops). The results were also compared with those of a previous study of mandrills (Mandrilli sphinx; Jouventin, Pasteur, & Cambefort, 1976). These three primate species belong to the same family, Cercopithecidae (Nowak, 1999), but exhibit different social organizations. The propagation of the socially learned feeding skill was closely related to the social structure or, more specifically, the attention structure of each species. Vervet monkeys exhibited the weakest hierarchy and group cohesion, and there was no social transmission of the novel foraging skill. In chacma baboons, which exhibited intermediate hierarchy strength, group cohesion, and attention to social partners, there was weak transmission of the novel foraging skill. Finally, in mandrills, which demonstrated the strongest hierarchy, strongest group cohesion, and most attention to social partners, there was fast social propagation of the novel foraging skill. Therefore, the pattern of transmission of the novel behavior mapped onto the hierarchical organization of the group. Another study compared the social transmission of flavor preferences in two hamster species—the golden hamster (Mesocricetus auratus), which is solitary, and the dwarf hamster (Phodopus campbelli), which is moderately social (Lupfer, Frieman, & Coonfield, 2003). When given a choice between a flavor chosen by a demonstrator and another flavor, dwarf hamsters preferred the demonstrator ’s flavor, whereas golden hamsters preferred the demonstrator ’s flavor only if the demonstrator was their mother. In the wild, adult dwarf hamsters interact with one another to share burrows and raise pups. In contrast, golden hamsters rarely interact with one another outside of the mother–pup relationship. Therefore, the degree of social learning expressed may have been predicted by the social organization of the species. A similar finding was reported when the more social pinyon jay (Gymnorhinus cyanocephalus) was compared to the less social Clark’s nutcracker. The individual and social learning abilities of the two species were compared, and the results indicated that pinyon jays learned faster socially than they did individually, whereas the nutcrackers’ performance was not enhanced in the social learning condition (Templeton, Kamil, & Balda, 1999).

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Social Learning 39

Whereas interspecies comparisons provide insights about the effect of the social organization of a species on social learning, intraspecies comparisons allow for investigation of the effects of social relationships within a species on social learning. Nicol and Pope (1994) found that the social transmission of a behavior (keypecking) in flocks of laying hens (Gallus gallus) was influenced by the hierarchy of the group and that social learning was greatest when the demonstrator was a dominant individual. A similar effect of social relationships was found in a study of cooperatively breeding common marmosets. Dyads were presented with a task requiring both individuals to act, whereby one manipulated an apparatus to bring food into the reach of the second individual, who could then retrieve the food. Dyads in which the more dominant individual assumed the role of retriever were more successful at solving the coproduction task. However, in the successful dyads, dominant individuals did not consume more rewards than subordinates, even though they had more direct access to the food rewards (Werdenich & Huber, 2002). Schwab, Bugnyar, Schloegl, and Kotrschal (2008) demonstrated that affiliative relationships among kin enhanced the performance of common ravens (Corvus corax) in a social learning task. Drea and Wallen (1999) reported intriguing results indicating that subordinate rhesus macaques (Macaca mulatta) performed less well on learning tasks when in the presence of dominant individuals, even though they had learned the information equally well. Drea and Wallen (1999) were not investigating social learning specifically, but their findings, combined with those of other studies on the effects of social relationships on performance, indicate that the identity of the demonstrator, as well as by-standers in the experimental setup, likely influences the degree of social learning expressed. Intraspecies comparisons can also elucidate developmental differences in social learning. Dependence on social learning may vary in predictable ways throughout the lifetime of an individual (Galef & Laland, 2005). As mentioned previously, there are likely fewer opportunities for social learning in a species that is rarely in proximity with conspecifics, but during periods of offspring dependence proximity will be more frequent and social learning opportunities may be quite regular. Black rats (Rattus rattus) learn socially to open pinecones to obtain seeds; however, it is only the pups, not adults, that are able to acquire the technique by observing experienced rats (Aisner & Terkel, 1992). Across many studies and species, it seems that juveniles are more likely than adults to incorporate new actions into their behavioral repertoire (e.g., Goodall, 1986; InoueNakamura & Matsuzawa, 1997; Kawai, 1965). Chimpanzees are generally adept at social learning both in the wild (reviewed in Matsuzawa, 2001) and in

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captivity (reviewed in Whiten, Horner, & Litchfield, 2004). Chimpanzee societies exhibit a strict dominance hierarchy, with the most tolerant and longest lasting relationships existing between mother and offspring. An intraspecific comparison of tool use acquisition by young chimpanzees in Gombe National Park, Tanzania, indicated a striking sex difference in the social acquisition of the skill. Females began using tools for termite fishing at a younger age than males, although there was no difference in the behavior of the mother (the model) toward males and females. Young females spent more time watching their mothers use the tools, whereas males spent more time playing at the termite mound. Therefore, the attention paid by the juveniles was an influential factor in determining the onset of the socially learned skill (Lonsdorf, Eberly, & Pusey, 2004). Again, the direction of attention and the close social relationship appears crucial for predicting the occurrence of social learning. Influence of Feeding Ecology on Social Learning Predictions about the prevalence of social learning across species have also been made on the basis of feeding ecology. Specifically, species with opportunistic or generalist lifestyles, in which individuals are exposed to more environmental variation, should be more likely to demonstrate social learning than species that are conservative or specialists (Johnston, 1982; Klopfer, 1961). This hypothesis rests on the reasoning that social learning allows an individual to modify its behavior to the current environment more efficiently or quickly than would be possible by either individual learning or genetically determined behavior. In this sense, social learning is an adaptive specialization shaped by natural selection. The influence of feeding ecology on social learning has been explored experimentally. Klopfer (1961) demonstrated that greenfinches (Chloris chloris) learned a food discrimination task less well in pairs than individually, as opposed to great tits (Parus major), which did not suffer a learning decrement in pairs. He speculated that a failure to learn socially about novel foods would only fail to be maladaptive in species that display conservative feeding habits, such as the great tit. Dolman, Templeton, and Lefebvre (1996) compared two populations of Barbados Zenaida doves (Zenaida aurita) with different foraging styles. One population consistently exhibits conspecific aggression while foraging, whereas the other forages in flocks without aggression. The aggressively foraging species performed better on a social learning task that employed a heterospecific demonstrator, whereas the nonaggressive species performed better with a conspecific demonstrator. In this study, the type of feeding environment appeared to predict

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the pattern of social learning expressed, as the population that typically interferes with conspecifics to compete for food was unable to acquire a socially learned skill from a conspecific. The prevalence of social learning throughout the animal kingdom seems to be best explained by a combination of social, developmental, and ecological factors. Phylogenetic predictions alone appear to do a relatively poor job of explaining interspecific variation in social learning, especially if one considers that comparisons across populations of a single species often lead to as much variation as between-species comparisons. Social Learning and the Brain It is unlikely that the neuronal bases that underlie social learning differ dramatically from mechanisms known to underlie other forms of learning (for a review of the neuronal basis of learning, see Chapter 26). However, an additional pattern of neuronal activation may be unique to social learning and may not be involved in asocial learning such as classical or operant conditioning or trial-and-error learning. Mirror neurons have been identified in macaques (Macaca ssp.) in the ventral premotor and rostral inferior parietal cortex (see Figure 8.11 in Chapter 8 for the approximate location). The defining characteristic of mirror neurons is that they fire both when the animal performs an object-oriented action with its hand or mouth, and when the animal observes another individual (human or conspecific) performing the same motor action (di Pellegrino, Fadiga, Fogassi, Gallese, & Rizzolatti, 1992; Gallese, Fadiga, Fogassi, & Rizzolatti, 1996; Rizzolatti, Fadiga, Gallese, & Fogassi, 1996). A leading hypothesis regarding the function of the mirror neuron system posits that mirror neurons are the basis of action understanding (Rizzolatti, Fogassi, & Gallese, 2001). Indeed, accumulating evidence suggests that the understanding of the meaning of actions determines the discharge of mirror neurons, rather than observations of the actions themselves. Umiltà et al. (2001) demonstrated that the majority of mirror neurons of rhesus monkeys respond during the observation of partially hidden actions, when full visual information about the action is not necessary to recognize the goal. The mirror neuron system also responds to the sound of a goal-related action, even when the monkey cannot see the action (Keysers et al., 2003; Kohler et al., 2002). These observations provide support for the interpretation that the mirror neuron system recognizes actions performed by other individuals and matches these actions on neurons that code the same action, providing the observer with the motoric perspective of the actor. It is through this interpretation that the mirror neuron

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system has garnered the attention of those interested in social learning. A growing amount of information available from EEG, transcranial magnetic stimulation, functional magnetic resonance imaging (fMRI), and positron emission tomography (PET) studies indirectly supports the presence of a mirror neuron system in humans (reviewed in Rizzolatti & Craighero, 2004). However, data suggest that there are some key differences between the mirror neuron systems of macaques and humans. For example, the mirror neuron system of humans, but not rhesus monkeys, responds to intransitive, meaningless movements. Perhaps this difference in sensitivity of the mirror neuron system sheds some light on the different imitative tendencies of humans and monkeys: Human children will “over imitate” (Horner & Whiten, 2005), copying movements that are irrelevant to accomplishing the task. (For a more detailed review of the mirror neuron system, see Chapter 16.) It is tempting to speculate that the mirror neuron system underlies social learning, given that the system allows for matching of the observation of motor actions with their execution. However, more data are needed on the involvement of motor neurons during the observation and acquisition of socially learned skills to understand whether mirror neurons are central to social learning processes. Furthermore, given that there are fundamental differences between macaques and humans in the properties of the mirror neuron system, data from additional species, ideally species that vary in their expression of social learning, will also be necessary to elucidate the role of the mirror neuron system in social learning.

SPATIAL MEMORY Spatial memory can be very important to help animals navigate through their environments to find mates or food. Because closely related species differ in the degree to which they rely on spatial memory, the comparative approach has been extremely powerful for understanding the neural mechanisms that underlie these behavioral differences. Foraging and food-caching behavior vary between species in both corvids (crows, ravens, jays) as well as parids (chickadees, tits, titmice). Some species must store food each fall and remember the locations of storage sites in order to find food over the winter. The Clark’s nutcracker from Arizona has been estimated to store up to 30,000 pine nuts each fall and appears to remember their locations even when the ground cover is transformed by snow (Balda & Kamil, 1992). Because other species within the same family do not need to store food over the winter, some have predicted that the food-caching species will show greater

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Spatial Memory 41

Brain Mechanisms and Spatial Memory The hippocampus is known as a primary region involved in both spatial memory and in transforming short-term to longterm memories. Several studies have shown differences in hippocampal volume, or the structure of neurons within the hippocampus, as a function of ecological differences. Birds from 3 seed-caching families had greater hippocampal volume relative to both body weight and brain volume than birds from 10 families that do not cache seeds (see Figure 3.4; Sherry, Jacobs, & Gaulin, 1992). Hampton and Shettleworth (1996) found large hippocampal volume and better spatial nonmatching-to-sample performance in food-storing black-capped chickadees (Poecile atricapillus) compared with nonfood-storing dark-eyed juncos

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100 Hippocampal Complex Volume (mm3)

spatial learning ability and greater spatial memory than closely related species that do not store food. Several experimental studies support these predictions. Bednekoff, Balda, Kamil, and Hile (1997) showed that Clark’s nutcracker and pinyon jays (Gymnorhinus cyanocephalus) had better memory for seed cache location up to 60 days than noncaching Mexican jays (Aphelecoma ultramarina) and western scrub jays (A. coerulescens), although all species showed good recall up to 250 days after learning. Bond, Kamil, and Balda (2007) found better serial reversal learning performance in highly social pinyon jays compared with Clark’s nutcracker and Western scrub jays. Western scrub jays, in contrast, are able to learn to avoid caches where stored food has spoiled with passage of time, suggesting that scrub jays have a form of declarative memory that involves not only space but also time (Clayton, Yu, & Dickinson, 2001). Therefore, each species of corvid has particular abilities that serve functions appropriate for that species. In rodents, Barkley and Jacobs (2007) found that Merriam’s kangaroo rat (Dipodomys merriami), which hoards food, was more accurate in remembering locations for food than the nonhoarding Great Basin kangaroo rat (D. microps). Monogamous versus polygynous species differ in spatial memory requirements because males of monogamous species have relatively small territories and home ranges relative to those of males from polygynous species that must travel over a wider range to locate multiple females. Gaulin and Fitzgerald (1989) hypothesized that polygynous male meadow voles (Microtus pennsylvanicus) would learn spatial mazes more rapidly than females of the same species, whereas there would be no sex differences between males and females of monogamous pine and prairie voles (M. pinetorium and M. ochrogaster). Using a series of Hebb-Williams spatial mazes, Gaulin and Fitzgerald found a sex difference in polygynous voles with males learning faster, but, as predicted, there was no sex difference in maze acquisition in monogamous voles.

Nonstoring Food storing

10

1 1

10

100

1000

Body Weight

Figure 3.4 Hippocampus volume plotted against mean body weight for 13 families of birds. Open symbols indicate families with some species that store food. Both axes are plotted logarithmically. (Adapted from Sherry, Jacobs, & Gaulin, 1992)

(Junco heyemalis). Within these species, there is also variation in caching behavior, spatial memory, and hippocampal volume. Pravosudov and Clayton (2002) compared chickadees from Alaska (where food resources are scarce) with those from Colorado and found that Alaskan birds cached more food, recovered food more efficiently, were more accurate on spatial (but not on nonspatial) learning tasks and had greater hippocampal volume with more neurons. Migratory subspecies of juncos (which presumably have greater need for spatial memory) performed better than nonmigrating juncos in a spatial memory task and had more densely packed neurons in the hippocampus. Polygynous male meadow voles, which must travel over a greater distance to find multiple females than monogamous males or females of either type, also had larger hippocampal volume than females and than either sex of monogamous pine voles (see Figure 3.5; Jacobs, Gaulin, Sherry, & Hoffman, 1990). As noted previously, polygamous males also were faster to solve multiple spatial maze problems than were polygamous females, whereas monogamous males and females were equal in solving spatial mazes (Gaulin & Fitzgerald, 1989). However, increased hippocampal volume is not universally found in species that do more seed caching. Pravosudov and de Kort (2006) found that noncaching Western scrub jays had similar hippocampal volumes to food-caching European jackdaws (Corvus monedula), and Brodin (2005) found that willow tits (Parus montanus) from Europe had twice the hippocampal volume of blackcapped chickadees but did not differ in food hoarding behavior. We cautioned earlier about accepting size alone as a measure of complexity. Precise measures of neuronal

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42

Comparative Cognition and Neuroscience 700 Male Female

Home Range Size (m2)

600 500 400 300 200 100 0 Meadow Vole

Pine Vole

0.05 Male Female

Hippocampal Volume Relative to Brain Volume

0.049 0.048 0.047 0.046 0.045 0.044 0.043 0.042 0.041 Meadow Vole

Pine Vole

Figure 3.5 Home range size and relative volume of hippocampus in polygamous meadow voles and monogamous pine voles. Male meadow voles have significantly greater home range sizes and hippocampus volume than female meadow voles and monogamous voles of either sex. (Adapted from Jacobs, Gaulin, Sherry, & Hoffman 1990)

number, density, and dendritic fields may be more accurate measures of brain differences across species. Much more than spatial memory may be involved in food caching, such as remembering about food quality and time of storage (Clayton, 1998). This suggests that not only the hippocampus but many other brain areas may be involved in cognitive processes relating to foraging and food caching. COOPERATION, RECIPROCITY, AND DONATION Cooperative behavior emerges numerous times throughout the animal kingdom and, as is the case for social learning, its presence does not fit with a simple phylogenetic or brain size explanation. Cooperative interactions between conspecifics can be divided into two categories based on the distribution of costs and benefits to the actors. In one form of cooperation, cooperation for mutual benefit, the act is beneficial to all individuals involved (West, Griffin, & Gardner, 2007). The second form of cooperation occurs when one individual incurs a cost, albeit potentially temporary, while providing a benefit to a second individual. From an evolutionary

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perspective, this costly form of cooperation is most often accounted for by kin selection (W. D. Hamilton, 1964) or reciprocal altruism (reciprocity; Trivers, 1971). The most commonly observed form of mutually beneficial cooperation is cooperative hunting (Packer & Ruttan, 1988). Data from African wild dogs (Lycaon pictus) and spotted hyenas (Crocuta crocuta) demonstrate that hunting success, prey mass, and the probability of multiple kills increased with the number of adults taking part in the hunt (Creel & Creel, 1995; Drea & Frank, 2003). Mutually beneficial cooperative hunting also occurs among African lions (Panthera leo; Schaller, 1972; Scheel & Packer, 1991). In the open plains of Etosha National Park in Namibia, lionesses have acquired preferential hunting roles within their pride, and hunts were more likely to be successful when the group size was large and huntresses occupied their preferred roles (Stander, 1992). Elaborate displays of cooperative hunting have also been observed in some wild populations of chimpanzees (Pan troglodytes; Boesch & Boesch, 1989; Gilby, Eberly, & Wrangham, 2008). Cooperative hunting is not limited to megavertebrates, however. A South American spider (Anelosimus eximius) weighing approximately 1 mg works in groups of a few to several thousand individuals to construct large basket-shaped webs that cover several cubic meters, allowing the spiders to cooperatively capture and subdue prey up to 30 times their own body size (Rypstra & Tirey, 1991). Mutually beneficial cooperation occurs outside of the hunting context as well. For example, many species exhibit mobbing, which is an antipredator behavior characterized by multiple individuals simultaneously attacking or harassing a predator. This behavior has been observed in mammals, fish, birds, and insects (Bartecki & Heymann, 1987; Curio, 1978; Dominey, 1983; Hennessy & Owings, 1978; Hoogland & Sherman, 1976; Shields, 1984). Other examples of mutually beneficial cooperative behavior include alliance formation, in which two or more individuals combine efforts against a third (reviewed in Dugatkin, 2002); mutual grooming (Dugatkin, 1997); and even cooperative mate acquisition (DuVal, 2007). Cooperative interactions that are not mutually beneficial are more difficult to explain within evolutionary theory. If one actor incurs a cost and a second actor acquires a benefit, the question arises as to why the first actor takes part in the costly act. If the individuals are related, then kin selection is the mechanism most commonly referenced to account for this behavior. It is assumed that costs incurred by the actor are offset by the indirect fitness benefits obtained through increasing the survival of relatives with shared genes (W. D. Hamilton, 1963). Well-documented examples of kin selection include cooperative breeding or helping at the nest, whereby reproductively mature individuals remain in

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Cooperation, Reciprocity, and Donation 43

their natal group to assist in the rearing of younger siblings (behavior in birds reviewed in Brown, 1987; behavior in mammals reviewed in Solomon & French, 1997). If the individuals are unrelated, then reciprocal altruism (Trivers, 1971) is often credited as the mechanism for maintaining the apparently costly behavior. Under reciprocal altruism, one individual incurs a cost and provides a benefit to a second individual at present, and at a later time the benefit is repaid by the second individual. In his seminal paper, Trivers listed the following factors that should increase the likelihood of reciprocal altruism occurring in a species: (a) long life span, (b) low dispersal rate, (c) high degree of mutual dependence, (d) extensive parental care, (e) lack of strong dominance hierarchies, and (f) tendency to aid in combat. Examples of reciprocal altruism in the wild include reciprocal sharing of blood meals by vampire bats (Desmodus rotundus; Wilkinson, 1984), predator inspection by sticklebacks (Gasterosteus aculeatus; Milinski, 1987), and reciprocal grooming in impalas (Aepyceros melampus; Hart & Hart, 1992). However, many have argued that empirical evidence for reciprocity is lacking and that, although the idea of reciprocity is theoretically appealing, its occurrence is extremely rare outside of humans (Hammerstein, 2003; Stevens & Hauser, 2004). Schuster and colleagues (Schuster, 2002; Schuster & Perelberg, 2004) have argued that coordinating behavior with a conspecific, often one involved in cooperative behavior, may be intrinsically rewarding. They posited from experiments on rats that economic analyses of the costs and benefits of cooperative interactions would not provide enough information to an outside observer to determine whether the cooperative interaction was beneficial to an actor because intrinsic rewards also play a role, as they do in other social interactions. This hypothesis remains to be tested on additional species and in varying contexts, but it provides a novel way of thinking about when cooperative behavior may emerge across taxa when economic reasoning falls short of explaining observed cooperative behavior. Investigations of cooperation historically focused on the selective pressures that could lead to its emergence (Alexander, 1974; Axelrod & Hamilton, 1981; Brown, 1983; I. M. Hamilton, 1963; W. D. Hamilton, 1964; Mayr, 1961; Trivers, 1971). However, interest in the proximate mechanisms of cooperation has grown (Brosnan & de Waal, 2002), and this may explain why cooperative behavior emerges in some taxa but not others. Recent studies have included investigations into the actors’ understanding of the partner ’s role in the cooperative act, the effects of various spatial and temporal reward distributions on cooperative performance, the impact of the relationship between actors on their ability to cooperate, the social system of the

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species presented with an opportunity to cooperate, and the cognitive skills required to cooperate. Understanding of the partner ’s role in a cooperative act varies across species. Boesch and Boesch (1989) were the first to call attention to and distinguish between cooperation in which the actors take into account their partner ’s behavior and cooperation in which individuals are mutually attracted to the same resource and act independently of one another. The authors conceptually organized the hunting behavior of wild chimpanzees into four categories of increasing complexity based on the degree to which actors integrated their own behavior with that of their partner. This categorization scheme has been subsequently used to understand partner interactions in many cooperative contexts (Chalmeau, Lardeux, Brandibas, & Gallo, 1997; Chalmeau, Visalberghi, & Gallo, 1997; Cronin et al., 2005; Mendres & de Waal, 2000). Great apes (including humans) appear to understand the role their partner plays in a cooperative act and adjust their behaviors accordingly (Brownell, Ramani, & Zerwas, 2006; Chalmeau & Gallo, 1996a, 1996b; Chalmeau, Lardeux, et al., 1997). Variables used to evaluate understanding of the partner typically include measures of attempts to solve the apparatus in the absence of the partner and glances exchanged between actors. Investigations of whether monkeys understand the role of their partner have produced mixed results both within and across species. Some studies with tufted capuchin monkeys demonstrated that capuchins did not take into account the role of their partner when confronted with a cooperative task. The subjects solved the task but did so by chance alone, as determined by the high number of uncoordinated attempts by each actor (Chalmeau, Visalberghi et al., 1997; Visalberghi, 1997; Visalberghi, Pellegrini Quarantotti, & Tranchida, 2000). Others have argued that capuchin monkeys can solve a cooperative task and take into account their partner ’s role when the task design is intuitive, that is, when the subjects are able to see how the apparatus works and presumably understand the effects their actions have on the apparatus (Mendres & de Waal, 2000). We investigated the extent to which pair-bonded cotton-top tamarins understood the role of their partner in a cooperative problem-solving task and found that tamarins adjusted their behavior based on the presence or absence of their partner (Cronin et al., 2005), providing evidence that cotton-top tamarins are capable of understanding their partner ’s role in a cooperative task. Whether an individual will attend to a partner ’s behavior will likely vary with the amount of behavioral coordination and attentiveness to social cues typically expressed by that species. Intra- and interspecific variation in cooperation may be affected by the relationship between actors, as is the case

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for social learning. As argued by van Schaik and Kappeler (2006), individuals bonded over an extended length of time likely do not evaluate the immediate costs and benefits of their behavior but rather evaluate the long-term benefits and costs exchanged throughout the relationship. Dominance asymmetries may also affect cooperative success, either in the form of coercion by dominants to solve the task or avoidance of the task by subordinates (Chalmeau, 1994; Chalmeau & Gallo, 1996b; Chalmeau, Lardeux, et al., 1997; Tebbich, Taborsky, & Winkler, 1996). Melis, Hare, and Tomasello (2006) have found that tolerance of cofeeding in chimpanzee dyads is predictive of their success on a cooperative task. In addition, the social characteristics of a species (e.g., their characteristic degree of tolerance for nearby conspecifics, behavioral coordination, and mutual dependence) may influence the likelihood of successful cooperation (Snowdon & Cronin, 2007). Trivers (1971) noted that in species with strong dominance hierarchies, the likelihood of reciprocal altruism is reduced. Because of high levels of intragroup competition in Guinea baboons (Papio papio), Japanese macaques (Macaca fuscata), and rhesus macaques (M. mulatta), these species did not coordinate efforts to move heavy food-baited stones (Burton, 1977; Fady, 1972; Petit, Desportes, & Thierry, 1992), whereas Tonkean macaques (M. tonkeana), which are characterized by less strict dominance hierarchies and greater social tolerance, were more often successful at coordinating their actions to displace the baited stone (Petit et al., 1992). Consistent with the idea that social context can predict performance on cooperative tasks are the observations that chimpanzees, characterized by a strict dominance hierarchy and low social tolerance, performed better on competitive tasks than cooperative tasks (Hare & Tomasello, 2004) and that the socially tolerant bonobos outperformed chimpanzees on cooperative tasks (Hare, Melis, Woods, Hastings, & Wrangham, 2007). Attempts to investigate the evolutionary origins of the human tendency to act in the best interest of others (i.e., to act prosocially) have led to interesting results. Initial investigations into the evolutionary origins of prosociality indicated that humans’ closest living relatives, chimpanzees, overwhelmingly refused to donate food to a social partner, even when the potential donor could do so with very little effort and could not obtain the food for itself (Jensen et al., 2006; Silk et al., 2005; Vonk et al., 2008). One interpretation that followed from these findings was that the human tendency to act prosocially must have emerged recently in our evolutionary history because the trait was not present in the common ancestor of chimpanzees and humans. However, as we cautioned earlier in this chapter, convergent as well as divergent evolution should be considered. Some evidence has emerged to indicate that prosocial tendencies may be evident elsewhere in the primate order,

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more specifically in a species that exhibits a social system similar to that of humans: the cooperatively breeding common marmoset. Burkart and colleagues (2007) have shown that common marmosets donated food to conspecifics in a task nearly identical to that used with chimpanzees. The authors interpreted their findings as evidence that a cooperatively breeding social system, one shared by humans and cooperatively breeding marmosets and tamarins, is key to the emergence of prosocial tendencies. Additional research is needed to determine whether the social systems of the species are the most important factor in these findings, as marmosets and chimpanzees differ in many other ways as well. However, these initial results highlight the importance of considering social factors in addition to phylogeny. Discussions of cognitive requirements for cooperation are most often made in relation to the ability to understand the role of the partner, as discussed previously, or the ability to engage in reciprocal altruism. Some have argued that the paucity of empirical evidence for reciprocal altruism is due to its steep cognitive demands (Hammerstein, 2003; Stevens & Hauser, 2004). Cognitive skills hypothesized to be necessary for reciprocal altruism include numerical quantification, time estimation, delay of gratification, detection and punishment of cheaters, analysis and recall of reputation, and inhibitory control (Stevens & Hauser, 2004). To date, these cognitive requirements have been discussed only on theoretical grounds and have not been examined empirically. Others assert that reciprocal altruism does not require complex cognition. Two forms of reciprocal altruism that do not require advanced cognition have been put forth. The first is generalized reciprocity, in which individuals decide whether to cooperate based on prior experiences, irrespective of the identity of the current partner (Pfeiffer, Rutte, Killingback, Taborsky, & Bonhoeffer, 2005). Generalized reciprocity does not require analysis and recall of individual reputations. This concept has been well exemplified in recent studies with Norway rats (Rattus norvegicus; Rutte & Taborsky, 2007, 2008). The second form of reciprocal altruism is symmetry-based or attitudinal reciprocity. Symmetry-based reciprocity occurs when closely bonded individuals help one another without stipulating returns. Because of the symmetrical and long-lasting characteristics of the relationship, the benefits generally balance out over a lifetime without the need for any purposeful “scorekeeping” (de Waal & Luttrell, 1988). We have found that unrelated, pair-bonded cottontop tamarins not only cooperate for mutual rewards but also cooperate when rewards are reciprocally distributed between actors and when rewards are repeatedly received by a single individual in the dyad. The tamarins were sensitive to the reward scenarios, cooperating most consistently for mutual rewards and least consistently when the rewards repeatedly went to the same individual, but cooperation

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Cooperation, Reciprocity, and Donation 45

persisted nonetheless (Cronin et al., 2005; Cronin & Snowdon, 2008). The tamarins’ cooperative performance in this study may have been due to attitudinal reciprocity as described by de Waal and Luttrell (1988); that is, the tamarins were cooperative with their long-term mates without keeping track of the exact costs and benefits incurred. Further studies of cooperation in tamarin dyads that differ in their social relationships are needed to further test this interpretation. As with the other phenomena discussed here, cooperative behavior emerges throughout the animal kingdom with little regard for phylogeny. Investigations into the social organizations of species and the social relationships within species have proved the most fruitful for explaining and predicting species and individual differences. Unfortunately, little is known about the neural processes that underlie cooperative interactions in nonhuman animals. However, scientists can gain some insight and begin to make predictions about neural involvement from recent studies performed on the most cooperative species: humans. Cooperation and the Brain Studies emerging from the nascent field of neuroeconomics have begun to elucidate some of the regions of the brain involved in human cooperative behavior (see also McCabe, this volume). These studies have primarily used fMRI data collected while humans engage in cooperative games. However, a single cooperative interaction necessarily includes multiple sequential stages, including the stage in which an individual decides whether to cooperate and the stage in which he or she experiences the outcome of the

cooperative interaction. Each stage is likely to recruit different brain regions, and fMRI investigations have aimed to isolate different stages of cooperative interactions. A recent fMRI study utilized the prisoner ’s dilemma to evaluate neural activation during the initial stage of cooperation, when participants decided whether to cooperate with a confederate. The prisoner ’s dilemma is a widely used game in which two individuals are given the simultaneous choice either to cooperate with the other individual or to defect. The payoff to the individual depends on his or her own choice and the choice of the partner, which is revealed once both individuals have made their choices. Regardless of the other player ’s choice, the choice to defect yields a higher payoff than the choice to cooperate. But if both players defect, both do worse than if both had cooperated (see Figure 3.6A). Rilling et al. (2007) contrasted activations when the participant decided to cooperate with those when the participant decided to defect. Results indicated that the choice to defect was associated with stronger activation in the rostral anterior cingulate cortex (rACC) and the dorsolateral prefrontal cortex (DLPFC). The activation of the rACC and the DLPFC is interesting because the DLPFC is often associated with executive control and goal maintenance and the rACC with the processing of aversive experiences and the detection of cognitive conflict. This pattern of activation seems to indicate that defection is not a simple, favorable choice by participants. The choice to cooperate was correlated with activation in the orbitofrontal cortex (OFC; see Figure 8.12 in Chapter 8). The OFC has been implicated in the processing of rewarding and punishing factors and in emotional decision making

Cooperate Defect

Player A

Player B Cooperate

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R⫽3

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Figure 3.6 A. Prisoner’s dilemma matrix. Payoff awarded to Player A is the top value within each cell; payoff awarded to Player B is the bottom value within each cell. Different payoff values can be used, however this rule must be followed: T > R > P > S. B. Trust game diagram. Each node represents a point at which a player must make a decision. At the first node, Player 1 either

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P1: $0 P2: $405 P2 decision P1 decision

P1: $180 P2: $225 P1: $45 P2: $45

(B)

decides to split the benefits equally, or to allow Player 2 a move. If Player 1 decides to allow Player 2 a decision, the pot increases and both the potential gain and loss increase for Player 1. Player 2 then decides between an option that allows both players rewards but provides him/herself with a greater reward, or an option that provides him/herself with all the rewards. Different payoff values can be used. (Diagram modified from McCabe et al., 2001).

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(Bechara, Damasio, & Damasio, 2000; Ichihara-Takeda & Funahashi, 2006). Furthermore, Rilling et al. (2007) found a negative correlation between OFC and DLPFC activation that they interpreted as interplay between emotional and cognitive control leading up to the execution of cooperation or defection. Additional insight into the brain regions recruited during the decision to cooperate with another individual comes from fMRI investigations of humans engaged in a trust game that required individuals to decide whether to invest money in a second individual who could increase or decrease the payoff to the first individual (see Figure 3.6B). The investigation revealed that those participants who consistently attempted cooperation exhibited more specific prefrontal cortex (PFC) activation when playing the game with a human as compared to a computer employing a fixed, known probabilistic strategy (McCabe, Houser, Ryan, Smith, & Trouard, 2001). However, differential PFC activation between social and nonsocial versions of the game was not found for individuals who rarely cooperated. Using another version of the trust game, King-Casas et al. (2005) reported that the magnitude of neuronal activity in the dorsal striatum correlated with the intention to trust the other player, and the peak of the response shifted earlier in the decision process as player reputations developed in this reciprocal game. Rilling et al. (2002) found that different patterns of neural activation determined by whether the playing partner was identified as a computer or a human. Greater activation was observed in the anteroventral striatum, the rACC, and the OFC during social cooperation than during cooperation with a computer. The OFC and ventral striatum are recruited while one is anticipating an economic reward (e.g., Padoa-Schioppa & Assad, 2006; Roesch & Olson, 2004; Schultz, Dayan, & Montague, 1997), however Rilling et al. (2002) controlled for monetary gain in the social versus nonsocial contrast. Some investigations have specifically examined the differences in neural activation following a potentially cooperative interaction. Using the prisoner ’s dilemma and fMRI, Rilling, Sanfey, Aronson, Nystrom, and Cohen (2004) found that participants displayed increased neural activity in the ventral striatum in response to reciprocated interactions and decreased neural activity in the ventromedial PFC in response to unreciprocated interactions. The authors interpreted these results to indicate that the mesolimbic dopamine system processes errors in predictions about whether a social partner will act reciprocally. In fact, many of the regions recruited during cooperative interactions are the same regions classically involved in processing reward. The neural circuitry often involved in reward includes the caudate and particularly the ventral striatum, amygdala, and the medial and orbital PFCs

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(reviewed in Cardinal, Parkinson, Hall, & Everitt, 2002; see also Volume 2, Chapter 40). It is interesting that even when the effects of tangible rewards are controlled, social cooperative interactions continue to engage some of the reward circuitry, suggesting that cooperating with another individual may be intrinsically rewarding. Similarly, Harbaugh, Mayr, and Burghart (2007) found that voluntary monetary donations also increase the neural activity of reward regions. It seems that data obtained from neuroscientific investigations as well as behavioral observations (i.e., Schuster, 2002; see above) converge to support the hypothesis that acting cooperatively with conspecifics is rewarding, and because these regions are rich in dopamine, it is likely that dopamine is involved in these cooperative interactions. The neuropeptide oxytocin may also be involved in cooperative social interactions. Oxytocin is a nonapeptide produced in the supraoptic and paraventricular nuclei of the hypothalamus. Oxytocin is released into the periphery from magnocellular neurons of the supraoptic nuclei, which project to the posterior pituitary. In addition, oxytocin is released centrally from parvocellular neurons of the paraventricular nuclei (Uvnas-Moberg, 1998). Oxytocin has been identified repeatedly in the coordination of positive social interactions in a wide range of species (reviewed in Uvnas-Moberg, 1998). In addition to having a well-known role in the onset of uterine contractions, milk letdown, and mother–infant bond formation (Gimpl & Fahrenholz, 2001; Levy, Kendrick, Goode, Guevara-Guzman, & Keverne, 1995; Nelson & Panksepp, 1998), oxytocin facilitates the formation and maintenance of important relationships outside the mother–infant context. Oxytocin is integral to the formation of pair bonds in monogamous voles (Carter, 1998; Carter, DeVries, & Getz, 1995; Young, 1999; see Box 3.1 and below). Oxytocin correlates with the expression of trust in humans (Zak, Kurzban, & Matzner, 2005) and increases the amount of generosity (donation) in humans (Zak, Stanton, & Ahmadi, 2007). Furthermore, treatment with oxytocin increases one’s willingness to accept risk in interpersonal interactions (Kosfeld, Heinrichs, Zak, Fischbacher, & Fehr, 2005). Because of the involvement of oxytocin in positive social interactions, some have begun to speculate that oxytocin may play a role in prosocial behaviors such as cooperation, reciprocity, and donation (i.e., Rutte & Taborsky, 2007). The term cooperation is used in different ways by economists, psychologists, and ethologists, and, as we have shown, investigations of cooperative behavior have spanned studies of individuals acting altruistically, engaging in reciprocity, taking risks for kin, providing benefits to mates, and engaging in economic games. The common link in all of these forms of cooperation is that they provide a benefit of some sort for another individual, and the most

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Pair Bonds

BOX 3.1 OXYTOCIN AND VASOPRESSIN Oxytocin (OT) and arginine vasopressin (AVP, also known as antidiuretic hormone) are closely related in structure but have different physiological roles. Both are made of nine amino acids (peptides) folded into a ring, and the two hormones differ in only two amino acids. These hormones are released into the blood from the posterior pituitary gland at the base of the brain, but they are also released from neural tissue in the hypothalamus and are active in several areas of the brain. Until 20 years ago, the best known role for these hormones was in the regulation of peripheral physiological processes. OT is involved in uterine contractions at birth (and indeed a synthetic version of OT is frequently used to induce labor) and in the milk letdown reflex in nursing. AVP is involved in the contraction and relaxation of smooth muscle (e.g., blood vessels) and regulates transport of water and sodium across cell walls, especially in the kidney. In recent years, other behavioral functions of these hormones have become widely known. OT is involved in the formation of pair bonds in monogamous female rodents (Carter, 1998), and AVP is involved in pair bond formation in monogamous male rodents. AVP is also involved in the aggressive behavior shown by territorial monogamous male rodents (Marler et al., 2003). AVP also plays a role in paternal behavior in monogamous rodents (Marler et al., 2003), and OT plays an important role in regulating female sexual behavior and maternal behavior. Both hormones are anxiolytic (i.e., they reduce anxiety), and release of OT during deep massage, stroking, and sexual intercourse has been hypothesized to play an important role in social bonding in humans. Indeed Gonzaga, Turner, Keltner, Campos, and Altemus (2006) found that OT levels increased during displays of romantic love, but not sexual arousal, in college women, suggesting a role of OT in human pair bonding and social reinforcement. information known so far about the brain has come from humans engaged in neuroeconomic games. However, these patterns of neuronal activation provide just a starting point for investigations of other forms of cooperative behavior. Much more information is needed to determine whether species that typically cooperate with one another, such as cooperatively hunting hyenas, have regional specializations or patterns of neuronal activation that differ from those of species that do not typically cooperate with one another. If a comparative neuroscience approach were to

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be applied to cooperative behavior, one might speculate that in species in which it is beneficial to act cooperatively with conspecifics, cooperative behavior will have become associated with the brain’s reward system throughout the evolutionary history. But hypotheses of this sort remain to be tested.

PAIR BONDS In many mammalian species, close affiliative relationships between males and females outside of mating are rare. The closest relationships are found in species with biparental or cooperative care such as prairie voles, titi monkeys, tamarins, and marmosets. In mammals, females incur the costs of gestation and lactation, and males can never be certain of paternity of infants. Under such conditions, it has been assumed that males will be more successful attempting to fertilize as many females as possible rather than investing in parental care. However, in species with biparental or cooperative care, fathers often play a critical role in infant survival, and thus the reproductive success of both parents depends on joint infant care. Biparental, socially monogamous species are expected to have closer, more affiliative social relationships than polygamous species, and it is, in fact, under these conditions that close relationships between mates are found. The formation of the relationship has been well studied. Williams, Catania, and Carter (1992) found that prairie vole females cohabiting with a male for 24 hours with or without mating developed a strong bond. Cohabitation with mating for less than 24 hours, but not cohabitation alone, also led to pair formation. Savage, Ziegler, and Snowdon (1988) found that newly paired cotton-top tamarins spent more time in contact, in grooming, and engaged in sexual activity than did established pairs, with males initiating affiliation more often than females. Widowski, Porter, Ziegler, and Snowdon (1992) found that exposure to a novel cottontop tamarin male with no direct physical contact led to ovulation in reproductively suppressed female cotton-top tamarins. Schaffner, Shepherd, Santos, and French (1995) also found that male black tufted-ear marmosets (Callithrix kuhli) initiated more proximity behavior in the first 40 days after pairing and that sexual behavior decreased over time. Silva and Sousa (1997) studied variation in pair formation in sexually naive common marmosets and found that those that became pregnant within the first 10 weeks had more affiliative behavior, especially grooming, and greater coordination of behavior than those pairs that did not conceive immediately. This is the only study on the reproductive consequences of within-species variation in pair formation.

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Determination that a pair bond exists requires some experimental documentation of attachment: that an animal is distressed upon separation from its mate and displays greater affiliation than baseline upon reunion, that an individual will behave aggressively toward intruders, or that an individual will preferentially seek contact with its mate when given a choice between conspecifics. Mendoza and Mason (1986b) found greater disturbance, more aggression to intruders, and higher cortisol levels in the monogamous titi monkey (Callicebus moloch) compared to the polygynous squirrel monkey (Saimiri sciureus). When separated and offered a choice between mate and infant, titi monkeys chose their mates preferentially. Infants that were separated from both parents chose fathers over mothers (Mendoza & Mason, 1986a). Subsequently, when paired adult titi monkeys were separated for 30 min or 5 days and tested with either their mate or an opposite-sex stranger, male and female titi monkeys showed equal affiliation upon reunion with the mate. In contrast, they showed high levels of arousal when placed with an opposite-sex stranger. These results suggest strong and lasting relationships (FernandezDuque, Mason, & Mendoza, 1997). Cotton-top tamarins also exhibit distress when separated from mates for short time periods, with increased rates of long calling (which is used for within-group cohesion and by lost animals) during the period of separation and increased affiliative and sexual behavior when reunited after separation (Snowdon & Ziegler, 2007). Males are more vocal and appear more disturbed by separation than females. They also display levels of aggression toward intruders of both sexes, with males displaying equal amounts whether the mate is present or absent and females displaying higher levels only in the presence of the mate. Males also groom females significantly more often than females groom males (Snowdon & Ziegler, 2007). All of these results suggest that males are more responsible for maintaining relationships than females. Partners in pair-bonded species can also buffer against the effects of stress. T. E. Smith and French (1997) tested male and female tufted-ear marmosets in novel environments and found that levels of cortisol were lower when pairs were together in a novel environment than when they were alone. Rukstalis and French (2005) played back calls of the mate to isolated marmosets and found that the calls alone reduced cortisol levels as well as stress-related behavior. In summary, lasting adult heterosexual affiliative relationships are found in a few species that are socially monogamous or cooperatively breed. Several studies have looked at behavioral changes during pair formation and have evaluated attachment through studies involving challenges by unfamiliar animals of same and opposite sex and studies involving separation and reunion. The individual

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recognition of a specific individual as a mate and the differential response to the mate than to other potential mates are also key components of a pair bond. What are the neural and hormonal mechanisms that maintain these relationships? Role of Oxytocin and Vasopressin Oxytocin is a neuropeptide that has been implicated in affiliative relationships. In the past 15 years, extensive research, primarily on monogamous voles, has indicated the importance of oxytocin in formation of affiliative relationships, especially in females (for reviews, see the following: mother–infant relationships, Nelson & Panksepp, 1998; and heterosexual adult relationships, Carter, 1998; Carter et al., 1995; Insel, 2003). Oxytocin, but not the closely related nonapeptide vasopressin, appears critical for female prairie voles in relationship formation (Insel & Hulihan, 1995). However, vasopressin levels increase in male prairie voles after pair formation, suggesting a sex difference in which hormones are involved in a pair bond for these rodents (Winslow, Hastings, Carter, Harbaugh, & Insel, 1993). In non-pair-bonding species of voles, oxytocin is not effective for inducing a pair bond and has a different distribution of receptors in the brain (reviewed by Young, 1999). Nonetheless, oxytocin infused chronically into male rats (a polygamous species) led to increased social interactions (Witt, Winslow, & Insel, 1992). Pedersen and Boccia (2002) found that oxytocin was critical for initiation and maintenance of sexual behavior in female rats. Oxytocin injected into the medial preoptic area of male rats also facilitated social recognition, whereas vasopressin did not (Popik & van Ree, 1991). However, Dantzer, Koob, Bluthé, and Le Moal (1988) reported that vasopressin in the septal region facilitated social memory in rats. Oxytocin knockout mice fail to recognize familiar conspecifics, and oxytocin administered to the medial amygdala prior to initial exposure facilitated social recognition (Bielsky & Young, 2004; Ferguson, Aldag, Insel, & Young, 2001). Rosenblum et al. (2002) compared highly affiliative bonnet macaques (Macaca radiata) with socially distant pigtail macaques (M. nemestrina) and found that the more affiliative bonnet macaques had higher cerebrospinal fluid oxytocin levels and lower corticotrophin-releasing hormone levels than the pigtail macaques. Winslow, Noble, Lyons, Sterk, and Insel (2003) reported that mother-reared male rhesus macaques had elevated cerebrospinal fluid oxytocin levels compared with nursery-reared macaques, and there was a significant correlation between oxytocin levels and affiliative behavior. In contrast, vasopressin levels did not differ with rearing condition but correlated positively with fearful behavior in rhesus macaques.

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Pair Bonds

Interest in oxytocin in humans has increased recently because of the role of oxytocin in both mother–infant attachment and adult affiliative behavior. Wismer Fries, Ziegler, Kurian, Jacoris, and Pollak (2005) found that children had elevated oxytocin levels when performing a task that included contact by the mother but not contact by an unfamiliar female. In contrast, children adopted from Eastern European orphanages several years previously showed low oxytocin responses to both adopted mothers and an unfamiliar female, indicating that early experience affects the oxytocin system. Oxytocin administered intranasally increases trust among humans (Kosfeld et al., 2005; Zak et al., 2005). Infusions of oxytocin into people with autism and with Asperger syndrome reduced repetitive behavior (Hollander et al., 2003) and increased retention of social cognition (Hollander et al., 2006). These studies support the role of oxytocin in producing calm behavior and facilitating social memories in humans. Turner, Altemus, Enos, Cooper, and McGuinness (1999) found that women asked to recount a negative experience of loss or abandonment showed decreased levels of serum oxytocin correlated with the degree of negative emotion expressed. Using data from Turner et al. (1999), Gonzaga et al. (2006) reported that women engaging in affiliation signals with a romantic partner had a significant positive correlation between the amount of positive signaling and serum oxytocin. Grewen, Girdler, Amico, and Light (2005) reported higher plasma oxytocin in both men and women in relationships with strong partner support. Thus, changes in oxytocin appear to track affiliative and positive emotional states in both sexes and across many species. In humans, peripheral measures of oxytocin levels appear to correlate with differences in affiliation, and peripheral infusions of oxytocin can increase affiliative behavior and social memory. Most comparative research on pair bonds has involved evaluating species differences; however, within-species individual differences in relationship quality and hormonal correlates are found in nonhuman primates as well. We have observed a fivefold variation in the amount of affiliative behavior between pairs of cotton-top tamarins and have also observed a fivefold variation in urinary oxytocin levels in both sexes that correlates with the amount of affiliative behavior expressed. Variation in sexual behavior explains most of the variance in male oxytocin levels, whereas variation in contact and grooming explains most of the variance in female oxytocin levels (Snowdon et al., in preparation). In summary, there is clear evidence for the importance of oxytocin for pair bonding in females of monogamous vole species and for the importance of vasopressin in male voles. Oxytocin can supersede the effects of mating

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in pair bond formation. Oxytocin affects sexual behavior in female rodents and macaques and also appears to be important for social recognition in male rodents. In rodents, tamarins, and human primates, peripheral measures of oxytocin appear to be correlated with affiliation, attachment, trust, and positive emotions, and peripheral administration of oxytocin can alter affiliative behavior. Brain Mechanisms Comparative work between monogamous and polygynous voles has shown that monogamous female voles but not polygamous voles have increased oxytocin receptor density in the nucleus accumbens and caudate putamen and greater vasopressin receptor density in the ventral pallidum (Table 3.2; Young & Wang, 2004). Little is known about oxytocin distribution in monogamous, pair-bonded primate species, but Wang, Moody, Newman, and Insel (1997) found that there were no sex differences in the distribution of immunoreactive oxytocin neurons and fibers in the common marmoset. Oxytocin immunoreactive neurons were found in the paraventricular and supraoptic nuclei of the hypothalamus, the bed nucleus of the stria teminalis, and the medial amygdala. Vasopressin cells were found in the paraventricular, supraoptic, and suprachiasmatic nuclei and in the lateral area of the hypothalamus. The only sex difference was that males had a greater density of vasopressin reactive cells in the bed nucleus of the stria terminalis than females. In a biparental species, in which both parents are essential for infant care and both sexes contribute to the formation and maintenance of a pair bond, it is reasonable to find no sex differences in distribution of oxytocin-reactive neurons in the brain. In further work on common marmosets, Wang, Toloczko, et al. (1997) found vasopressin receptor binding in the nucleus accumbens, diagonal band, lateral septum, bed nucleus of the stria terminalis, amygdala, and anterodorsal and ventromedial hypothalamus, in addition to areas with immunoreactivity. Marmosets differed from voles in important ways: No vasopressin-producing cells were found in the amygdala. There was no plexus of immunoreactive fibers in the lateral septum. There was much greater visualization of vasopressin immunoreactive cells in the bed nucleus of the stria terminalis. Taken together, the results on marmosets suggest relatively little sexual dimorphism, an extensive overlap of oxytocin and vasopressin immunoreactive cells, and a different distribution than that found in rodents. So far, pair formation and maintenance has been treated as a unitary, species-specific trait, and yet there may be considerable individual variation within species, as we have noted. Recently, Hammock and Young (2005) described variation in social engagement and affiliative

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licking and grooming suggests the possibility for an interaction between early experience and gene expression.

TABLE 3.2 Oxytocin and arginine vasopressin receptor distribution as a function of mating system in voles and monogamous common marmosets. Brain Area

Monogamous vs. Polygamous Volea,b

Monogamous Primatec

SUMMARY

Oxytocin Nucleus accumbens

Monogamous

NA

Prelimbic cortex

Monogamous

NA

Bed nucleus of stria terminalis

Monogamous

NA

Midline thalamus

Monogamous

NA

Ventral reunions

Monogamous

NA

Lateral amygdala

Monogamous

NA

Central amygdala

Both

NA

Lateral septum

Polygamous

NA

Ventromedial hypothalamus

Polygamous

NA

Diagonal band

Monogamous

Yes

Laterodorsal thalamus

Monogamous

No

Central amygdala

Monogamous

Yes

Basolateral amygdala

Monogamous

Yes

Bed nucleus stria terminalis

Monogamous

Yes

Nucleus accumbens

Both

Yes

Accessory olfactory bulb

Both

No

Superior colliculus

Both

No

Lateral septum

Polygamous

Yes

Periventricular hypothalamus

NA

Yes

Ventromedial hypothalamus

NA

Yes

Suprachiasmatic hypothalamus NA

Yes

Vasopressin

a Oxytocin data from “Oxytocin Receptor Distribution Reflects Social Organization in Monogamous and Polygamous Voles,” by T. R. Insel and L. E. Shapiro, 1992, Proceedings of the National Academy of Sciences, USA, 89, pp. 5981–5985. b Vasopressin data from Insel, Wang & Ferris (1994). c Marmoset vasopressin data from “Vasopressin in the Forebrain of Common Marmosets (Callithrix jacchus): Studies with in situ Hybridization, Immunocytochemistry and Receptor Autoradiography,” by Z. Wang, D. Toloczko, L. J. Young, K. Moody, J. D. Newman & T. R. Insel, 1997, Brain Research, 768, pp. 147–156. Note. NA ⫽ not reported or known.

behavior in monogamous male prairie voles and correlated this variation with a polymorphism in the promoter region of the vasopressin 1a receptor. Thus, variation in the promotor region of the receptor genes, and consequently the expression of vasopressin 1a receptors, may account for behavioral variation within a species. Similar studies remain to be done with respect to oxytocin receptors. The variation in receptor sensitivity to estrogen described by Champagne et al. (2001) as a function of early maternal

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We have demonstrated that both the proximate and ultimate mechanisms contributing to inter- and intraspecific variation across a wide range of cognitive phenomena can be best understood through a comparative lens that integrates not only phylogeny and brain size but also social and ecological factors. In our discussions of social learning, cooperation, spatial memory, and pair bonding, we have emphasized strong theoretical predictions about socioecological influences on the expression of these behaviors within and across taxa, and, when data have been available, we have highlighted underlying neuronal and hormonal differences that may relate to the observed variation. One major implication of this comparative perspective is that no single species will likely serve as a suitable model for understanding the myriad of interesting human cognitive abilities. For example, there is strong momentum to use various species of macaques as model species for humans in neuroscience, but we should keep in mind the potential implications of the differing social and ecological pressures that macaque and human lineages have faced since the divergence of our ancestry nearly 40 million years ago. Awareness of the cognitive domains in which macaques and humans would be expected from a socioecological perspective to differ may sharpen interpretations of findings from macaque studies and generate interesting hypotheses about interspecific variation if we were to look beyond humans and macaques. To better understand the selective pressures that have contributed to the wide range of expression of cognitive abilities across taxa, we must embrace data collected on primates and nonprimates alike and rely on multifaceted hypotheses that incorporate social and ecological pressures as well as phylogeny and brain size. Interdisciplinary collaboration is integral to accomplishing this feat but should yield a more comprehensive and generalizable understanding of both human and nonhuman cognition.

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Williams, J. R., Catania, K. C., & Carter, C. S. (1992). Development of partner preferences in female prairie voles (Microtus ochrogaster): The role of social and sexual experience. Hormones and Behavior, 26, 339–349. Winslow, J. T., Hastings, N., Carter, C. S., Harbaugh, C. R., & Insel, T. R. (1993, October 7). A role for central vasopressin in pairbonding in monogamous prairie voles. Nature, 365, 545–548. Winslow, J. T., Noble, P. L., Lyons, C. K., Sterk, S. M., & Insel, T. S. (2003). Rearing effects on cerebrospinal fluid oxytocin concentration and social buffering in rhesus monkeys. Neuropsychopharamacology, 28, 910–918. Wismer Fries, A. B., Ziegler, T. E., Kurian, J. R., Jacoris, S., & Pollak, S. D. (2005). Early experience in humans is associated with changes in neuropeptides critical for regulating social behavior. Proceedings of the National Academy of Sciences, USA, 102, 17237–17240. Witt, D. M., Winslow, J. T., & Insel, T. R. (1992). Enhanced social interactions in rats following chronic, centrally infused oxytocin. Pharmacology, Biochemistry, and Behavior, 43, 855–861. Young, L. J. (1999). Oxytocin and vasopressin receptors and speciestypical social behaviors. Hormones and Behavior, 36, 212–221. Young, L. J., & Wang, Z. (2004). The neurobiology of pair bonding. Nature Neuroscience, 7, 1048–1054. Zak, P. J., Kurzban, R., & Matzner, W. T. (2005). Oxytocin is associated with human trustworthiness. Hormones and Behaviour, 48, 522–527. Zak, P. J., Stanton, A. A., & Ahmadi, S. (2007). Oxytocin increases generosity in humans. Public Library of Science, 2(11), E1128.

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Chapter 4

Biological Rhythms LANCE J. KRIEGSFELD AND RANDY J. NELSON

“a rose is not necessarily and unqualifiedly a rose . . . it is a very different biochemical system at noon and at midnight.” —Colin Pittendrigh, 1965

When it comes to success, the expression “timing is everything” is an oft-touted mantra. Traditionally, however, timing was not considered important in the study of the mechanisms underlying behavior. Yet, behavioral constructs such as learning, memory, sensation, perception, attention, and motivation vary markedly according to the time of day or season of the year. Motivation can be defined generally as why individuals do what they do. From a neurobiological perspective, an animal eats because its hunger circuits are activated by specific neurochemicals. However, the response of circuits underlying motivated behaviors is quite different given time of day or year, and it is essential to consider timing when asking questions about the biological mechanisms underlying behavior. To add to this example, an animal eats at night in response to the impact of its internal biological clock on the responsiveness of hunger circuits to specific neurochemical signals. Despite the impact of timing systems on brain and behavior, their influence on dependent measures of interest is frequently ignored. For example, learning and memory performance are typically examined during the middle of the day in nocturnal (night active) rodents, a time when they are normally asleep. As a result, many reported deficits in performance can be attributed to time-of-day effects rather than (or in addition to) the intended manipulation. One goal of this chapter is to underscore the impact of biological rhythms on behavior and physiology to encourage a full appreciation of the significance and magnitude of such timed changes for investigators in the behavioral sciences.

FUNCTIONAL SIGNIFICANCE OF BIOLOGICAL RHYTHMS One of the most predictable features of life on earth is the regular pattern of environmental changes associated with the movement of the planet. Life evolved in a cyclic environment. Except for organisms living at the bottom of the oceans or deep within caves, day follows night, and the seasons change. These orderly and predictable changes in the environment have existed since life first began to evolve, although the timing of these events may have changed slightly over time. The rotation of the earth results in periodic exposure to the radiation of the sun, which causes predictable changes in light and ambient temperatures, as well as associated changes in the relative humidity of the air and in the oxygen levels of aqueous habitats. The biological clocks of animals and plants permit them to start or stop locomotor activities or activate photosynthetic machinery, respectively, in preparation for light. The action of the master biological clock in synchronizing individual bodily functions has been compared to the role of the conductor of an orchestra. As a result, internal processes serve to prepare the body for certain activities to occur later; for example, the elevated adrenal secretions coinciding with the morning onset of activity prepare an individual for increased activity levels and for breaking the nightly fast. All eukaryotic and most prokaryotic organisms tested to date, from unicellular organisms to humans, display circadian rhythms. Predictably, circadian rhythms have not evolved in organisms that live for less than 24 hours. Similarly, circannual rhythms have evolved only in animals that live for a year or more. Physiological systems reveal a wide range of rhythmic changes. For instance, neurotransmitter turnover, body temperature, and blood plasma levels of adrenalin, potassium, sodium, cortisol, androgens, and growth

We thank Zachary Weil for helpful comments on an earlier version of this chapter. The authors were supported during the preparation of this chapter by NIH grants HD050470 (LJK), MH57353, and NSF grant OIS 04-16897 (RJN). 56

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hormone all show pronounced circadian rhythms. Some of these changes may be on the order of 100% to 200% from baseline values. Peak daily cortisol concentrations, for example, which usually occur just prior to or immediately after awakening, coincide with the onset of activity in the morning. This programmed elevation of cortisol concentrations increases blood pressure and cardiac output prior to the active phase of the day. Increased cortisol concentrations are not driven by increased activity levels because the same circadian rhythm is observed in bedridden patients under constant conditions (Aschoff, 1965). In some instances, neurochemical receptors are produced prior to a circadian-programmed increase in ligand production, with both processes being coordinated by the circadian system. Body temperature peaks in mid-afternoon, when people are most active, but again, muscular activity is not solely responsible for this “heating” (Refinetti & Menaker, 1992). It may seem that the concept of biological clocks and programmed temporal changes in behavioral function is at odds with the concept of homeostasis in biological sciences and medicine. Homeostatic processes work to maintain physiological parameters within specific and often narrow ranges. Biologists and physicians frequently considered large fluctuations in physiology and behavior to be pathological; until recently, many resisted the idea of the programmed changes in physiology and behavior that we now understand to underlie homeostatic processes. As a result, we should not consider the appropriate parameters for a specific system to be fixed but, instead, variable relative to time of day or year. What orchestrates physiology and behavior so that organisms engage in appropriate behaviors at suitable times of day or during optimal seasons of the year? Some rhythms are the result of environmental factors that impose temporal control over behavior and physiology—these rhythms are termed driven rather than endogenous (from within). In contrast, most temporal changes in brain and behavior are controlled by internal biological clocks. This chapter focuses on these latter clocks in the control of behavior.

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Chronobiology has borrowed terms and concepts extensively from the field of engineering. For example, a rhythm is defined as a recurrent event that is characterized by its period, frequency, amplitude, and phase (Vitaterna, Takahashi, & Turek, 2001). Period is the length of time required to complete one cycle of the rhythm in question (e.g., the amount of time required to go from peak to peak or trough to trough). Frequency is computed as the number of completed cycles per unit of time (e.g., 6 cycles/day). Amplitude is the amount of change above and below the average value; that is, the distance of the peak from the average. The phase represents a point on the rhythm relative to some objective time point during the cycle. For example, under standard conditions, the phase of onset of the activity portion of a mouse’s activity-rest cycle corresponds closely with the onset of dark (Figure 4.1). Phase relations among various biological rhythms can also be described (e.g., the onset of the active phase of the daily sleep-wake cycle tightly corresponds with the peak of stress hormones secretion in both mice and humans). Many biological rhythms have been recognized for thousands of years. In the past, however, rhythms in migration, daily activity patterns, or hibernation were attributed to exogenous factors. Although exogenous factors may serve a permissive or synchronizing role for biological rhythms, endogenous timing mechanisms mediate many of the observed rhythms in brain and behavior. The best evidence to document whether a rhythm is driven by exogenous or endogenous signals is obtained via isolation studies (Aschoff, 1981; DeMairan, 1729; Thrun, Moenter, O’Callaghan, Woodfill, & Karsch, 1995; Vitaterna et al., 2001; Zucker & Boshes, 1982). Persistence of a biological rhythm in the absence of environmental cues provides compelling evidence that the rhythm under study is generated from within the individual and not driven by the environment. If a biological rhythm disappears under constant conditions, then it is reasonable to suggest that some cyclic cue in the environment drives the biological rhythm. This logic

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Figure 4.1 Components of biological rhythms. Rhythms can be analyzed in terms of amplitude, frequency, and period length. Note. The relationship from one rhythm to another is expressed in terms of phase relationships. Both cycles have a period of 24 hours (frequency  1 day).

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has now been applied in hundreds of studies that have firmly established the existence of biological rhythms driven by endogenous biological clocks. Incredibly, the period of the daily activity rhythm of one individual can be transferred to another individual by means of brain tissue transplants that contain the master biological clock (Lehman et al., 1987; Ralph, Foster, Davis, & Menaker, 1990). All animals (and all eukaryotic and some prokaryotic plants) studied to date have endogenous clocks that mediate biological rhythms (Roenneberg & Merrow, 2002). The periods of biological rhythms range from the 1-ms cycle of firing among some neurons to longer cycles such as the 90-minute cycle of REM sleep, the 4- or 5-day estrous cycles of rats, the annual cycle of hibernation, the 17-year cycle of cicada emergence, or the 100-year cycle of century plant flowering. How are these biological rhythms generated? Are they all mediated by biological clocks? Certainly, the periods of some biological rhythms, including most central nervous system and cardiovascular rhythms vary widely within the same individual depending on activity. The periods of other endogenous cycles, such as the wake-sleep cycle, are largely constant for the same individual, but there may be great inter-individual or species variation. Four types of biological rhythms can be identified, however, that are typically coupled with environmental factors, and the periods of these rhythms vary little under natural conditions. These relatively constant biological rhythms mimic the periods of the geophysical cycles of night and day (circadian), the tides (circatidal), the phases of the moon (circalunar), and the seasons of the year (circannual; Dunlap, Loros, & DeCoursey, 2004). These rhythms persist when animals are isolated from the respective environmental cues, but when isolated, these rhythms only approximate the periods of the environmental cues to which they are normally synchronized. Thus, the terms for many biological rhythms use the prefix circa (Halberg, 1959). Although isolation experiments are necessary to determine the extent of endogenous generation of any biological rhythm, it should be emphasized that the environment often exerts permissive effects on endogenously driven biological rhythms. The evidence for circadian or circannual rhythms is much stronger than for circatidal and circalunar rhythms, especially among vertebrates. Thus, this chapter focuses on circadian and circannual rhythms in behavioral neuroscience. Light serves as an important environmental time cue, or zeitgeber (from the German, meaning “time giver”), for most species (Morin & Allen, 2006; Roenneberg, Daan, & Merrow, 2003). Temperature is an important zeitgeber for some poikilothermic animals, and possibly a secondary zeitgeber for birds and mammals, but light is the primary zeitgeber among homeothermic animals (Foster,

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Hankins, & Peirson, 2007). As mentioned previously, the internal clock runs with a period that is approximately 24 hours in constant conditions, yet the functions controlled by this clock recur precisely every 24 hours when entrained (synchronized) to a 24-hour light-dark cycle. Because day length varies across the year in relatively small increments, internal clocks must exhibit plasticity. If a mouse is placed in constant dim light so that there is no daily zeitgeber, then the onset time of its locomotor activity begins to drift out of synchrony with local time; similarly, if a person is put into a windowless room for weeks, his wake-sleep cycle will drift out of phase with local conditions. Without the daily light-dark cycle providing a daily reset, the endogenous circadian clock of a hamster or human only approximates 24-hour cycles. Biological rhythms that are not synchronized with environmental cues are called free-running. Each individual displays its own free-running period (abbreviated as ), which remains relatively constant. The free-running periods of biological clocks are precise, but are not exactly 24 hours. The observation that different hamsters display an array of different free-running periods when housed in the same room suggests that they are not synchronized by each other ’s behavior, and that subtle geophysical cues are not providing temporal information. However, social factors can provide a zeitgeber for humans living in constant conditions (Mistlberger & Skene, 2004, 2005; Turek & Zee, 1999). Appropriately timed exercise or exogenous melatonin also influences human circadian timing (Mistlberger & Skene, 2005). As noted, individual animals display species-specific times of locomotor activity onset that are often linked to the timing of food intake, water consumption, and social activities (Mistlberger & Skene, 2004, 2005). If the zeitgeber is phase-shifted, then animals adapt their activity to the new regimen in a few days (length of time is dependent on the extent of change). The circadian rhythms of other physiological processes, such as adrenocortical hormone release, body temperature, and blood plasma volume, may require more or less time to synchronize to the new lighting regime (Davidson, Yamazaki, Arble, Menaker, & Block, 2006; Yamazaki et al., 2000). This process of adaptation to rapid phase shifts in environmental lighting results in what is commonly called “jet lag” when people travel through different time zones. Ultradian (shorter than circadian) and infradian (longer than circadian) rhythms are biological rhythms that do not correspond to any known geophysical cycles. Ultradian cycles are commonly observed; for example, the 90-minute cycle characteristic of REM sleep is a welldocumented ultradian rhythm (Schulz & Lavie, 1985). The pulsatile secretion of several hormones, including gonadotropin-releasing hormone (GnRH), luteinizing hormone

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Circadian Control of Brain and Behavior 59

(LH), testosterone, growth hormone, and corticosterone, represent ultradian rhythms (Schulz & Lavie, 1985). Ultradian rhythm variation in free estradiol and cortisol concentrations during menstrual cycles correlates with depressive symptoms (Bao et al., 2004) as well as sleep disturbances (Voss, 2004). Ultradian rhythms in locomotor activity, feeding, and metabolism have been reported in a number of high-metabolic mammalian species such as shrews and voles (reviewed in Liu, Li, & Wang, 2007), and may be related to ancient metabolic cell cycles (Lloyd, Lemar, Salgado, Gould, & Murray, 2003). Infradian rhythms are less frequently observed than ultradian rhythms; such biological rhythms are longer than a day, but shorter than a lunar month. Generally, infradian rhythms in testicular function are rare among vertebrates. Dormice (Glis glis) have been reported to display infradian rhythms of about 60 days in body mass and body fat content (Grimes, Melnyk, Martin, & Mrosovsky, 1981; Melnyk, Mrosovsky, & Martin, 1983a, 1983b). The most common types of infradian rhythms are those associated with ovarian cycles; that is, estrous or menstrual cycles. Hamsters and mice display 4-day estrous cycles; rats have estrous cycles of either 4 or 5 days. Estrous cycles are 16 days in guinea pigs and sheep. Estrous cycles persist in constant conditions (i.e., self-sustaining), but are endogenously generated rhythms that do not correspond to any known geophysical cue. Although human menstrual cycles approximate a lunar month, the length of the menstrual cycle seems to be only coincidentally similar to the length of the lunar month, rather than reflecting any adaptive link to the lunar cycle (Knobil & Hotchkiss, 1988). CIRCADIAN CONTROL OF BRAIN AND BEHAVIOR Evolution of the Circadian System From prokaryotic to eukaryotic organisms, the 24-hour solar cycle has had a pervasive impact on the evolution of life. As suggested previously, a variety of conditions require that organisms internally track daily time. For instance, all energetic requirements for normal health and functioning cannot be simultaneously fulfilled, and peaks in energetic processes must be partitioned throughout the day. Likewise, all behavioral requirements (e.g., foraging, mating, nest building) cannot be simultaneously performed. To allow organisms to anticipate daily environmental change and synchronize their behavior and physiology accordingly, individuals have evolved an endogenous circadian timekeeping mechanism. Not only does this system allow for the coordination of internal physiology, but it also allows animals to predict recurring 24-hour events, such

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as food availability and predator activity, and adjust their behavior appropriately. Perhaps the most direct evidence for the adaptive significance of circadian rhythms comes from studies of cyanobacteria—a group of prokaryotes. Analysis of relative fitness of different strains of cyanobacteria shows that strains with a circadian period similar to the light/dark cycle of the environment have greater reproductive success (Ouyang, Andersson, Kondo, Golden, & Johnson, 1998; Woelfle, Ouyang, Phanvijhitsiri, & Johnson, 2004). Strains whose clocks were disrupted were defeated by strains with a functional clock, but only when held in a light/dark cycle; any competitive advantage was lost in constant conditions. In multicellular organisms, this competitive advantage is also observed; Arabidopsis thaliana, as in cyanobacteria, grow faster and survive longer when housed in light cycles closest to their endogenous circadian period (Dodd et al., 2005). Circadian rhythms are strikingly ubiquitous, with most behavioral, biochemical, and physiological responses of organisms showing daily variation that persists in constant environmental conditions. As described next, some genes are part of the cellular clockwork mechanism, whereas others are controlled directly or indirectly by these core clock genes (i.e., clock-controlled genes). To further understand the evolution of the circadian system, one logical step is to determine the percent of the genome under circadian control across taxa. In Arabidopsis, about 6% of the estimated 8,000 genes studied are rhythmic (Harmer et al., 2000). In contrast, in the retina of Xenopus perhaps only a few critical proteins (0.2%) are directly under the control of the circadian clock (C. B. Green & Besharse, 1996). In Drosophila, an oligonucleotide-based high-density array to measure gene expression changes on a whole genome level revealed that approximately 7% to 9% of genes express a circadian pattern (Ceriani et al., 2002; Claridge-Chang et al., 2001; C. B. Green & Besharse, 1996; McDonald & Rosbash, 2001). Investigations of head versus body rhythms showed that the genes exhibiting circadian patterns have little overlap, suggesting specificity of function for core clock genes (CCGs) in distinct systems (Ceriani et al., 2002). In mice the same general conclusion has been reached by comparing gene expression patterns between liver and heart (Storch et al., 2002). While 8% to 10% of genes are rhythmically expressed in one of the tissues, there are few genes that show circadian regulation in both tissues. Even the genes that have a rhythm in both tissues are frequently out of phase, suggesting independent local function. Determination of similarities and differences across species in clock gene and CCG homologues, and the signals that set their phase, will be necessary to fully understand the evolution of the circadian system.

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Biological Rhythms

Adaptive Significance of the Circadian System Animals have evolved to synchronize their endogenous circadian rhythms with the environment in order to promote survival (e.g., DeCoursey & Krulas, 1998). Numerous behaviors are restricted to specific times of day in response to a variety of selection pressures. For example, diurnal species (i.e., species active during the day) confine behaviors such as feeding, locomotion, foraging, and reproduction to the light hours in order to avoid predation. Likewise, nocturnal (i.e., active at night) species such as owls are active at night to maximize the availability of nocturnal prey (e.g., small rodents). As described later, the circadian “clock” that synchronizes rhythms in behavior and physiology is localized to a discrete bilateral nucleus in the anterior hypothalamus. If this master brain clock is destroyed, animals lose their ability to restrict behaviors to a particular time of day, and this arrhythmicity may compromise survival (DeCoursey & Krulas, 1998; DeCoursey, Krulas, Mele, & Holley, 1997). Humans have evolved to maintain maximum performance during daytime and circadian variation in learning and memory, attention, reaction time, and perception are prominent (Babkoff, Caspy, Mikulincer, & Sing, 1991; Colquhoun, 1981). This is especially important for individuals working night shifts that require critical cognitive performance. Generally, human performance peaks with the daily afternoon peak in body temperature, although verbal reasoning appears to peak earlier in the circadian cycle (Colquhoun, 1981). When people were maintained in the laboratory for a month in either 24- or 24.6-hour days, about half of the individuals adapted with peak melatonin occurring during the normally scheduled sleep period (synchronized), whereas half displayed peak melatonin while awake (nonsynchronized). Nonsynchronized individuals reduced total sleep time, as well as sleep latency and rapid eye movement (REM) latency. Cognitive performance was impaired among the nonsynchronized individuals and enhanced among the synchronized subjects (Wright, Hull, Hughes, Ronda, & Czeisler, 2006). People are likely to be nonsynchronized when working during the night shifts, but resorting to a normal day-night schedule during the weekend, or when undergoing repeated jet travel across several time zones. The ability of people to estimate time intervals also varies on a circadian basis. For example, time perception evaluations at 0900, 1300, 1700, and 2100 hours revealed that short-term time perception was more influenced by circadian phase than by memory load or various psychophysiological factors (Kuriyama et al., 2003). Such human studies should be interpreted cautiously because either approximately onethird of the day is missing or circadian influences are masked by sleep deprivation. These latter difficulties can be overcome by distributing small meals and naps throughout the day and having subjects maintain a constant routine designated by

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the researchers (Brown & Czeisler, 1992; el-Hajj Fuleihan et al., 1997; Khalsa, Jewett, Duffy, & Czeisler, 2000). One study of visual selective attention used a different technique to dissociate the effects of circadian phase and time awake (Horowitz, Cade, Wolfe, & Czeisler, 2003). After 38 hours of no sleep, observers increased reaction times for spatial configuration and conjunction tasks. Observers traded accuracy for speed when sleepy, which could lead to decision errors. Indeed, extended shift durations lead to increased impairments in attentional errors, significant medical errors, and so-called adverse events in critical care units (Barger et al., 2006). In a web-based analysis of medical errors, interns working more than 5 extended-duration shifts per month reported more attentional failures during lectures, rounds, and clinical activities, including surgery; fatiguerelated preventable adverse events (including fatality) increased by 300% in these interns. Temporal variation in behavior is associated with temporal variation in underlying physiology and biochemistry. These underlying processes are modulated by endogenously driven circadian rhythms that are synchronized by environmental time cues. Clearly, a body clock that is not synchronized with the environment is not adaptive. Some rhythms, however, exhibit a peak in activity during one time of day while other rhythms are at a nadir at this time. For example, in humans, cortisol concentrations peak at dawn while melatonin and prolactin concentrations peak in the middle of the night. The relationship between daily fluctuations in physiology and the environmental light/dark cycle is different for nocturnal and diurnal species. Thus, although a myriad of rhythms have a period of about 24 hours, each of these rhythms has a unique phase with respect to one another and to the light/dark cycle (Figure 4.2). Stated differently, although bodily rhythms exhibit different phases

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Figure 4.2 Examples of circadian rhythms in humans. Note. Shaded areas represent times of sleep whereas the unshaded areas represent times of activity. All parameters exhibit unique peaks and troughs relative to each other. This phase relationship among individual rhythms is critical for optimal functioning and the maintenance of homeostasis.

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Circadian Control of Brain and Behavior 61

relative to some objective point in time, the phase relationship among rhythms remains stable, unless a perturbation occurs (e.g., jet lag). Consequently, circadian rhythms help to maintain homeostasis within the body. In addition, the phase of specific rhythms helps to prepare the body for necessary daily activities in advance of their actual occurrence. For example, cortisol rhythms rise in humans prior to waking in order to facilitate the onset of morning activity (e.g., Van Cauter & Refetoff, 1985; Weitzman et al., 1971). It is not difficult to imagine how physiological functioning would suffer without an internal circadian clock synchronized to the environment. Most of us have experienced general feelings of malaise and other maladies following a long flight across time zones. While we can recover within a few days from acute jet lag, millions of frequent flyers, shift workers, individuals with sleep disorders, and other individuals whose work day is not fixed are exposed chronically to such temporal disruptions. These individuals provide the opportunity to examine the effects of more chronic circadian disruptions. In fact, this loss of synchrony between the circadian clock in the brain and the environment leads to pronounced clinical pathologies. One recent study found that elderly mice subjected to temporal disruptions equivalent to a flight from Washington to Paris, once a week for 8 weeks, die sooner a result of their bodies being out of sync with local time (Davidson, Sellix, et al., 2006). Flight attendants frequently traveling across time zones exhibit cognitive deficits associated with reductions in temporal lobe structures (Cho, 2001; Cho, Ennaceur, Cole, & Suh, 2000). Numerous studies show that shift workers have a higher incidence of cancer (Conlon, Lightfoot, & Kreiger, 2007; Hansen, 2006; Kubo et al., 2006; O’Leary et al., 2006; Patel, 2006), diabetes (Karlsson, Alfredsson, Knutsson, Andersson, & Toren, 2005; Morikawa et al., 2005; Poole, Wright, & Nattrass, 1992; Robinson, Yateman, Protopapa, & Bush, 1990; Sanborn, Currie, & Bailey, 1982), ulcers (Costa, 1996; Koda et al., 2000; Kolmodin-Hedman & Swensson, 1975; Segawa et al., 1987), hypertension and cardiovascular disease (Alstadhaug, Salvesen, & Bekkelund, 2005; Costa, 1996; Hwang & Lee, 2005; Kivimaki et al., 2006; Wolk, Gami, Garcia-Touchard, & Somers, 2005), psychological disorders (Bildt & Michelsen, 2002; De Koninck, 1991; Leonard, Fanning, Attwood, & Buckley, 1998; Munakata et al., 2001; Skipper, Jung, & Coffey, 1990; Venuta, Barzaghi, Cavalieri, Gamberoni, & Guaraldi, 1999), and a host of other clinical issues. In fact, just changing the time of day during which cancer chemotherapy is administered can nearly double the chances of survival in patients suffering from cancers with an estimated 30% to 40% 5-year survival rate, including childhood leukemia and colorectal carcinomas (Hrushesky, 1990, 1993, 1995; Kanabrocki et al., 2006; Lee & Balick, 2006; Levi, 1987, 1994, 2001, 2002; Takimoto, 2006). These findings, although largely correlated, point to a critical role

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for internal circadian timing in maintaining normal brain functioning and peripheral physiology. The two fundamental functions of the circadian system, internal organization and entrainment to the environment, are fundamental for optimal regulation of physiology and behavior. For each aspect of physiology and behavior, the circadian system sits upstream of a regulatory system, modulating the timing and synchronization of events. The mechanisms controlling circadian function—coordination of bodily systems and synchronization with the environment— represents the focus of the following sections. The Brain Clock Whereas a “master” circadian clock has been localized to the suprachiasmatic nucleus (SCN) located in the anterior hypothalamus in mammals (Figure 4.3; Moore & Eichler, 1972; Stephan & Zucker, 1972), it is now more appropriate to conceptualize the circadian system as an assembly comprised not only of a master clock, but also a series of subordinate clocks whose phase and coordinated activity is set by the SCN. As described in the following sections, the SCN has direct access to environmental time via retinal projections to the clock. Because subordinate central and peripheral clocks do not have access to such time cues, it is necessary for the SCN to communicate such information throughout the CNS and periphery. In addition to the core clock genes (CCGs) responsible for SCN and subordinate clock function, CCGs represent important output and local coordination systems. The stability of this hierarchical arrangement is necessary for normal body functioning and disease prevention. Numerous lines of evidence indicate that the master mammalian circadian clock is located in the SCN. The first

Figure 4.3 The mammalian circadian clock is located in the suprachiasmatic nucleus (SCN) of the anterior hypothalamus. Note. The SCN is pictured in this schematic of a coronal section through a rodent brain. The SCN is situated at the base of the brain directly above the optic chiasm (OC) and directly surrounding the third ventricle (V3). The sagittal schematic in the upper right corner depicts the approximate rostral-caudal location depicted in the coronal section.

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Biological Rhythms

indication that the SCN is the master clock comes from studies in which lesions ablating the SCN abolish circadian rhythmicity in adrenal corticoid secretion and locomotor behavior (Moore & Eichler, 1972; Stephan & Zucker, 1972). SCN-lesioned animals continue to show the full range of normal behaviors, but their temporal organization is lost and never recovers, irrespective of how early in development the lesions are performed (Mosko & Moore, 1979). The initial conclusion that the SCN serves as the master clock in the brain has been confirmed in the subsequent 30 years by converging lines of research involving in vivo, ex vivo, and in vitro studies carried out in many different laboratories. For example, transplants of donor SCN tissue into the brains of arrhythmic, SCN-lesioned hosts restore circadian rhythmicity in behavior (Lehman et al., 1987; Ralph et al., 1990). Importantly, rhythms are restored with the period of the donor SCN, indicating that the transplanted tissue does not act by restoring host-brain function but that the “clock” is contained in the transplanted tissue. Further evidence that clock function is contained within the SCN comes from studies demonstrating that circadian rhythms in neural firing rate persist in isolated SCN tissue maintained in culture (D. J. Green & Gillette, 1982; Groos & Hendriks, 1982; Shibata, Oomura, Kita, & Hattori, 1982). These studies confirmed that input from extra-SCN brain sites is not necessary for circadian rhythms in this nucleus (D. J. Green & Gillette, 1982; Groos & Hendriks, 1982; Shibata et al., 1982). In hypothalamic slice preparations, the SCN is intrinsically capable of sustaining not only circadian rhythms in neuronal firing rate, but also rhythms in glucose utilization and vasopressin secretion (Gillette & Reppert, 1987; D. J. Green & Gillette, 1982; Newman & Hospod, 1986). Primary cultures and organotypic explants of the rat SCN are similarly characterized by the distinctive capacity to generate circadian rhythms in vasopressin and vasoactive intestinal polypeptide (VIP) release for multiple cycles (Earnest & Sladek, 1987; Shinohara, Honma, Katsuno, Abe, & Honma, 1994; Watanabe, Koibuchi, Ohtake, & Yamaoka, 1993). Vasopressin and VIP rhythms produced by the same SCN explant are independently phased suggesting that these circadian rhythms may be generated by neurons that comprise two separable populations of oscillators within the SCN (Shinohara et al., 1994). An excellent overview of these studies in historical perspective is available (Weaver, 1998). The Molecular Clock Within a cell, circadian rhythms are produced by an autoregulatory transcriptional/translational negative feedback loop that takes approximately 24 hours (Box 4.1). While the general mechanism for circadian oscillations at the

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cellular level is common among organisms, the components comprising the feedback loop differ. For the purpose of clarity, only the core mammalian feedback loop is described. Earlier work proposed a core feedback loop that begins when two proteins, CLOCK and BMAL1, bind to one another and drive the transcription of messenger RNA (mRNA) of the Period (Per) and Cryptochrome (Cry) genes by binding to the E-box (CACGTG) domain on these gene promoters. Three Period (Per1, Per2, and Per3) and two cryptochrome genes (Cry1 and Cry2) have been identified. The mRNA for these genes is translated into PER and CRY proteins in the cytoplasm of the cell over the course of the day. Throughout the day, these proteins build up within the cytoplasm, and when they reach high enough levels, they form hetero- and homo-dimers. These newly formed dimers then feed back to the nucleus where they bind to the CLOCK:BMAL1 protein complex to turn off their own transcription (Figure 4.4).

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Figure 4.4 A simplified model of the intracellular mechanisms responsible for mammalian circadian rhythm generation. Note. The process begins in the cell nucleus when CLOCK and BMAL1 proteins dimerize to drive the transcription of the Per (Per1, Per2, and Per3) and Cry (Cry1 and Cry2) genes. In turn, Per and Cry are translocated to the cytoplasm and translated into their respective proteins. Throughout the day, PER and CRY proteins rise within the cell cytoplasm. When levels of PER and CRY reach a threshold, they form heterodimers, feed back to the cell nucleus, and negatively regulate CLOCK:BMAL1 mediated transcription of their own genes. This feedback loop takes approximately 24 hours, thereby leading to an intracellular circadian rhythm. From Kriegsfeld and Silver, 2006. Reprinted with permission.

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BOX 4.1 TRANSCRIPTION, TRANSLATION, AND POSTTRANSLATIONAL EVENTS Gene transcription is the process by which the sequence of nucleotides in a single strand of DNA is transcribed into a single strand of complementary RNA. Transcription factors bind to the beginning of the DNA sequence where the gene to be transcribed is located. In order to synthesize RNA, the two tightly twisted strands of DNA must be unraveled by enzymes called helicases. A gene consists of a unique linear sequence of DNA. Among eukaryotic organisms, some of the nucleotide sequences within the gene are noncoding sequences, called introns, which alternate with coding sequences, called exons. There are special markers denoting the start and end points of each gene. A distinct sequence of nucleotides, called a promoter or facilitory region, marks the start of the gene. The binding of a transcription factor to the promoter allows special enzymes, called RNA polymerases, to attach to the promoter and begin the process of RNA synthesis. The sequence of RNA nucleotides, determined by the sequence of nucleotides along the DNA, eventually determines the sequences of amino acids in the protein gene product of the specific gene in question. After transcription, enzymes clip out the intron sequences; then other enzymes splice together the remaining segments (exons) to form messenger RNA (mRNA). The mRNA leaves the cell nucleus, travels to the rough endoplasmic reticulum (RER), and serves as the template for translation into a linear sequence of amino acids, which occurs on ribosomes. After translation is completed, further processing occurs. The penultimate product is typically packaged into vesicles before being transported to the Golgi apparatus. Additional posttranslational processing usually occurs within the rough endoplasmic reticulum, the Golgi apparatus, and in the vesicles to yield the final version of the peptide/protein. More recently, it has become clear that the cellular clockwork is more complex, with a number of integrated feedback loops whose regulators are often, themselves, controlled by elements of the core clock mechanism. Two other promoter elements have emerged as important for circadian rhythm generation, DBP/E4BP4 binding elements (D boxes) and REV-ERB/ROR binding elements (RREs; Ueda et al., 2005). REV-ERB, an orphan nuclear receptor, negatively regulates the activity of the CLOCKBMAL1 complex and is also acted on by PER and CRY. Transcription of REV-ERB is controlled by the same mechanism controlling Per and Cry transcription. Similarly, the transcription factor DPB is positively regulated by the

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CLOCK:BMAL1 complex (Ripperger & Schibler, 2006) and acts as an important output mechanism by driving rhythmic transcription of other output genes via a PAR basic leucine zipper (PAR bZIP; Lavery et al., 1999). Whereas most work to date has focused on transcriptional regulation as the key mechanism driving cellular rhythms, posttranscriptional and posttranslational events are also critical for circadian coordination (Baggs & Green, 2003; C. Kramer, Loros, Dunlap, & Crosthwaite, 2003; Reddy et al., 2006). In addition to transcriptional/translational control of cellular clock function, regulatory kinases also play a pronounced role in regulation of circadian period. Over a decade ago, a circadian mutation called tau was identified that resulted in a shortened circadian period in Syrian hamsters (Ralph & Menaker, 1988). It is now known that the tau locus is encoded by casein kinase I epsilon (CKI; Lowrey et al., 2000; H. Wang, Ko, Koletar, Ralph, & Yeomans, 2007). In normal rodents, CKI phosphorylates PER and “tags” it for degradation throughout the day. Eventually, PER acts to overwhelm CKI, and dimerizes with CRY to feed back to the cell nucleus. The mutant form of CKI is unable to phosphorylate PER, leading to a short circadian period in tau mutant mice due to premature nuclear entry of PER:CRY dimers (Lowrey et al., 2000; Vielhaber, Eide, Rivers, Gao, & Virshup, 2000). The pronounced effects of single circadian gene mutations have been dramatized in a study identifying the genetic basis for a sleep abnormality in humans known as familial advanced sleep-phase syndrome (FASPS). In affected individuals, sleep onset occurs very early, around 19:30 hr, sleep duration is normal, and wake-up time is advanced to about 4:30 hr. This sleep disorder was found to be the result of a single point mutation in the CKI binding region of the PER2 gene, causing hypophosphorylation by CKI in vitro (Toh et al., 2001). Thus, as in the tau mutant, abnormal phosphorylation of PER protein by CKI likely leads to premature negative feedback of PER:CRY heterodimers, thereby speeding the “gears” of the circadian clock. A role for Per3 in human delayed sleep phase syndrome has also been reported (Archer et al., 2003). Whereas extraordinary progress has been made in uncovering the mechanisms responsible for clock function at the molecular level, the complexity of this mechanism is still not fully understood. Determining the specific interactions among complementary feedback loops will allow a further understanding of cellular clock function. Because downstream effects of CCGs are crucial in translating cellular clock function to physiological outcomes, it will be necessary to broaden our understanding of the means by which core clock mechanisms convey relevant timing information at a systems level.

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Ubiquity of Circadian Clocks The genes regulating circadian rhythmicity and their protein products have been found in numerous sites, including extra-SCN brain loci and in the periphery (Abe et al., 2002; Balsalobre, Damiola, & Schibler, 1998; Kriegsfeld, Korets, & Silver, 2003; Yamazaki et al., 2000). These findings led to questions regarding the unique nature of the master oscillator in the SCN, the functional significance of extra-SCN oscillators, and mechanisms of coordination of these widely dispersed clocks. As described in the following sections, the SCN serves to coordinate cellular oscillators throughout the CNS and periphery. Without a master clock, all other timing systems cease to function as independent cellular oscillators in target systems lose their combined coordination. The loss of coherent rhythmicity in the SCN or peripheral tissue can be due to dampening of rhythms in individual cells or to loss of synchrony among a population of cells in the tissue. Use of Per1-luciferase reporter animals indicates that rhythms in peripheral tissues damp then disappear over time due to uncoupling (desynchronization) among oscillators that retain their individual rhythms (Nagoshi et al., 2004; D. Welsh, 2004). Presumably, peripheral clock cells normally get phase information from the SCN to synchronize individual oscillators to each other. In this view, the SCN sets the phase of peripheral circadian clocks daily, coordinating the activity of tissues and organs of the body relative to one another, thereby maintaining homeostasis. However, the phase of peripheral clocks is also significantly influenced by the daily food intake schedule (e.g., Stokkan, Yamazaki, Tei, Sakaki, & Menaker, 2001). In contrast to hamsters, which show strong circadian organization of nocturnal feeding, common voles (Microtus arvalis) feed throughout the day with an ultradian rhythm of 2 to 3 hours. In voles, the clock-gene mRNAs display high circadian amplitudes in the SCN, but display virtually no cyclicity in the liver (van der Veen et al., 2006). Entrainment of the Circadian System As suggested previously, in order to be adaptive for an organism, circadian rhythms must be synchronized to local environmental time. In addition to a direct visual pathway from retinal ganglion cells to the visual cortex, there is also a direct retinohypothalamic tract (RHT) projecting from the optic nerve to the SCN (Klein & Moore, 1979; Moore & Klein, 1974; Figure 4.5). This second visual pathway is necessary and sufficient to entrain (synchronize) the SCN to the environmental light/dark cycle. If the primary visual pathway is transected at the level of the optic tract beyond the optic chiasm (i.e., caudal to the SCN), then the animal

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Figure 4.5 The visual pathway mediating entrainment in mammals. Note. Light stimulates intrinsically photosensitive ganglion cells within the eyes that convey illumination information directly to the SCN of the hypothalamus. This pathway is separate from the classical visual system and uses melanopsin as the light-sensitive photopigment.

is visually blind, but the circadian system continues to respond to photic cues by synchronizing the animal to the light/dark cycle (Klein & Moore, 1979). These studies demonstrate the route whereby environmental photic information can reach the SCN. However, the specific retinal photoreceptor responsible for transmitting this signal was initially enigmatic, as entrainment was independent of traditional, image-forming photoreceptors; mice lacking both rod and cone photoreceptors (rd/rd) exhibit grossly normal entrainment even though they are visually blind (Foster et al., 1993; Freedman et al., 1999; Lucas, Freedman, Munoz, Garcia-Fernandez, & Foster, 1999; Van Gelder, 2001). These findings led to a search for a novel nonrod/noncone photoreceptor. Several years of rapid discovery beginning in this millennium identified a subset of light-responsive ganglion cells containing the photopigment melanopsin (Berson, Dunn, & Takao, 2002; Hannibal & Fahrenkrug, 2002). These ganglion cells project directly to the SCN and were initially thought to be the sole photoreceptors necessary for entrainment. However, melanopsin deficient mice exhibit only minor impairments in entrainment (Lucas et al., 2003; Panda et al., 2002; Ruby et al., 2002). This discrepancy was resolved by showing that entrainment is abolished in mice doubly mutant for both melanopsin and traditional rod/cone photoreceptors (Hattar et al., 2003; Panda et al., 2003). These findings suggest that traditional rod/cone photoreceptors project to

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specialized light-responsive ganglion cells that then transmit this integrated photic information directly to the SCN. Thus, these two types of receptors likely work together to entrain the circadian clock, and either receptor type can support entrainment in the absence of the other.

Circadian Output and the Control of Behavior Diffusible SCN Output Early work in which SCN grafts were shown to restore circadian patterns in activity-related behaviors suggested that circadian rhythmicity could be supported by diffusible output from the clock (Lehman et al., 1987; Ralph et al., 1990; Silver, Lehman, Gibson, Gladstone, & Bittman, 1990). This supposition was based on the fact that transplants restored circadian function independent of the establishment of neural SCN connections with the host brain. This possibility was demonstrated definitively by encapsulating donor SCN tissue in a membrane that prevented neural outgrowth while allowing the diffusion of signals between graft and host; behavioral rhythms were still restored under these conditions (Silver, LeSauter, Tresco, & Lehman, 1996). One candidate diffusible signal is prokineticin-2 (PK2; Cheng et al., 2002). This protein is expressed rhythmically in the SCN and its receptor is present in all major SCN targets (Cheng, Bittman, Hattar, & Zhou, 2005; Cheng et al., 2002). Likewise, PK2 administration during the night (when levels are low) inhibits wheel-running behavior. Whether this signal normally operates in a diffusible manner and/or is released synaptically requires further examination. A second candidate diffusible signal is transforming growth factor-alpha (TGF-alpha; A. Kramer et al., 2001). As with PK2, TGF-alpha is expressed rhythmically in the SCN and its administration inhibits wheel-running behavior. The receptor for TGF-alpha is also expressed in the subparventricular zone (SPVZ), the major target of the SCN. Again, the degree that TGF-alpha is released in a diffusible manner under normal conditions requires further study. Studies in which fiber output is eliminated from the SCN (allowing only for diffusible output), in conjunction with administration of PK2 and TGF-alpha antagonists, are necessary to begin to answer this question. Although it is intriguing to speculate on the role of these signals in communicating information from the SCN, the problem of unequivocally identifying an endogenous, physiologically relevant diffusible SCN signal is complex. The necessary and sufficient criteria to confirm the existences of a diffusible signal in a fluid volume have been summarized previously (Nicholson, 1999). First, evidence that the removal or replacement of the signaling substance results in a change in the response being controlled and an

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assay of the substance should indicate that it is present or increases, or both, in a well-defined temporal relationship to the response (and similarly declines when the response disappears). In addition, evidence must be obtained that a fluid compartment is the conduit for a diffusible or transported signal. The signal must have access to and enter the compartment where the fluid dynamics and turnover in the compartment should allow appropriate movement of the signal. Although PK2 and TGF-alpha meet some of these criteria, further research is necessary to clarify the role of these molecules in communicating circadian information. Neural Control of Neurosecretory Factors In contrast to behavioral rhythms (e.g., locomotion, drinking, gnawing), endocrine rhythms require neural projections from the SCN to endocrine targets; endocrine rhythms are abolished after knife cuts severing SCN fibers (Hakim, DeBernardo, & Silver, 1991; Nunez & Stephan, 1977) and are not restored in SCN-lesioned transplanted animals (Meyer-Bernstein et al., 1999; Nunez & Stephan, 1977; Silver et al., 1996), presumably due to inadequate neural innervation of the host brain by the graft. Further evidence for a neural SCN output signal regulating hormone secretion is seen in studies of female hamsters. When housed in constant light, the activity of a subset of hamsters “splits” into two separate activity bouts within a 24-hour interval. These split females display two daily preovulatory LH surges, each approximately half the concentration of a single surge in a nonsplit female (Swann & Turek, 1985). Under normal conditions, both halves of the bilaterally symmetrical SCN are active in synchrony. In ovariectomized, estrogen-implanted split hamsters examined during one of their activity bouts, however, activation of the SCN occurs on one side of the brain (monitored by expression of the early immediate gene FOS), but not on the other, suggesting that each half of the SCN can control an activity bout (de la Iglesia, Meyer, Carpino, & Schwartz, 2000). Remarkably, FOS activation in GnRH neurons was only seen on the side of the brain in which SCN FOS expression occurred (de la Iglesia et al., 2000; de la Iglesia, Meyer, & Schwartz, 2003). These findings suggest that the precise timing of the LH surge is derived from a neural signal originating in the SCN and communicated to ipsilateral GnRH neurons, as a diffusible output signal would reach both sides of the brain. Importantly, some hypothalamic sites are activated ipsilaterally, while others are activated either ipsilaterally or bilaterally in the split animal, again supporting the notion of multiple SCN output pathways (Yan, Foley, Bobula, Kriegsfeld, & Silver, 2005). Neural output from the SCN has been extensively investigated in rats and hamsters using tract-tracing techniques (Kalsbeek, Teclemariam-Mesbah, & Pevet, 1993;

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Kriegsfeld, Leak, Yackulic, LeSauter, & Silver, 2004; Leak & Moore, 2001; Morin, Goodless-Sanchez, Smale, & Moore, 1994; Stephan, Berkley, & Moss, 1981; Watts & Swanson, 1987). Importantly, many of these monosynaptic projections target brain regions containing neuroendocrine cells producing hypothalamic-releasing hormones. Direct projections have been traced from the SCN to the medial preoptic area (MPOA), supraoptic nucleus (SON), anteroventral periventricular nucleus (AVPV), the paraventricular nucleus (PVN), the dorsomedial nucleus of the hypothalamus (DMH), and the lateral septum and the arcuate (Arc). The SCN also projects to the pineal through a multisynaptic pathway (Klein, 1985; Klein et al., 1983). There is abundant evidence for direct neural SCN control of neuroendocrine cell populations (Buijs, Hermes, & Kalsbeek, 1998; Buijs, van Eden, Goncharuk, & Kalsbeek, 2003; Egli, Bertram, Sellix, & Freeman, 2004; Gerhold, Horvath, & Freeman, 2001; Horvath, Cela, & van der Beek, 1998; Kalsbeek & Buijs, 2002; Kalsbeek, Fliers, Franke, Wortel, & Buijs, 2000; Kalsbeek, van Heerikhuize, Wortel, & Buijs, 1996; Kriegsfeld, Silver, Gore, & Crews, 2002; Van der Beek, Horvath, Wiegant, Van den Hurk, & Buijs, 1997; Van der Beek, Wiegant, Van der Donk, Van den Hurk, & Buijs, 1993; Vrang, Larsen, & Mikkelsen, 1995). Because these cell populations can regulate neurochemicals that are secreted into the CSF (Reiter & Tan, 2002; Skinner & Caraty, 2002; Skinner & Malpaux, 1999; Tricoire, Moller, Chemineau, & Malpaux, 2003) or general circulation, SCN-derived signals can control widespread systems in the brain and body. Considered together, the findings summarized previously suggest several possibilities. For example, behavioral rhythms may be controlled by a diffusible signal(s), whereas endocrine rhythms may require neural output. Alternatively, behavioral and endocrine rhythms can both be supported by diffusible signals, but the threshold for supporting behavioral rhythms is lower. Finally, behavioral rhythms are controlled by both neural and diffusible signals, and either can maintain rhythmic function, while endocrine rhythms can only be supported via neural connections. Definitive identification of biologically significant endogenous diffusible signal(s) and the precise mode of SCN control is a current line of inquiry.

SEASONAL CHANGES IN BRAIN AND BEHAVIOR In common with daily fluctuations in energy availability, intake, and requirements, energy availability and requirements vary throughout the year, and individuals must parse their various activities across the year to maximize energy

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utilization and survival. Thus, all energetically expensive activities (e.g., mating, migrating, foraging, nest building, and thermoregulation) cannot be simultaneously performed. To allow anticipation of annual fluctuations in energy conditions and synchronization of their behavior and physiology accordingly, individuals have evolved mechanisms to determine time of year. In common with the circadian system, seasonal timekeeping mechanisms permit the coordination of internal physiology, and also allow animals to predict recurring seasonal events, such as food availability, general weather conditions, and predator activity, and adjust their behavior appropriately. Seasonal Changes in Reproductive Function Whereas many seasonal changes in behavior have been documented, breeding seasons represent the most salient seasonal change in behavior. In addition to mating behaviors, other associated behaviors such as food intake, aggression, and territorial defense show marked seasonal fluctuations. In general, small animals tend to breed during the long days of spring and summer, whereas large animals with relatively extended periods of gestation, breed during the short days of autumn. In both cases, offspring are produced in the spring when food availability is at a seasonal maximum. The specific timing of breeding represents a selective compromise among competing trade-offs such as the availability of food for gestation or lactation. Syrian hamsters (Mesocricetus auratus) have been studied as the exemplar of a long-day breeder. Individuals of this species reduce circulating concentrations of reproductive steroid hormones, LH, and follicle-stimulating hormone (FSH), after exposure to simulated winter day lengths or appropriate melatonin treatment (Bartness, Powers, Hastings, Bittman, & Goldman, 1993; Swann & Turek, 1988). In sheep, the most commonly studied short-day breeder, exposure to short days leads to increased LH secretion, manifested as an increase in the frequency of pulsatile LH secretion (Lehman et al., 1997). Such changes in pituitary gonadotropin secretion lead to changes in gonadal growth and gonadal steroid hormone secretion and the subsequent onset of mating behavior during the autumn. Circulating testosterone concentrations decrease rapidly in response to the short day via enhanced negative feedback effects on gonadotropin-releasing hormone (GnRH) secretion in male rodents (Turek, 1977). Short-day male rodents display lower circulating testosterone concentrations compared with long-day males. Because testosterone modulates many behaviors (in addition to reproduction), this hormone is another potential mediator of seasonal adjustments in the brain, which could act alone or in combination with melatonin or other hormones. Examples of testosterone affecting

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seasonal behaviors include the combined effect of testosterone and photoperiod on locomotor activity in male Syrian hamsters (Ellis & Turek, 1983) and the reinstatement by exogenous testosterone of female odor preference in castrated male meadow voles maintained in long or short days (Ferkin & Gorman, 1992). Testosterone replacement prevents elevated food intake in castrated male deer mice (Blank, Korytko, Freeman, & Ruf, 1994) and restores aggressive behaviors in Syrian hamsters maintained in short days (Jasnow, Huhman, Bartness, & Demas, 2000). Although no correlation has been reported between testosterone concentrations and spatial learning and memory among male meadow voles (Galea, Kavaliers, Ossenkopp, & Hampson, 1995), short days reduce testosterone, hippocampal volume, and spatial learning in deer mice (Perrot-Sinal, Kavaliers, & Ossenkopp, 1998). However, direct manipulation of testosterone in seasonally breeding rodents and the potential interaction between photoperiod and testosterone has rarely been investigated in learning and memory paradigms (Pyter, Trainor, & Nelson, 2006). Testosterone increases the volume of song-production brain regions in songbirds (Ball et al., 2004) and altered neurochemistry (Bittman, Tubbiola, Foltz, & Hegarty, 1999) and neuroanatomy (Gomez & Newman, 1991) in photoperiodic rodents. For example, testosterone increases the number of cells expressing the proopiomelanocortin gene in the hypothalamus (Bittman et al., 1999) and restores neuronal branching and morphology within the amygdala (Gomez & Newman, 1991) of castrated Syrian hamsters. Steroid hormones have important effects on learning and memory although there are species differences. Despite the presence of androgens and estrogens in both males and females, for the most part studies on males have focused on the role of androgens whereas studies on females have focused on the role of estrogens. In addition to manipulating photoperiod to induce seasonal changes in brain and behavior, studies have mimicked similar seasonal results by manipulating melatonin, the physiological signal into which ambient photoperiod is transduced (Bartness et al., 1993; Carter & Goldman,

Figure 4.6 Input pathway from the retina to the pineal gland in mammals.

Pineal

SCN

PVN

OC

Eye SCG

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1983; Nelson, Badura, & Goldman, 1990). The synthesis of melatonin occurs exclusively at night and is inhibited directly by light. The duration of melatonin release is proportional to the length of the dark phase (Illnerova, Hoffmann, & Vanecek, 1984); consequently, short-day animals experience longer durations of melatonin than do long-day animals. Infusion of appropriate duration profiles of physiological concentrations of melatonin to pinealectomized hamsters has shown the critical role of melatonin duration in signaling day length (Bartness et al., 1993). Peromyscus and other small rodents, whose gestations are relatively short (about 3 weeks), are long-day breeders and respond reproductively to long-day (short-duration) melatonin signals. Melatonin affects circadian rhythms of behavior (Golombek, Pevet, & Cardinali, 1996), but also provides seasonal information throughout the body. Melatonin receptors are distributed discretely throughout the rodent brain (Drew et al., 2001; Dubocovich, Rivera-Bermudez, Gerdin, & Masana, 2003; Weaver, Carlson, & Reppert, 1990). Importantly, melatonin receptors are present in the hippocampal area (entorhinal cortex) and other nonreproductive regions of rodent brains (Musshoff, Riewenherm, Berger, Fauteck, & Speckmann, 2002; Weaver et al., 1990), providing a neural substrate for direct effects of melatonin on learning and memory and other cognitive and motivated behaviors (described next). Manipulation of melatonin duration to mimic long or short days induces seasonal changes in behavior. Removal of the source of melatonin via pinealectomy impairs photoperiodic responses (Bartness & Goldman, 1989; Goldman, 2001). The neural mechanisms regulating circadian changes in melatonin secretion have been well characterized (Figure 4.6). Circadian rhythms in melatonin secretion in most mammals depend on neural efferents from the SCN to the region of the paraventricular nucleus of the hypothalamus (PVN). This projection continues through the medial forebrain bundle to the superior cervical ganglion (SCG) of the spinal cord. From the SCG, sympathetic neurons drive pineal melatonin secretion during the dark, while melatonin production and secretion are inhibited during the light

Note. Light information is transduced into a neural signal in the retina and transmitted via a direct retino-hypothalamic tract to the suprachiasmatic nucleus (SCN). From the SCN, fibers synapse in the paraventricular nucleus of the hypothalamus (PVN). From the PVN, fibers travel through the medial forebrain bundle to the superior cervical ganglion (SCG). Postganglionic fibers from the SCG then project to the pineal gland to modulate melatonin production/secretion. OC  Optic chiasm.

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portion of the LD cycle (Cassone, 1990; Ganguly, Coon, & Klein, 2002). This multisynaptic pathway has been confirmed using the transneuronal retrograde tracer, pseudorabies virus, injected into the pineal gland (Card, 2000; Larsen, Enquist, & Card, 1998). This technique confirmed the links in the pathway from the SCN to the pineal gland, and also suggested that two parallel circuits from the SCN (one from the dorsomedial and one ventrolateral) likely drive melatonin secretion. This day-night regulation of melatonin is regulated by the SCN (for a review, see Cassone, 1990), and, as with other hormonal systems mentioned previously, lesions of the SCN abolish circadian rhythms in melatonin production and secretion (Scott, Jansen, Kao, Kuehl, & Jackson, 1995; Tessonneaud, Locatelli, Caldani, & Viguier-Martinez, 1995). Although melatonin influences GnRH secretion, this hormone does not appear to act directly on GnRH neurons. Given the widespread distribution of the GnRH system, it has been difficult to determine the melatonin-sensitive systems that, in turn, act on GnRH neurons to regulate their activity. Because regression of the reproductive axis is, in part, due to melatonin-mediated increases in the negative feedback of sex steroids, those brain regions co-expressing melatonin receptors and androgen receptors may be critical in this response. The dorsomedial hypothalamus (DMH) of Syrian hamsters binds both melatonin and androgen with high affinity (Maywood, Bittman, & Hastings, 1996). Lesions of the DMH block short-day and melatonin-induced regression of the reproductive system (Lewis, Freeman, Dark, Wynne-Edwards, & Zucker, 2002; Maywood et al., 1996), suggesting that the DMH is a key target of melatonin in this species. In other species such as Siberian hamsters, melatonin feedback to the SCN is critical for melatonin-induced seasonal alterations in several physiological parameters. Lesions of the SCN block the effects of daily, long-duration melatonin infusions (i.e., short-day pattern) on body mass, fat pad distribution, and reproductive function (Bartness, Goldman, & Bittman, 1991; Bittman, Bartness, Goldman, & DeVries, 1991). This finding suggests that the circuit beginning with the SCN also requires the SCN as a target. In addition to acting on the DMH, melatonin binding is largely seen in the pars tuberalis of seasonally breeding mammals (Bittman & Weaver, 1990; Weaver et al., 1990). In hypothalamic-pituitary transected sheep, melatonin implants in region of the pars tuberalis reduce prolactin secretion in a manner similar to short-day lengths (Lincoln & Clarke, 1997). However, melatonin implants in this region do not affect gonadotropin secretion, suggesting that this region may be important for the regulation of the lactotropic, but not gonadotropic, photoperiodic effects (Lincoln & Clarke, 1997). Research on the means by which melatonin regulates other parameters is still in its infancy.

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It is unclear whether these melatonin-sensitive targets project directly on the GnRH system or indirectly via projections to other peptidergic systems. One candidate intermediary system is kisspeptin, a peptide recently shown to have marked stimulatory effects on the GnRH system. After chronic exposure to short days, Siberian hamsters with suppressed reproductive function exhibit marked reduction in kisspeptin cell labeling in the anteroventral periventricular nucleus, a neural target of the SCN (Greives et al., 2007; Mason et al., 2007). In common with other species, a subset of individual Siberian hamsters fails to respond to day-length information, however, and maintains their reproductive function. These so-called photoperiod nonresponsive individuals exhibit kisspeptin expression akin to that of long-day animals, suggesting that short photoperiods (and melatonin) are ignored by the brains of these animals. These results suggest an important role for kisspeptin in coordinating and relaying environmentally relevant information to the reproductive axis as well as a role for this peptide in regulating seasonal changes in reproductive function (Revel et al., 2006, 2007). A number of studies suggest a role for clock genes in the control of seasonality (Hofman, 2004; Johnston et al., 2003; Lincoln, Andersson, & Loudon, 2003). In Syrian and Siberian hamsters, photoperiod alters the duration of clock and clock-controlled gene expression, while the amplitude of gene expression is influenced by photoperiod in the pars tuberalis (Johnston et al., 2003; Messager, Hazlerigg, Mercer, & Morgan, 2000). In sheep, however, the relative timing of clock genes is altered by photoperiod in the pars tuberalis, providing a mechanism of temporal encoding and downstream control (Hazlerigg, Andersson, Johnston, & Lincoln, 2004; Lincoln et al., 2003; Lincoln, Johnston, Andersson, Wagner, & Hazlerigg, 2005; Lincoln, Messager, Andersson, & Hazlerigg, 2002). These correlational results are intriguing and suggest that phase and/or amplitude of clock and clock-controlled genes in SCN brain targets and endocrine glands may predict their responsiveness to upstream signals on a daily schedule. Seasonal Changes in the Avian Song System Associated with seasonal breeding are activities related to reproduction such as territorial defense, migration, or communication. For example, male canaries sing more frequently in spring than in winter, and they appear to lose components of their songs after each breeding season and incorporate new song components each spring (Brenowitz & Beecher, 2005). In spring, as day length increases, the testes grow and secrete androgens, song frequency increases, song repertoire enlarges, and the higher vocal center (HVC) and the robust nucleus of the archistriatum (RA), two brain

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nuclei necessary for song production, double in size. Under autumnal day lengths, testes regress in size and androgen production virtually stops, frequency of singing decreases, song repertoire shrinks, and the HVC and RA regress in size (Nottebohm, 2005). Treatment with testosterone in autumn mimics spring hormonal conditions and supports song production. The seasonal plasticity in behavior has been assumed to reflect the seasonal changes in brain morphology induced by hormonal adjustments (Tramontin & Brenowitz, 2000), although the precise mechanisms underlying seasonal plasticity in song complexity remains unspecified (Brenowitz, Lent, & Rubel, 2007). Estrogens, converted from testicular androgens or produced de novo in CNS neurons (Schlinger, Soma, & London, 2001), appear necessary to activate the neural mechanisms underlying the song system in birds. Androgens enter neurons containing aromatase, which converts them to estrogens. Aromatase is generally localized in neurons adjacent to other neurons containing estrogen receptors in the hypothalamus and preoptic area of songbird brains, as well as in limbic structures and in the structures constituting the neural circuit controlling bird song. The brain appears to be the primary source of estrogens, which activate masculine behaviors in many bird species (London, Monks, Wade, & Schlinger, 2006). Photoperiod is important in some birds to mediate these changes, including recruitment of new neurons (Nottebohm, 2005). Testosterone, or its metabolites including estrogens, appears to drive the seasonal changes in brain structure and behavior. In addition to seasonal changes in singing behavior, substantial seasonal changes occur in the morphology of several song nuclei. For example, the volume of the HVC and RA increases by 99% and 77%, respectively, among male canaries maintained under spring day lengths (12 hours light/day) relative to birds in autumnal conditions ( 12 hours light/day; Nottebohm, 1981). Similar results have been reported for more than 25 other bird species (reviewed in Ball, Riters, & Balthazart, 2002). These seasonal changes in the size of specific brain structures are probably mediated by testosterone or its metabolites (Ball et al., 2002; London et al., 2006). Testosterone appears to act via brain-derived neurotrophic factor (BDNF) to promote survival of new neurons in the brains of adult songbirds (Rasika, Alvarez-Buylla, & Nottebohm, 1999). Testosterone upregulates BDNF in the HVC of adult male and female canaries and infusion of antibody against BDNF blocks androgen-induced neurogenesis. Behavioral feedback is important because singing increases BDNF expression in the HVC in proportion to the number of songs produced (Li, Jarvis, Alvarez-Borda, Lim, & Nottebohm, 2000). BDNF seems to determine life expectancy of newly born cells in adult songbird brains.

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Vernal elevation in testosterone concentrations coincident with long days provokes BDNF production that protects new neurons within 10 days of cell birth (Alvarez-Borda, Haripal, & Nottebohm, 2004). Other avian models of seasonal brain plasticity include seasonal change in hippocampal volume of food-caching (Hoshooley & Sherry, 2007; Smulders, Sasson, & DeVoogd, 1995) and brood parasitic (Sherry, Forbes, Khurgel, & Ivy, 1993) birds. The hippocampus is involved in spatial learning and memory, and generally, species with a larger relative hippocampal volume display better spatial learning and memory (Sherry, Jacobs, & Gaulin, 1992). Hippocampal size is reduced during the winter in these bird species when spatial learning and memory performance associated with food storing (Barnea & Nottebohm, 1994, 1996) and nest parasitism (Sherry et al., 1993) is reduced. Comparable studies of seasonal brain plasticity in mammals have been relatively rare, despite the fact that seasonal breeding has been well-documented in nontropical mammals. Seasonal Changes in Mammalian Brain and Behavior Although the brain constitutes only about 2% to 3% of the total body mass in rodents, it consumes over 10% of total energy expenditure (Mink, Blumenschine, & Adams, 1981). Thus, minor reductions in brain mass could save significant energy (Jacobs, 1996). Seasonal changes in brain weight have been documented in rodents and shrews (Yaskin, 1984). Brain weights are higher in summer than winter (Yaskin, 1984). Winter brain mass is reduced in grey squirrels (Sciurus carolinensis; Lavenex et al., 2000b) and ferrets (Mustela putorius; Weiler, 1992). Also, brain mass (absolute and corrected for body mass) and specific brain regions (e.g., hippocampus) are reduced during winter in common shrews (Sorex araneus) and bank voles (Clethrionomys glareolus; Yaskin, 1984). A significant part of the seasonal change in brain weight might merely reflect differences in water content; however, the neocortex and the basal portion of the brains (i.e., the corpus striatum) of rodents and shrews show seasonal cytoarchitectural changes. Seasonal changes in hippocampal morphology have been reported in hibernating ground squirrels (Citellus undulatus; Popov, Bocharova, & Bragin, 1992) and meadow voles (Microtus pennsylvanicus; Galea & McEwen, 1999), although hippocampal volume did not vary with season in grey squirrels (Lavenex, Steele, & Jacobs, 2000). In European hamsters (Cricetus cricetus) and sheep, seasonal changes in the innervation of the brain have been reported (Buijs et al., 1986; Xiong, Karsch, & Lehman, 1997). Seasonal changes in neuroendocrine function (Wehr, 1998), hypothalamic peptide expression (Hofman & Swaab,

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1995), and serotonin function (Alstadhaug et al., 2005; Brewerton & George, 1990) have also been reported in humans. Seasonal changes in human behavioral pathology are also observed in anxiety and depression (Enns et al., 2006; Lewy, Lefler, Emens, & Bauer, 2006; Rosenthal et al., 1984; Sigmon et al., 2007), migraine headaches (Alstadhaug et al., 2005; Brewerton & George, 1990), as well as incidence, severity, and mortality of strokes (Carolei, Marini, De Matteis, Di Napoli, & Baldassarre, 1996; H. Wang, Sekine, Chen, & Kagamimori, 2002; Y. Wang et al., 2003). Thus, despite the relative lack of seasonal organization of reproductive function, it is apparent that humans retain responsiveness to photoperiod (reviewed in Bronson, 1995, 2004), and that photoperiod-mediated adjustments in rodent brain and behavior may be important to understand seasonal changes in human brain and behavior. Photoperiod also affects cell division in the dentate gyrus and subependymal zone of adult mammals (Huang, DeVries, & Bittman, 1998). Long-term exposure to short days in Syrian hamsters doubles the number of new neurons produced in these brain regions, as well as in the hypothalamus and cingulate-retrosplenial cortex (Huang et al., 1998). There are no appreciable photoperiodic differences in brain volume of either the granule cell layer of the hippocampus or the dentate. These results are in contrast to studies of Peromyscus which likely represents a species difference. No differences in cell proliferation are observed in brains of grey squirrels (Lavenex et al., 2000), although the use of low doses of BrdU may not pick up differences (Gould & Gross, 2002). Motoneurons controlling penile muscles, neuromuscular junctions, and somas are smaller in adult male Siberian hamsters and white-footed mice exposed to short days compared with animals exposed to long days (Forger & Breedlove, 1987). Adult male deer mice (Peromyscus maniculatus) maintained in short days have smaller brains and lower adjusted hippocampal volume relative to those maintained in long days (Perrot-Sinal et al., 1998). Thus, several examples of seasonal influences on several brain measures exist, but what is lacking is an understanding of how seasonal factors influence neural structures in mammals. It is likely that seasonal brain changes will be subtle in mammals compared to birds because birdsong generally displays an all-or-none seasonality; seasonal changes in learning and memory are consistent, albeit subtle, and such variation likely provides important grist for evolutionary processes. Seasonal Changes in Learning and Memory Deer mice and white-footed mice tested during the breeding season display better spatial learning performance compared to mice tested during the nonbreeding season (Galea,

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Kavaliers, Ossenkopp, Innes, & Hargreaves, 1994; Pyter, Reader, & Nelson, 2005). This may represent differences in territory size, and therefore spatial memory requirements (Jacobs, 1996). The relationship among home range size and spatial learning and memory has been established in several rodent species. For example, male Peromyscus maniculatus have larger home ranges than females during the breeding season that decline in winter (Bronson, 1985, 1988). This is paralleled by superior spatial learning and memory performance in male Peromyscus as compared to females in long, but not short, days (Galea, Kavaliers, & Ossenkopp, 1996; Galea et al., 1994). The effects of photoperiod on learning and memory are consistent with a life-history explanation of reproductive strategy. Both hippocampal size and spatial learning and memory performance are sexually dimorphic among polygynous rodents (Jacobs, 1996). Polygynous male rodents outperform females in spatial tasks and have larger hippocampi (Galea et al., 1994, 1996; Jacobs, 1996). In common with birds, spatial learning and memory performance is often positively correlated with hippocampal size in mammals (Jacobs, Gaulin, Sherry, & Hoffman, 1990; Sherry, Jacobs, & Gaulin, 1992). In food caching birds, one might predict that hippocampal volume increases during winter (Pravosudov & Clayton, 2002), and that caching behaviors and hippocampal volume should increase among individuals inhabiting particularly harsh conditions. Indeed, black-capped chickadees (Poecile atricapillus) from the most harsh conditions (i.e., exposed to lowest temperatures, shortest day lengths, and most snow cover) had the largest hippocampal volumes and most hippocampal neurons compared to birds from mild conditions (Roth & Pravosudov, 2008). Thus, environmental conditions can shape specific brain structures in precise ways to enhance survival and reproductive success. Seasonal Changes in Aggression It is well documented that androgens regulate aggressive behavior, especially the dramatic male-male interactions associated with mating territories. For instance, many male mammals and birds set up and defend territories before the onset of the breeding season (Goymann, Landys, & Wingfield, 2007; Wingfield, Jacobs, & Hillgarth, 1997). Females often choose males on the basis of resources available in these territories so evolutionary pressures are high on males to compete successfully. Aggressive behavior is costly, both in terms of energy and in terms of potential injury or death. Thus, individuals often exchange information about the likely outcome of the aggressive encounter without actual combat. The energetic costs and survival from wounds vary seasonally leading to fluctuations in

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the likelihood of aggressive encounters as the cost-benefit ratio changes. Individuals of several rodent species undergo a seasonal shift from highly territorial, asocial behavior during the breeding season to a social, highly interactive existence during winter. Such species typically undergo reproductive quiescence at the end of the breeding season in response to short days. The resulting decrease in androgen secretion may be necessary or permissive for the seasonal shift in sociality, but in wood rats (Neotoma fuscipes) nonsteroidal mechanisms mediate the seasonal change in social behavior (G. S. Caldwell, Glickman, & Smith, 1984). The seasonal change in social organization confers several advantages. During the breeding season, rodents control resources that promote their own survival and that of their offspring, and they often aggressively exclude nonkin from access to resources. During the winter, however, this strategy is abandoned in favor of group living that conserves energy and enhances survival in the face of low temperatures and reduced food availability. Many species of rodents conserve energy during the winter by forming aggregations of huddling animals (Madison, 1984). In these aggregations, different sexes and even different species commingle. Even in the absence of huddling behavior, animals may tolerate one another better in close quarters during the winter than during the breeding season. For example, male meadow voles (Microtus pennsylvanicus) are highly territorial in the spring and summer and occupy open meadows, whereas red-backed voles (Clethrionomys gapperii) breed in forest habitats. During the winter months, meadow voles migrate into the spruce forest habitats occupied by the redbacked voles, presumably to take advantage of the protective cover provided by the trees. In some cases, they share nests with individuals of other rodent species (Madison, 1984). Consistent for a role of androgens mediating both mating and aggression, male meadow voles trapped during the winter and tested in paired encounters in a neutral arena displayed less interspecific aggression than voles trapped in summer. The winter reduction in aggressiveness permits energy-saving group huddling. As the animals enter their breeding condition in the spring, they reestablish mutually exclusive territories. Some males within a population do not undergo reproductive regression, maintaining testicular function and producing sperm and androgens during simulated winter conditions (for review, see Prendergast, Kriegsfeld, & Nelson, 2001). The advantages of continuous breeding capability evidently incur substantial hidden costs because only a minority of each population adopts this strategy. One such cost may be that reproductively competent males, because of unusual aggressiveness during the winter, are unable to participate in communal huddling and thus incur greater energetic costs in overwintering.

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High behavioral and energetic costs associated with the maintenance of the reproductive system in winter may explain why nonregressive types do not normally predominate in temperate or boreal-zone populations of rodents (Prendergast et al., 2001). This observation is supported by a field study of winter nesting behavior of prairie voles (M. ochrogaster; McShea, 1990). Most voles in the population studied were reproductively inactive during the winter and formed groups of huddling individuals. Two males, however, remained in breeding condition and never huddled with other animals. In pairwise aggression tests, these two males were much more aggressive than reproductively quiescent individuals. In another study, reproductive status also influenced odor preferences of meadow voles maintained in simulated winter day lengths (Gorman, Ferkin, Nelson, & Zucker, 1993). Males that retained reproductive capability in winter day lengths preferred the odors of females that also failed to inhibit reproduction during short days. This preference may facilitate the sporadic occurrences of winter breeding frequently reported for this species (reviewed in Nelson, 1987). As noted, many if not most species reduce aggression outside of the breeding season. For some species, however, aggression must be maintained throughout the year independent of breeding season. For example, over-wintering migrant birds must compete with residents within local feeding flocks. In such cases, aggression would likely be modulated by mechanisms unrelated to reproduction. In hamsters, short days increase male resident-intruder aggression (P. sungorus; Demas, Polacek, Durazzo, & Jasnow, 2004; Wen, Hotchkiss, Demas, & Nelson, 2004; M. auratus; H. K. Caldwell & Albers, 2004; Garrett & Campbell, 1980; Jasnow et al., 2000). This effect is paradoxical because increased aggression occurs when testosterone concentrations are at a nadir. Despite the lack of plasma androgens, estrogens may still be important (see the following discussion). Adrenalectomy prevents increased aggression in short days in Siberian hamsters (Demas et al., 2004). This mechanism may be involved in winter aggression of birds. Studies of zebra finches (Taeniopygia guttata) show that the adrenal hormone dehydroepiandrosterone (DHEA) can be indirectly converted into estrogens within the brain (Soma, Alday, Hau, & Schlinger, 2004); however, DHEA does not appear to influence aggression in Siberian hamsters (Scotti, Belén, Jackson, & Demas, 2008). In common with hamsters, short day lengths increase aggression levels in beach mice (Peromyscus polionotus). Hormone manipulation studies revealed that the  estrogen receptor subtype (ER ) agonist PPT (propylpyrazole-triol) and the ER, agonist DPN (diarylpropionitrile) increased aggression in short-day P. polionotus and decreased aggression in “long-day” mice (Trainor, Lin, Finy, Rowland, &

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Nelson, 2007). These results suggested that photoperiod regulates processes that occur after estrogens bind to their appropriate receptors. Steroid hormones, including estrogens, can affect physiological and behavioral processes via slow (hours to days) genomic or fast (seconds to minutes) nongenomic pathways (Vasudevan & Pfaff, 2007). Estradiol injections of beach mice resulted in rapid ( 15 minutes) increases in aggression in short-day mice, but not longday mice (Trainor et al., 2007). This suggests that estradiol increases aggression via nongenomic actions in short days, but not in mice housed in long days. Moreover, gene chip analyses indicated that estrogen-dependent expression of genes containing estrogen response elements in their promoters was decreased in the bed nucleus of the stria terminalis (BNST) of short-day mice compared with that of long-day mice. These data suggest that the environment regulates the effects of steroid hormones on aggression in P. polionotus by determining the molecular pathways that are activated by steroid receptors (Nelson & Trainor, 2007). Another factor that plays an important role in regulating aggression is neuronal nitric oxide synthase (nNOS or NOS-1; 11). nNOS produces the neurotransmitter, nitric oxide (NO), as a by-product of the conversion of arginine into citrulline in the central and peripheral nervous systems (Nelson & Trainor, 2007). Nitric oxide produced from neurons appears to be involved in regulating some aggressive behaviors. For example, male mice with targeted disruption of the nNOS gene (nNOS–/–) display sustained aggressive behavior and persistent sexual behavior (Nelson & Chiavegatto, 2001; Nelson et al., 1995). Castration and testosterone replacement studies indicate that testosterone is necessary, but not sufficient, to provoke elevated aggressive behavior in nNOS–/– mice (Kriegsfeld, Dawson, Dawson, Nelson, & Snyder, 1997). Because gonadal steroid hormones influence neuronal nitric oxide synthase (nNOS), and this enzyme has been implicated in aggressive behavior, nNOS expression was hypothesized to be decreased in short-day male Siberian hamsters and negatively correlated with the display of territorial aggression. Again, all short-day housed hamsters were significantly more aggressive than long-day animals, regardless of whether they regressed (responsive) or maintained (nonresponsive) gonadal size or testosterone concentrations (Wen et al., 2004). Short-day animals, both reproductively responsive and nonresponsive morphs, also displayed significantly fewer nNOS-immunoreactive cells in brain areas associated with aggression including the anterior and basolateral amygdaloid areas and paraventricular nuclei as compared to long-day hamsters. Together, these results suggest that seasonal aggression in male Siberian hamsters is regulated by photoperiod, through mechanisms that are likely independent from gonadal steroid hormones.

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SUMMARY The predictable daily and seasonal changes in the environment have led to the evolution of clock mechanisms that permit anticipation of daily and seasonal events to maximize reproductive fitness and survival. Responses to natural light cycles result in adaptive temporal organization in humans and other animals. Since the invention and use of electrical lights, starting around the turn of the twentieth century, this temporal organization has been dramatically altered. Light at night has significant social, ecological, behavioral, and health consequences that are only now becoming apparent for both daily and seasonal rhythms. Increased understanding of circadian organization may lead to individually tailored medical treatments. Appreciation that there are individuals who are “morning larks” or “night owls” may lead to individualized learning programs that schedule cognitive demanding tasks at circadian-appropriate times-ofday. As we learn more about entrainment effects of light, improvements in lighting schedules or melatonin treatment for nightworkers should be made. Understanding the genes underlying the biological clock function has been a remarkable tour de force in molecular biology. As more precision in the mechanisms underlying biological clock function is revealed, we need to improve understanding of the output signals of the central clocks to the periphery. How are circadian rhythms in cell cycling, metabolism, or physiology entrained and maintained? What are the consequences of poor coupling among oscillators? Additional research is necessary to appreciate the health effects of faulty circadian output signals. Behavioral phenotype is the result of a gene and environment interactions. As we have become increasingly sophisticated about the mechanisms underlying gene expression, we need to simplify the environmental variables that affect gene expression to improve our studies. Studies of photoperiod on behavior are important because they allow precise environmental probing of the geneenvironment interactions. Future research will capitalize on the use of simple, yet precise, environmental manipulations to understand the behavioral effects of differential gene expression. Additional studies of the effects of global warming on organisms low on the food chain (e.g., plants and insects) are also warranted. If the timing of abundance of plants and insects shift, then this will affect breeding success of amphibian, reptiles, birds, and mammals that rely solely on photoperiod to time reproduction. Shifts in seasonal links among food, survival, and reproduction may already be affecting populations of amphibians and important pollinating insects, which could have enormous effects on our food availability and survival.

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Importantly, understanding biological clocks and their associated biological rhythms is critical for a full understanding of behavior. Behavior is not constantly expressed, but rather, displays a temporal organization across the day or year. In order to understand why a behavior occurs, an appreciation of the temporal organization of individuals is necessary. It is not appropriate to study learning and memory in nocturnal rats tested in the middle of our day. It is not appropriate to maintain animals on seasonally ambiguous photoperiods of 12 hours of light and 12 hours of dark per day. Not attending to biological rhythms will confound behavioral studies of humans and other animals.

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References 81 Wen, J. C., Hotchkiss, A. K., Demas, G. E., & Nelson, R. J. (2004). Photoperiod affects neuronal nitric oxide synthase and aggressive behaviour in male Siberian hamsters (Phodopus sungorus). Journal of Neuroendocrinology, 16, 916–921. Wingfield, J. C., Jacobs, J., & Hillgarth, N. (1997). Ecological constraints and the evolution of hormone-behavior interrelationships. Annal of New York Academy of Science, 807, 22–41. Woelfle, M. A., Ouyang, Y., Phanvijhitsiri, K., & Johnson, C. H. (2004). The adaptive value of circadian clocks: An experimental assessment in cyanobacteria. Current Biology, 14, 1481–1486. Wolk, R., Gami, A. S., Garcia-Touchard, A., & Somers, V. K. (2005). Sleep and cardiovascular disease. Current Problems in Cardiology, 30, 625–662. Wright, K. P., Jr., Hull, J. T., Hughes, R. J., Ronda, J. M., & Czeisler, C. A. (2006). Sleep and wakefulness out of phase with internal biological time impairs learning in humans. Journal of Cognitive Neuroscience, 18, 508–521. Xiong, J. J., Karsch, F. J., & Lehman, M. N. (1997). Evidence for seasonal plasticity in the gonadotropin-releasing hormone (GnRH) system of the ewe: Changes in synaptic inputs onto GnRH neurons. Endocrinology, 138, 1240–1250. Yagita, K., Tamanini, F., van Der Horst, G. T., & Okamura, H. (2001). Molecular mechanisms of the biological clock in cultured fibroblasts. Science, 292, 278–281.

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Yamamoto, H., Nagai, K., & Nakagawa, H. (1987). Role of SCN in daily rhythms of plasma glucose, FFA, insulin and glucagon. Chronobiology International, 4, 483–491. Yamazaki, S., Numano, R., Abe, M., Hida, A., Takahashi, R., Ueda, M., et al. (2000). Resetting central and peripheral circadian oscillators in transgenic rats. Science, 288, 682–685. Yan, L., Foley, N. C., Bobula, J. M., Kriegsfeld, L. J., & Silver, R. (2005). Two antiphase oscillations occur in each suprachiasmatic nucleus of behaviorally split hamsters. Journal of Neuroscience, 25, 9017–9026. Yaskin, V. (1984). Seasonal changes in brain morphology in small mammals. Pittsburgh, PA: Carnegie Museum of Natural History. Yoo, S. H., Yamazaki, S., Lowrey, P. L., Shimomura, K., Ko, C. H., Buhr, E. D., et al. (2004). PERIOD2::LUCIFERASE real-time reporting of circadian dynamics reveals persistent circadian oscillations in mouse peripheral tissues. Proceedings of the National Academy of Sciences, USA, 101, 5339–5346. Zucker, I., & Boshes, M. (1982). Circannual body weight rhythms of ground squirrels: Role of gonadal hormones. American Journal of Physiology, 243, R546–R551.

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Chapter 5

NEUROPHARMACOLOGY GARY L. WENK AND YANNICK MARCHALANT

given to people at this time produced more harm than good.) Finally, the term came to be associated with drugs that alter the mind in some manner, either positively or negatively.

This chapter is about drugs that affect the brain, and its purpose is to demonstrate that we can use our understanding of how drugs work in order to gain a better appreciation of how the brain works. Drugs alter brain function; therefore, it is possible to learn about normal brain function by studying how selected drugs with specific actions alter behavior and mental function (Wenk, 2003). Neuropharmacology is a discipline that uses drugs as tools to study the brain and better understand its functions. For our purposes, a drug is anything that we take into the body that ultimately affects brain function. This includes licit and illicit drugs, overthe-counter drugs, as well as nutrients such as vitamins, herbs, chocolate, and caffeine. The distinction between what is considered a nutrient, that is, something that the body needs, and a drug, that is, something that the mind might crave, has become quite blurred. Many people consider nicotine, chocolate, and caffeine essential parts of their daily diets. This chapter is organized according to the anatomy of the brain rather than according to the classes of the drugs. Each section begins with a brief discussion of the anatomy of the particular neurotransmitter system, the role of this system in specific brain regions, and how it is possible to manipulate brain function through the production, release, and inactivation of drugs.

Drugs that affect the brain are fundamental to most cultures and the routine use of stimulants and depressants is so omnipresent that most of us don’t even consider such substances to be drugs, but rather actual nutrients. Indeed, the distinction between drug and nutrient becomes more blurred with each generation. Many people cannot get through the day without the assistance of coffee, tea, tobacco, alcohol, cocoa, or marijuana. Throughout these pages, I will regard anything you take into your body as a drug, whether it’s obviously nutritious or not. As you will see, even molecules that are obviously nutrients, such as essential amino acids, have properties that can be ascribed to a psychoactive drug. Like nutrients, many drugs that affect brain function come from plants that grow all around us. Plant alkaloids (substances containing nitrogen and carbon)—the active ingredients of plants—are related structurally to the neurotransmitters your body uses, and so they can interact with the receptors on your neurons to influence brain function. This is a very important principle that will emerge as you read this chapter: A drug acts on the brain only if it in some way resembles an actual neurotransmitter, or if it is able to interact with an essential biochemical process in the brain that influences the production, release, or inactivation of a neurotransmitter. Plant alkaloids are essentially modified amino acids similar to those used by the brain and body. They resemble the natural chemicals produced and used by the brain. People in ancient cultures were very aware of these natural plant products and their unique properties; they often sought them out for remedies for a variety of illnesses. As a distinction, we will consider substances in plants that do not affect the brain either inert or nutritious.

All of the drugs discussed in this chapter affect the brain and therefore behavior. Because of this property, they are called psychoactive, a term that first appeared in 1548 in the title to a collection of prayers of comfort for the dead by Reinhard Lorichius, titled: “Psychopharmakon, Hoc Est: Medicina Animae.” Within these prayers, the term refers to a type of spiritual medicine that was used principally in miserable or hopeless situations near the end of life. The Greek word pharmakos referred to a human scapegoat—the person who was sacrificed, usually figuratively but sometimes literally by ritual stoning, as a remedy for the illness of another person, usually someone far more important in the society. Later, around 600 b.c. or so, the term came to refer to the drug, or poison, that was being given rather than the person. (Many of the psychoactive drugs

Ancient cultures’ use of plant extracts as medicines to affect the mind was likely the beginning of our concept of how our brain functions. The earliest cultures believed mental illness was something caused by evil spirits or was 82

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Principles of Neuropharmacology

a punishment delivered by an angry deity. In the middle part of the twentieth century, effective tranquilizers were introduced for the treatment of the mentally ill. The realization that it might be possible to cure mental illness in the same way that one cured physical illness was slow to gain general approval because of the wide-ranging, and for some quite frightening, implications about what this meant regarding the nature of the human brain. In the future, drugs will likely be used to do more than simply correct neurochemical imbalances but will actually be used to enhance our cognitive abilities. The reasons underlying the decision to take neuropharmaceuticals are likely as varied as the number of people using them. For example, people may take powerful antidepressant drugs when they cannot cope with the world as an imperfect place. Sometimes drugs are taken by people who are bored or distressed by tedious tasks. Soldiers have a long history of taking drugs to lessen the impact of long periods of intense terror. Before considering the neuropharmacology of specific neural systems and brain regions, some basic principles must be considered. First, drugs that affect the brain should not be viewed as being either good or bad. They are simply chemicals—no more, no less. They have actions within the brain that we desire or would like to avoid. Second, every drug has multiple effects. Because the brain is so complex and because drugs act in many different areas of the brain and body at the same time, they will often have many different effects on brain function and behavior. Third, the effects of a drug on the brain will always depend on the amount consumed or injected. Varying the dose of any particular drug will change the magnitude and the character of the effects of the drug. This principle is called the dose-response effect. In general, greater doses lead to greater, or sometimes completely opposite, effects. Finally, the effects of a drug on the brain are greatly influenced by the individual’s genetic and drug-taking experience and the expectations that the person has about the consequences of the drug-taking experience. For example, if you respond strongly to one drug, you’re likely to respond strongly to many drugs, and this trait is likely shared by one of your parents. Also, if you expect that a drug will act in a certain way on your brain and behavior, then it is much more likely to do so; this is referred to as the placebo effect. It’s ironic that the brain is the organ that decides for itself how it will experience the drug (for a more detailed discussion on placebo effects, see Chapter 63).

PRINCIPLES OF NEUROPHARMACOLOGY The part of the body where the drug acts to produce its effect is called the site of action. Drugs differ in their sites of action. This chapter focuses on those drugs that directly

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influence neural function. Often the behavioral effects of a drug provide clues to its site of action within the brain. For example, drugs that affect sleep usually alter activity in the reticular activating system. Another clue to site of action is afforded by the unequal distribution of neurotransmitters in the brain (Figure 5.1). For example, dopamine is highly concentrated in the basal ganglia, which controls movement. Therefore, administration of drugs that affect the dopamine system may affect the control of movement. Many drugs that might potentially influence brain function are never able to enter the central nervous system due to the presence of barriers; the most important is the blood-brain barrier, which is made up of many parts, the most important being a special type of capillaries. These capillaries have tight junctions between each other, have no fenestra, do not perform pinocytosis, exhibit a thick basement membrane made of an amorphous mucopolysaccharide and finally are covered by astrocyte processes that juxtapose on the basement membrane. These features allow the entry of drugs that are lipid soluble. The relative affinity of a drug for either lipid or water environments is known as its partition coefficient. The partition coefficient plays an important role in how drugs affect brain function. Very lipid-soluble drugs enter the brain very rapidly; they also tend to exit the brain rapidly, which limits the duration of their action (Meyer & Quenzer, 2005). Once a drug has entered the brain, its site of action is often a receptor protein within the synapse. The brain’s response to a drug is proportional to the fraction of receptors occupied. Drugs that bind to receptors and produce a pharmacological action are called agonists; drugs that bind to receptors but produce no pharmacological action are called antagonists. Receptors for several neurotransmitters, for example, acetylcholine, gamma-amino butyric acid (GABA), and glycine, have homologous structures and may share an evolutionary ancestor. An understanding of the evolution of the neurotransmitters and their receptors sometimes gives clues to their function. Once the drug has interacted with its respective receptor protein, its actions are terminated either enzymatically or by simple diffusion away from the synapse. Most neuroactive compounds are not transported into neuronal terminals. Some examples for selected neurotransmitter systems are discussed later. The removal of a drug from the brain is frequently accompanied by biological and behavioral changes that are opposite to those produced by the drug; that is, the brain always “pushes back.” For example, the euphoria induced by cocaine and amphetamine is often a prelude to severe depression. Many biological factors such as age and weight play a crucial role in how drugs affect the brain, and influence behavior and personality, an emergent property of the brain. This concept was probably best described by Wilder in his Law of Initial

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Neuropharmacology Corpus Callosum

P

A D N Brain Stem S

Cerebellum

Figure 5.1 Schematic anatomy of neurotransmitter systems. Note: A = Acetylcholine neurons within the basal forebrain region project topographically to the cortex, hippocampus, amygdala, and olfactory bulbs; D = Dopamine neurons within the rostral midbrain project into the ipsilateral striatum and frontal cortex; N = Norepinephrine neurons originate within a small region, the locus coeruleus, in the floor of the fourth ventricle and project into virtually all regions of the ipsilateral hemisphere, with the exception of the basal ganglia; S = Serotonin neurons originate with a scattered group of nuclei that lie along the midline of the pons and medulla and project both caudally into the brain stem and rostrally into all regions of the brain; P = Peptide-containing neurons tend to be more diffusely scattered as interneurons, although there are notable exceptions.

Value (Wilder, 1958). Each person has an initial level of excitation; the degree of response to a drug depends on this initial level. For example, euphoria is observed in patients suffering from pain, anxiety, or tension when they are given small doses of morphine. In contrast, a similar dose given to a happy, pain-free individual often precipitates mild anxiety and fear. Catatonic patients may respond with a burst of animation and spontaneity to an intravenous injection of barbiturates. Sedative drugs create more anxiety in outgoing, athletic people, as compared to passive, intellectual types. In the remainder of this chapter, we examine the intersection of neuroanatomy, neurochemistry, and neuropharmacology, beginning with the first neurotransmitter system discovered, acetylcholine.

ACETYLCHOLINE Acetylcholine is an important neurotransmitter for many species within all kingdoms. The precursors of acetylcholine synthesis, choline and coenzyme A, have been found in both prokaryotes and eukaryotes, including a strain of Pseudomonas fluorescens isolated from the juice of fermenting cucumbers as well as in the blue-green algae, Oscillatoria

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agardhii, where it may be involved with photosynthesis. Acetylcholine stimulates silk production in spiders and limb regeneration in salamanders (Venter et al., 1988). Therefore, it is difficult to ascribe a particular function to this molecule in nature. Acetylcholine neurons play an important role within the parasympathetic and sympathetic nervous system; their role in the periphery accounts for many significant side-effect profiles of drugs that also affect central cholinergic cells. Within the brain, there are numerous cholinergic systems; however, two are particularly important: the basal forebrain cholinergic system, which projects topographically to the cortex, hippocampus, and other limbic structures that influence memory, attention, and mood; and the intra-striatal collection of short-axon interneurons that influence control of movement (for review, see Wenk, 1997; see Chapter 17). Cholinergic neurons in the basal forebrain region that innervate the hippocampus and cortex are vulnerable to degenerative processes associated with Alzheimer ’s disease (AD) and may become dysfunctional during the early stages of the disease process (Davis et al., 1999; Whitehouse, Price, Clark, Coyle, & DeLong, 1981). The extent to which this neurotransmitter system is impaired may correlate with the severity of selected cognitive symptoms associated with dementia. For example, dysfunction of cholinergic input to the cortex may contribute to a deficit in attentional abilities (Sarter, Gehring, & Kozak, 2006); alterations in the projection to the central nucleus of the amygdala may underlie emotional changes (Power, Vazdarjanova, & McGaugh, 2003); and the dysfunction of cholinergic inputs to the hippocampus clearly underlies the presence of amnesia (Olton, Wenk, Church, & Meck, 1988; Wenk, 2007). A deficit in cholinergic biomarkers, including a decline in level of cholinergic synthetic enzyme, choline acetyltransferase activity, transmitter production, and release are commonly reported biochemical changes within the brains of patients with AD. It is important to recognize that the loss of these biomarkers does not herald the death of the neuron; an injured neuron will often reduce the production of its luxury systems related to neurotransmitter function in preference to biochemical processes that are essential for recovery. The persistence of the intact neurons offers an opportunity to rescue them from continued degeneration. As such, experimental manipulation of the functional integrity of cholinergic neurons in the basal forebrain of young rats has been used as an animal model for this component of AD pathology (Wenk, 2006; Wenk et al., 1994). Moreover, drug therapies designed to attenuate memory deficits associated with AD have focused on alleviating these impairments in the cholinergic synaptic function. Similar approaches have been used to compensate for presumed impairments in other neurotransmitter systems.

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Acetylcholine

These neurons synthesize acetylcholine from choline, obtained from the diet and acetyl groups that originate in mitochondria from the metabolism of glucose, and are transported into the cytoplasm attached to coenzyme A. The synthesis occurs within the cytoplasm, and the product is stored in synaptic vesicles or loosely bound to the cytoplasmic membrane for fast release. The vesicular pool is released only when the cytoplasmic pool is expended. Production via the enzyme choline acetyltransferase is controlled by end-production inhibition; the availability of choline and acetyl moiety is not rate limiting under normal conditions. Therefore, administration of choline via the diet does not increase acetylcholine production or release (Wild & Benzel, 1994). Once released, acetylcholine’s action within the synapse is terminated by the acetylcholinesterase enzyme; about 40% of the choline produced is actively taken up into the terminal to be reused again for synthesis of acetylcholine. The blockade of choline uptake by hemicholinium-3 is lethal because the reduced acetylcholine synthesis produces an imbalance in autonomic function and the loss of the ability of the motor neurons to contract the diaphragm; in contrast, this drug has no behavioral effects because it does not cross the blood-brain barrier. The release of acetylcholine from the presynaptic terminal can be inhibited by the toxin released from the Clostridium botulinum bacteria; death is caused by loss of diaphragmatic contraction, leading to asphyxiation. Once released, acetylcholine can act on two quite different protein receptors that have been designated (as have most neuropharmaceuticals) according to the compounds that were originally used to manipulate them, that is, nicotine and muscarine. Nicotinic receptors are directly coupled to a sodium-conducting channel and produce a rapid increase in sodium ion conductance and depolarization of the postsynaptic membrane. In contrast, muscarinic receptor stimulation leads to slower depolarization or hyperpolarization depending on the nature of the secondary messengers. Muscarinic receptors have been further subdivided (as have most other neurotransmitter receptors) according to their affinities for various newly investigated drugs. These receptors have not been found within kingdoms for protoctista and fungi although acetylcholine is produced by some members. These findings have led to some intriguing hypotheses regarding the evolution of receptors and the limitation of our chemical tools to investigate them. For example, nicotinic and muscarinic receptors have been found in peanut worms (whose fossils date back 500 million years), spoon worms, leeches, and earthworms. However, there is no evidence that the two receptors are related; muscarinic and nicotinic receptors differ in size, structure, and mechanism of action. Most of the acetylcholine receptors in the brain are muscarinic, while less

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than 10% are nicotinic (Cooper, Bloom, & Roth, 2002). Yet, stimulation of nicotinic receptors is clearly much more rewarding than stimulation of muscarinic receptors. The explanation for their divergent consequences underlies a basic principle in neuropharmacology that mimics the real estate industry: location is all that matters. Nicotine is a very potent agonist; as little as 60 mg can be fatal to an adult. Curare, a resinous extract of the plants Chondrodendron tomentosum and Strychnos toxifera from the Orinoco and Amazon basins in South America, is an antagonist at the nicotinic-type acetylcholine receptor. Because it does not cross the blood-brain barrier, its actions are expressed primarily on the autonomic ganglia and at the neuromuscular synapse. The drug is lethal because it blocks the neuromuscular nicotinic receptors located on the diaphragm that allow breathing; therefore, death is by asphyxiation. Muscarine, carbechol, and oxotremorine are agonists at the muscarinic-type acetylcholine receptors; atropine and scopolamine are antagonists at this receptor. Once acetylcholine is released into the synapse, its action is terminated by the enzyme acetylcholinesterase. This enzyme is produced by cholinergic neurons and released from the cytoplasm into the extracellular space where it is found in high concentration and sometimes taken up into noncholinergic nerve terminals. This enzyme can very quickly inactivate synaptic acetylcholine at the rate of approximately 25,000 molecules per second. Thus, even its partial inhibition will have a profound effect on synaptic levels of acetylcholine and postsynaptic stimulation of acetylcholine receptors. Physostigmine is a reversible inhibitor of this enzyme; because acetylcholine is usually inactivated by this enzyme, its levels increase quickly within the synapse. In the presence of physostigmine, or any other acetylcholinesterase inhibitor, acetylcholine will either diffuse out of the synapse or be catabolized by other esterase enzymes, such as butylcholinesterase. The widespread distribution of neuronal systems that use acetylcholine indicates that these systems play a significant role in many brain functions. Its role in neuroplasticity has been best studied. The blockade of the muscarinic receptors within the brain by scopolamine impairs memory and produces mental confusion due to its actions within the hippocampus and neocortex. (A not very amusing side note: Some news reports claim that thieves sometimes add scopolamine to chewing gum, chocolate, or drinks of unsuspecting people, or blow it into their faces, to immobilize and then rob them.) In contrast, drugs that enhance the action of acetylcholine at muscarinic receptors by preventing its catabolism, for example, physostigmine, may enhance memory and attentional abilities. Several plants that grow wild contain scopolamine, atropine, or related molecules, such as jimson weed (Datura stramonium),

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henbane (Hyoscyamus niger), and mandrake (Mandragora officinarum). The antagonism of muscarinic receptors within the neocortex slows neural activity and makes the user drowsy; this action within the hippocampus impairs plasticity. The deadly nightshade, Atropa belladonna, was given its name by Carl von Linné in the eighteenth century to indicate the poisonous nature of this plant. Atropos was one of the Greek “fates,” or “daughters of necessity” who also included Clotho, who spun the thread of life, and Lachesis, who allotted each man his portion of life. Atropos cut the thread of life at the appointed time. Muscarinic receptors are expressed by the smooth muscles that encircle the iris; their antagonism allows the pupils to dilate. The presence of muscarinic receptors within the motor cortex and the basal ganglia explains why scopolamine also produces slurred speech and generally impaired motor abilities. Higher doses of scopolamine can produce feelings of unpleasantness and visual and auditory hallucinations. The hallucinations are of ordinary objects and not mythic or other-worldly like those produced by drugs that directly affect serotonin receptors. This may provide insight into the function of acetylcholine and the influence of the location of muscarinic receptors within the normal brain. Stimulation of muscarinic receptors may produce euphoria and subjective sensory changes. The alkaloid muscarine is present in the mushroom Amanita muscaria; it can produce delirium and hallucinations when eaten. Another agonist of the muscarinic receptor is found in the areca nut of the betel pepper tree of Asia. The active ingredient is arecoline. The nut is used as a mild euphoriant and antitussive (cough suppressant) throughout Southeast Asia; these uses are consistent with the presence of muscarinic acetylcholine receptors with the limbic system and coughing centers of the brain. One of the best-studied agonists of the nicotinic acetylcholine receptor is, of course, nicotine. Nicotine occurs in more than 64 species of plants around the world, including the tobacco plant. The first use of tobacco was to treat persistent headaches, colds, and abscesses and sores on the head. Tobacco emetics were used to treat flatulence, and the smoke was inhaled deeply in order to lessen bad coughs. Jean Nicot sent some tobacco to Catherine de Medici, who was then queen to Henry II of France; she reported that it helped treat her migraines and the plant took on the title of herbe sainte or holy plant. Nicot got credit for the discovery and in 1565 Linnaeus named the genus Nicotiana in his honor. In the 1890s, the U.S. Pharmacopeia dropped nicotine from its list of useful therapeutic agents. The alkaloid nicotine is likely utilized by the tobacco plant as a defense against insects that would express this type of receptor in their body and be dose-dependently vulnerable to its toxicity. Tolerance and dependency develops from

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its chronic use. Most cigarettes contain about 1 to 2 mg of nicotine. Because nicotine is quite volatile and heat labile, only about 20% of this dose is actually inhaled into the body; however, due to its exceptional lipid solubility, at least 90% of the inhaled nicotine will be absorbed. Nicotine can be rapidly absorbed from mouth, lungs, or intact skin. Once the smoke is inhaled, it is absorbed via pulmonary alveoli and transported to the brain within 2 to 7 seconds. This makes smoking tobacco as efficient as an intravenous injection in terms of getting nicotine to its site of action within the brain. Nicotine is also quite toxic; 60 mg is considered a lethal dose for a human, and death takes only a few minutes to occur. The actions of nicotine at the synapse are complicated. Initially, at lower doses, nicotine stimulates the receptor; then, at higher doses it induces a depolarization blockade, or inactivation, of these receptors from further stimulation. The extent that desensitization occurs is influenced by the subunit composition of the receptor. Nicotine receptors are composed of five subunits that form an ion channel (Siegel, Albers, Brady, & Price, 2006). The brain expresses at least 12 different nicotinic receptor subunits. Thus, prolonged exposure to nicotine, for example, by continued smoking of tobacco products, would lead to a complex pattern of receptor stimulation and desensitization across brain regions. When this inactivation occurs in the periphery, it is associated with general muscle weakness as the function of the neuromuscular junction is impaired. Chronic exposure to nicotine leads to the rather paradoxical condition of an upregulation in the number of nicotinic receptors that may exist in a desensitized conformational state. The altered synaptic function within the brain leads to tremors. Once again, death is most often due to paralysis of the respiratory muscles. Nicotine affects cortical function in a complex dose-dependent fashion; low doses activate the left hemisphere and stimulate activity, while high doses activate the right hemisphere and are associated with sedative effects. Therefore, when doing boring tasks, a low dose of nicotine can increase subjective arousal. In contrast, during anxious or stressful situations, smokers may actually reduce subjective stress by activating the right hemisphere and producing sedation. Sixty percent of adults with attention deficit disorder are smokers, compared to less than 30% of the rest of the population, implying that these adults are finding a pharmacological substitute for their childhood medications. Nicotine also produces a dose-related self-report of euphoria that is most pronounced following overnight abstinence. This may explain why heavy smokers like to light up as soon as they awake. Throughout the day, smokers carefully, and probably unconsciously, control the amount of nicotine that reaches the brain by the number of cigarettes they use per hour, by altering the rate at which

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Catecholamines

they take a puff, and by the volume of their inhalation. This careful titration may optimize the amount of cholinergic receptor stimulation and cortical activation. In 1948, the Journal of the American Medical Association stated that “from a psychological point of view, in all probability more can be said in behalf of smoking as a form of escape from tension than against it.” Today, our perception of nicotine use has been altered principally by the consequences of the “vehicle” for nicotine administration, tobacco. Tobacco causes almost one U.S. death every minute or the equivalent of four major airline crashes daily.

CATECHOLAMINES The neurotransmitter systems considered in this section— dopamine and norepinephrine—are found in both the peripheral nervous system and central nervous system (Figure 5.2). Once again, I discuss the function of these

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two neurotransmitter systems by examining the consequences of stimulation and antagonism of their function by a rather large series of drugs. A consistent pattern of effects emerges demonstrating that dopamine is intimately related to reward and norepinephrine function underlies the components of arousal (for details, see Chapter 40). We know a great deal about the functions of these two neurotransmitter systems primarily because so many drugs have been discovered that can modify their function. Catecholamines are monoamines, that is, they contain at least one nitrogencontaining amine group. The term catechol refers to the presence of a six-carbon ring that has two hydroxy groups attached at adjacent positions to each other. Catecholamines occur extensively throughout nature and have been identified as neurotransmitters in animals as diverse as insects, crustacea, arachnids (spiders), and primates (Venter et al., 1988). Norepinephrine is predominant in the brain and peripheral nervous system in mammals, while dopamine is predominant in species that evolved prior to mollusks.

Blood-brain barrier Ca⫹⫹ 4 5 2 6

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Figure 5.2 The life cycle of a typical neurotransmitter. Note: (1) Nutrients, such as amino acids, glucose, fats, and dipeptides, from the diet are transported actively out of the arterial supply to the brain through the blood-brain barrier and, via astrocytes, into the neuron. (2) Enzymatic conversion, for example, occurs by hydroxylation, decarboxylation or esterification, and so on. (3) Active transport occurs into the synaptic vesicles for storage and later release. (4) Activation of the presynaptic neuron leads to the arrival of an action potential that induces the opening of voltage-controlled ion channels, in particular, calcium ion channels; (5) the entering calcium ions induce the fusion of the synaptic vessel to the presynaptic membrane and the release of the neurotransmitter into the synaptic space. (6) The neurotransmitter molecule briefly interacts with the specific proteins on the surface of the postsynaptic cell membrane and induces a large variety of potential responses, for example, opening or closing ion channels, de- or hyperpolarization of the membrane, and so on. (7) Secondary messengers may be produced due to the activation of enzymes that initiate a cascade of molecular processes. The consequences of this cascade are quite diverse, ranging from locally influencing the

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function of nearby receptors to alterations in the genome of the neuron itself. (8) The actions of the neurotransmitter must be terminated in order to permit the communication between two neurons. This is accomplished principally by actively reabsorbing the transmitter molecule back into the presynaptic terminal using dedicated protein complexes that are on the surface of the presynaptic membrane. (9) A secondary method of transmitter inactivation is by enzymatic conversion of the molecule so that it is no longer able to interact with its receptor. For example, the neurotransmitter molecule may be hydrolyzed, ionized, or conjugated onto a larger, and more water-soluble, molecule. (10) Once the neurotransmitter is enzymatically converted, it is removed from the brain and metabolites of neuronal function can be detected in the body fluids. (11) Drugs can interact with any of these processes and impair, or even sometimes enhance, the production, storage, release, receptor function, re-uptake, and inactivation processes. Fluctuations in the levels of the metabolites of different neurotransmitters can be monitored in order to judge the integrity of specific neural systems.

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In mammals, norepinephrine is distributed throughout the autonomic nervous system. Its presence there contributes significantly to the side effects, often undesired, of many drugs that alter the function of norepinephrine neurons in the brain. Norepinephrine is released from the postganglionic sympathetic neurons into the body organs as well as into blood vessels and hair follicles on skin. The activation of norepinephrine input to the skin is responsible for the goosebump response to frightening sights and sounds. Within the brain, almost all of the brain’s norepinephrine neurons are located in the locus coeruleus within the floor of the fourth ventricle. The name of this brain region is related to the fact that these norepinephrine neurons concentrate copper into a matrix of melanin pigment. Although copper is a required cofactor for one of the enzymes necessary for the synthesis of norepinephrine, the concentration of the copper far exceeds what is required for neurotransmitter synthesis. Unfortunately, the presence of this transition metal contributes to an age-associated vulnerability to oxidative stress. The axonal projections out of the locus coeruleus into the brain follow two ascending projections: The ventral pathway provides norepinephrine to the hypothalamus, septal area, pituitary, substantia nigra, and mammillary bodies. The dorsal pathway projects to the entire neocortex, hippocampus, thalamus, amygdala, superior and inferior colliculi, and the medial and lateral geniculate nuclei of the thalamus. Dopamine neurons exist within a small region of the midbrain and, although there are three to five times more dopamine neurons in the brain stem than norepinephrine neurons, they do not project as widely throughout the brain as do norepinephrine neurons. Although there are about 10 different groups of dopaminergic neurons within the brain, three general, long-axon pathways are recognized that ascend into the forebrain. The nigrostriatal pathway originates within the substantia nigra and projects to the striatum. The region was given the name substantia nigra, or dark substance, because it concentrates the transition metal iron that, similar to the situation seen in the locus coeruleus, is combined with a melanin pigment. The oxidation of iron contributes to the color of this brain region and also confers a degree of neuronal vulnerability to these cells (Molina-Holgado, Hider, Gaeta, Williams, & Francis, 2007). The degeneration of this pathway is associated with Parkinson’s disease and is characterized by tremors, spasticity, and akinesia. These symptoms provide insight into the normal responsibility of the release of dopamine within the striatum. Many drugs that interact with the function of the forebrain dopaminergic system have side effects that resemble those seen in people with Parkinson’s disease.

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Two other ascending pathways originate in the ventral tegmental area that lies just medial to the substantia nigra. The mesolimbic pathway projects to forebrain structures associated with the olfactory and limbic system. The mesocortical pathway projects to the frontal cortex. Too much activity in these pathways may underlie some of the symptoms associated with psychosis. The drugs that target this system will be discussed later. The many different catecholamine receptors that exist provide the opportunity for a large variety of drugs to have both selective and widespread effects on different parts of the peripheral and central nervous systems. These neurotransmitter receptor proteins have existed in the forms we find today for at least 600 million years. The actual point in evolution where receptor-mediated neurotransmission appears is not known. However, some of the oldest eukaryotes known respond to the same norepinephrine receptor–stimulating drugs as primates; for example, the beta subtype of norepinephrine receptors may have existed in annelids 500 million years ago during the Paleozoic era. Norepinephrine receptors were initially divided into two classes, alpha and beta, due to their distribution in the body and their selective responses to drugs that were available at that time. As newer drugs were studied, additional subclassifications were introduced to the standard nomenclature. Receptors are also categorized by their location on the neuron; for example, autoreceptors are found on the presynaptic membrane of many types of neurons; their purpose is to modulate the release of neurotransmitters from the axonal terminal. With continued investigations, subtypes of these subclassifications have led to the characterization of at least nine different receptors that are all linked to very similar primary signaling mechanisms. Pharmacological manipulation of these receptors, in order to understand their function, has been hindered by the lack of specific and selective agonists or antagonists. A second important feature of most receptors is that they are responsive to the presence of constant stimulation or constant blockade. For example, in response to constant blockade, receptors will become supersensitive. Receptor supersensitivity is a common behavior of receptors (Fleming, 1999) and may represent a compensatory mechanism by the postsynaptic cells in response to the absence of incoming signals via the receptor blockade. Exposure to receptor agonists can produce an uncoupling of the receptor from its intracellular signaling system, for example, a G-protein, leading to the stopped signaling. Ultimately, the receptor may be sequestered into intracellular compartments for later redeployment to the cell surface or degradation. The process of desensitization may involve genetic and posttranscriptional regulatory mechanisms (Heck & Bylund, 1998).

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Catecholamines

Many drugs that interact with the function of the neurotransmitter systems do so by affecting their metabolism. Three key enzymes are involved in the synthesis of dopamine and norepinephrine. These enzymes are structurally similar to each other, and the genes that encode them lay close together on the genome like beads on a string of DNA. These enzymes exist within the nervous system of all vertebrates and several invertebrate species but not all of these enzymes are expressed in every catecholamine neuron. When more than one enzyme is expressed in a neuron, their regulation is usually linked in a coordinated fashion. Occasionally during embryogenesis, selected enzymes are transiently expressed and then disappear. Evolutionary studies of these enzymes suggest a strong functional preservation of the catalytic site. The reason for this preservation may be related to the fact that the genes have a high degree of nucleotide homology, suggesting that they may have evolved from duplication of a common ancestral gene. Genetic analyses have shown that the enzymes that initiate the production of dopamine, norepinephrine, epinephrine, and serotonin, that is, phenylalanine hydroxylase, tyrosine hydroxylase, phenylethanolamine N-methyltransferase, and tryptophan hydroxylase, share considerable sequence homology (Baetge, Suh, & Joh, 1986; Grima, Lamouroux, Blanot, Biguet, & Mallet, 1985). The production of dopamine and norepinephrine begins with the amino acid tyrosine that is obtained from the diet and actively absorbed across the blood-brain barrier. Tyrosine is converted to l-dopa by the enzyme tyrosine hydroxylase. This enzyme is the rate-limiting step in the production of norepinephrine and dopamine. The activity of tyrosine hydroxylase is controlled by end-production inhibition and is also regulated by availability of precursors and cofactors. One very important cofactor is molecular iron. Without iron, this enzyme fails to function normally. People with anemia have reduced body levels of iron and, as a consequence, may have reduced tyrosine hydroxylase activity leading to reduced production of norepinephrine and dopamine. The reduced brain levels of these important neurotransmitters in the limbic system may lead to a slight depression. Tyrosine can also be acted on by the enzyme tyrosinase and converted into a melanin pigment. This enzyme is quite interesting to study because it is subject to a mutation that makes it heat labile, that is, it works only in the cooler areas of the body. The consequence of this mutation is a lack of pigmentation in humans; in cats it produces the Siamese breed. Apparently, this enzyme is critical for the normal decussation of visual tracts. The second critical enzymatic step in this pathway is L-Dopa decarboxylase. This enzyme converts the product of tyrosine hydroxylase, l-dopa, into dopamine. This enzyme is extremely

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efficient which may explain why brain levels of l-dopa tend to be so low and why providing substrate to it leads to a dramatic increase in the production of dopamine. The synthesis of dopamine occurs in the cytoplasm of neurons, and the dopamine produced is then transported into synaptic vesicles for storage until it is released with the passage of an action potential. This enzyme is rather nonspecific due to its evolutionary history and also produces serotonin in the presence of the amino acid tryptophan or converts tyrosine to tyramine, which is the precursor to the insect neurotransmitter octopamine. Octopamine is also present in low levels in the vertebrate brain. The third enzyme in this pathway is dopamine-beta-hydroxylase, and it converts dopamine into norepinephrine and is therefore not expressed by dopaminergic neurons. The enzyme is stored within the synaptic vesicles, where it lies in wait for the entry of dopamine molecules from the cytoplasm. The conversion occurs within the vesicle while it’s being transported to the terminals from the cell soma. The enzyme is released with the norepinephrine into the synapse with its principal cofactor, copper, and the intravesicle antioxidant ascorbic acid. The diet is important for the control of synthesis (Siegel et al., 2006). The shifting concentration of different precursor amino acids in the diet will alter their relative uptake and may limit or alter production. With a balanced diet, the uptake of all amino acids is fairly constant and in correct proportion. Too much of one amino acid will offset the uptake of the others and therefore alter the availability of dopamine or norepinephrine for release. Obviously, diet can affect a person’s mood, but the impact is usually more subtle than is typically produced by most psychoactive drugs. Once dopamine or norepinephrine is released, its actions within the synapse are terminated principally by re-uptake into the presynaptic terminal or by metabolic breakdown due to oxidative deamination by monoamine oxidase or transmethylation by catechol-O-methyltransferase. What if the vesicles are empty? One interesting drug, reserpine, prevents the transport of the neurotransmitters into the vesicles. Reserpine is found in the snake root plant. If dopamine and norepinephrine (and serotonin) cannot be stored safely in vesicles, they are caught in the cytoplasm, where the enzyme monoamine oxidase can catabolize them. Reserpine has a tranquilizing effect due to its ability to prevent the transfer of catecholamines into the synaptic vesicle. In addition, the reduction in the availability of these transmitters is associated with a severe depression that might explain why the snake root plant is called the insanity herb (pagla-kadawa) by Sherpas in the Far East. In contrast to reserpine, some drugs enhance the availability and release of catecholamines. One of the beststudied drugs is amphetamine. Amphetamine is taken up

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into the terminals disturbs the vesicular transport storage process, induces a presynaptic leakage of norepinephrine and dopamine, and also blocks their inactivation by reuptake and via monoamine oxidase (Sulzer, Sonders, Poulse, & Galli, 2005). The enhanced release of these catecholamines leads to heightened alertness, euphoria, lowered fatigue, decreased boredom, depressed appetite, insomnia, headaches, and tremors. The rebound symptoms from a drug’s effects on the brain are often proportional and in reverse to the effects of the drug. For example, amphetamine withdrawal produces extreme fatigue, dysphoria, and depression. Excessive exposure to amphetamine can produce a condition similar to paranoid schizophrenia, which is often the inevitable consequence of high-dose use. At the molecular level, these cognitive changes may be related to a reduction in the function or presence of dopamine transporters (Stanley, Pettergrew, & Keshavan, 2000). During World War II, forces on both sides of the battle lines used amphetamine to combat boredom, fatigue, and to increase endurance. Historians suggest that at the end of the war, Adolf Hitler ’s increasingly bizarre behavior may have been due to his excessive use of amphetamines. One of the basic principles of neuropharmacology is that lipid solubility is directly correlated with the speed of uptake of a drug into the brain (Meyer & Quenzer, 2005). Furthermore, the faster a drug enters the brain and alters its physiology, the greater the euphoria the drug is likely to induce. This principle has never been lost on illicit drug designers. Morphine becomes far more lipid-soluble and far more euphorigenic when two acetyl groups are added to produce heroin. Amphetamine has been modified many times in the past. The simplest manipulation was the addition of a methyl group to make methamphetamine, which is a very potent analog of amphetamine and far more lipidsoluble. Not surprisingly, its street name became “speed.” Over time, attempts to make amphetamine ever more lipidsoluble by the addition of carbon atoms has produce drugs that are more euphorigenic and/or hallucinogenic than amphetamine, for example, 3,4-methylenedioxyamphetamine and N-ethyl-3,4-methylenedioxyamphetamine were precursors to 2,5-dimethyl-4-methylamphetamine, and 3,4methylenedioxymethamphetamine; the last is known widely as “ecstasy.” Any chemical manipulation that makes amphetamine more lipid-soluble allows it to enter the brain faster; drugs that quickly enter the brain often become chosen as drugs of abuse. Although amphetamine does not occur naturally, some chemically similar molecules have been discovered. For example, asarone is chemically similar to amphetamine and is found in the plant Aacorus calamus that grows in Asia, Europe, and North America. Khat is an African plant, Catha edulis, that contains two phenylalkylamines called

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cathinone and cathine (d-norisoephedrine). The habit of chewing khat probably predates coffee drinking by centuries. The relative level of these naturally occurring analogs of amphetamine depends, as is true for most plant-derived psychoactive drugs, on where plant is grown, its age, and the time elapsed after collection. Cathinone is quite unstable, a proclivity that makes storage for distribution nearly impossible. Decoctions in hot water are called Abyssinian tea. Other compounds in this plant, as in so many others, include flavonoids that are anti-inflammatory. The cactus Lophophora williamsii is used to prepare a drink called peyote that contains 3,4,5-trimethoxyphenylethylamine (clearly a lipid-soluble molecule that is chemically similar to a catecholamine), also known as mescaline. It produces a dose-dependent range of effects that include euphoria at low doses and hallucinations at higher doses. Drugs that selectively block monoamine oxidase also occur naturally, for example harmaline and harmine can be found in a thick vine plant, Peganum harmala, which grows in the Amazon rain forest. The latency of onset is only 5 minutes after ingesting the plant, and the colorful visual hallucinations may last up to 8 hours. The catecholamine neurotransmitters are inactivated principally by re-uptake into the presynaptic nerve terminal (Siegel et al., 2006). Drugs that block this re-uptake process augment the effects of the neurotransmitter within the synaptic cleft. Most of these drugs have found clinical use as antidepressants. Depression is considered the common cold of psychiatric illness because each year more than 100 million people worldwide develop clinically recognizable depression (for a more detailed discussion of this topic, see Chapter 55). These agents block re-uptake of norepinephrine, dopamine, and serotonin into the terminals thus prolonging interaction in the synaptic cleft. However, all chemical action at the re-uptake site in clinical efficacy does not occur simultaneously. The drugs require 2 to 3 weeks to produce their antidepressant benefit. Their mechanism of action is believed to relate to the adaptive neural mechanisms following chronic use. Overall, the immediate effects block re-uptake and are offset by compensatory short- and long-term adjustments at catecholamine synapses. The blockade of re-uptake of dopamine and serotonin has profoundly euphorigenic effects, as do many current antidepressant drugs. In addition, many drugs that have this action are quite addicting, such as cocaine and amphetamine. At the other side of the synapse, drugs that block dopamine receptors have considerable therapeutic efficacy as antipsychotics. The fact that these drugs are capable of reducing some of the symptoms associated with psychosis does not prove that psychosis is due simply to a dysfunction in dopamine neurons. Indeed, this is a very important general point to consider when using drug actions to

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Serotonin 91

understand brain function. The knowledge that selective re-uptake inhibitors act on a few specific neurotransmitter systems in order to reduce the symptoms of depression does not prove that a dysfunction of one of these neural systems underlies either the mental disorder or its symptoms. It simply indicates that manipulation of re-uptake of a specific neurotransmitter molecule ultimately leads to an alteration in the presentation of symptoms. Indeed, compelling evidence exists that this action has very little to do with the therapeutic benefits of selective serotonin reuptake inhibitors (Santarelli et al., 2003). Furthermore, the alteration in dopamine function probably does not cause psychosis; rather, it is most likely just a secondary consequence of a complex array of alterations of one or more different neural systems in the brain. This may explain why the blockade of some dopamine receptors within certain brain regions reduces the severity of some symptoms but not others. The antagonism of dopamine receptors simply compensates for the presence of an error of brain chemistry or connectivity that may exist somewhere in the brain. There are five identified functional dopamine pathways within the brain (Cooper et al., 2002). The antagonism of each by antipsychotic drugs provides insight into their unique role in the brain. Two originate within the midbrain ventral tegmental area and project into many structures of the limbic system (mesolimbic) or into the frontal neocortex (mesocortical). The antipsychotic actions of dopamine receptor antagonists are thought to involve alterations in synaptic function of the mesolimbic and mesocortical dopamine pathways. A third dopaminergic pathway originates in an adjacent midbrain region, the substantia nigra, and projects into the striatum. Antagonism of dopamine receptors within the striatum by typical antipsychotic drugs leads to the appearance of a series of extrapyramidal side-effects that are similar to those seen in patients with Parkinson’s disease, including tremors when at rest, reduction of voluntary movement, spasticity, and dystonia. The level of dopamine within the frontal lobes and striatum has been correlated with smiling behavior in humans; consistent with this interesting pleasure-related role of dopamine are reports that the loss of the ability to smile is often seen in patients with Parkinson’s disease. Within the striatum, dopamine is likely released on acetylcholine interneurons; many of the extrapyramidal side-effects can be attenuated by drugs that block muscarinic acetylcholine receptors. Antipsychotic drugs also antagonize dopamine receptors within a dopaminergic pathway that originates within the hypothalamus and negatively controls the release of prolactin leading to the increased release of this hormone from the pituitary. It was once thought that antagonism of this pathway within the hypothalamus accounted for the significant weight gain associated with antipsychotic drugs;

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recent evidence suggests that the weight gain is related to the antagonism of histamine receptors. Ironically, the original clinical use of the first antipsychotic drug, Thorazine, occurred because of its ability to block histamine receptors and reduce symptoms of the common cold (Lindamood, 2005). Subsequently, its additional proclivities were recognized. In a manner similar to that observed following treatment with antidepressant drugs, the side effects of dopaminergic receptor blockade occur rather quickly but the clinical benefits require 2 to 3 weeks, or more, to fully develop.

SEROTONIN Serotonin was initially discovered in the serum and determined to have tonic effects on the vascular system, hence its name (Rapport, Green, & Page, 1948). It has been found in the venom of amphibians, wasps, and scorpions and within the nematocysts of sea anemone as well as in the nervous system of lobsters and parasitic flatworms. Serotonin immunoreactivity was not found in Coelenterata (Hydra magnipapillata), Echinodermata (Asterina pectinifera), or Protochordata (Halocynthia roretzi; Fujii & Takeda, 1988). In humans, approximately 90% of total body serotonin is found within the nervous system of the gastrointestinal system. About 8% is localized to platelets and mast cells and the remaining few percent is found within the brain, principally within the pineal gland—which is not generally considered part of the brain. Neurons that produce and release serotonin are organized into a series of nuclei that lie along the midline, or seam, of the reticular region of the brain stem; these are the raphe nuclei. The most caudal lying nuclei in the ventromedial pons send axonal projections into the spinal cord to control the sympathetic autonomic nervous system. The ascending pathways originate in the pons and midbrain raphe nuclei and pass through the medial forebrain bundle in the lateral hypothalamus to innervate the limbic system, hypothalamus, septal nuclei, cingulate gyrus, cerebellum, superior colliculi, hippocampus, and neocortex. Some fibers also make contacts with glial cells and blood vessels. The axonal terminals ramify very widely to topographically innervate most regions of the brain. Individual raphe neurons send projections into brain regions that have related functions. Raphe neurons have a regular, slow, spontaneous firing rate that varies little in response to sensory stimuli. Given the widespread ramifications of the individual neurons and the constant release rate of serotonin, it is likely that this neural system is involved in modulation of neural activity rather than actual information transfer. The production of serotonin requires the absorption of the amino acid tryptophan from the diet (Boadle-Biber, 1993).

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Transport of this large neutral amino acid is influenced by the level of other amino acids in blood. Reduced ingestion of tryptophan leads to reduced levels of brain serotonin; approximately 1% of ingested tryptophan is converted to serotonin within the brain and the remainder is used for protein synthesis. Tryptophan is converted to 5-hydroxytryptophan by tryptophan hydroxylase within the terminals; the enzyme is usually not saturated with substrate. Activity of enzyme can be inhibited by pchlorophenylalanine. 5-Hydroxytryptophan is then converted to serotonin by a decarboxylase and transported into vesicles by a reserpine-sensitive mechanism. End-product inhibition of serotonin synthesis is fairly minor. The enzymatic synthesis of serotonin is principally influenced by neuronal activity and availability of tryptophan in the blood; this may explain why depletion or supplementation of this amino acid in the diet can influence serotonin-controlled cognitive and neural processes such as mood and sleep (Wurtman & Wurtman, 1995; and see Chapter 24 for a detailed discussion). More than 12 serotonin receptors have been characterized. The action of serotonin at these receptors is terminated principally by re-uptake into the presynaptic terminal by a selective transporter protein. Astrocytes lack this transporter yet are able to take up serotonin. Catabolism of serotonin is performed only by monoamine oxidase; however, this enzyme has a relatively minor contribution to the overall inactivation of the action of serotonin. When serotonin is applied to autoreceptors on raphe neurons, principally the 5HT-1A subtype, cell firing is rapidly decreased (Hajós, Hajós-Korcsok, & Sharp, 1999). The firing rate of raphe neurons is likely regulated by a small neural loop (by autoregulation via somatodendritic autoreceptors) as well as by a long, negative feedback loop involving postsynaptic 5-HT-1A receptors (Dong, De Montigny, & Blier, 1999). Therefore, overall, serotonergic neurons negatively regulate their own activity. Drugs that influence serotonin release, re-uptake, or serotonin receptors will have profound affects on the stability of this control. In general, serotonin’s postsynaptic effect has a short latency and long-lasting inhibition. A pair of autoreceptors, 5-HT-1B and -1D, exist near the synapse on axon terminals and may regulate serotonin production and release in response to changes in the synaptic concentration of the transmitter. Exposure to the indole alkylamine d-lysergic acid diethylamide (LSD) also reduces raphe cell firing; however, the cognitive effects of this drug may far outlast the slowing of neuronal activity. The effects of LSD on serotonin neurons may only be the initial trigger that sets in motion a cascade of neural processes throughout the brain. Other hallucinogens, such as mescaline, do not affect raphe cell firing. Although significant

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evidence suggests an important agonist action at the 5HT2A receptor (Gonzalez-Maeso et al., 2007) in the initiation of hallucinations by LSD, the drug also has a high affinity for at least eight of the known serotonin receptors. Psilocybin, o-phosphoryl-4-hydroxy-N,N-dimethlytryptamine, obtained from the mushroom Psilocybe mexicana, is less potent than LSD but likely shares aspects of its actions on serotonin receptors (Spinella, 2001). Psilocybin is the only known naturally occurring indole to contain phosphorus and is chemically related to bufotenine, an interesting molecule that has been discovered in the skin and glands of a South American toad, bean pods from the South American tree Piptadenia peregrine, and the leaves and bark of the Central American mimosa, Acacia niopo. Genetically altered mice and positron emission tomography (PET) studies on humans have been very useful in demonstrating the potential role of specific serotonin receptors in the regulation of mood and control of anxiety. Mice lacking the 5HT-1A receptor show more anxiety-like behavior (Gobbi, 2005). In humans, ratings of religiosity and spirituality were inversely correlated with the number of 5HT-1A receptors (Borg, Andree, Soderstrom, & Farde, 2003). The potential effects of alterations in serotonin neuronal function in relation to spiritual experiences are consistent with the observations of the effects of hallucinogenic drugs such as LSD, psilocybin, and mescaline. In addition, these results have broad implications for understanding and treating several psychiatric conditions. Drugs that stimulate the 5HT-1A receptor have been shown to be clinically effective at reducing anxiety (Caliendo, Santagada, Perissutti, & Fiorino, 2005). Another common psychiatric disorder, depression, responds well to treatment with drugs that selectively block the re-uptake of serotonin into the presynaptic terminal. Long-term treatment with these drugs, and the gradual onset of clinical benefit, is associated with the progressive down-regulation of the number of 5HT-2C receptors (Millan, 2005). Recent evidence suggests that the clinical effectiveness of these antidepressant therapies may depend on their ability to induce neurogenesis within the hippocampus (Taylor, Fricker, Devi, & Gomnes, 2005).

ENDOCANNABINOIDS The very high potency of exogenous cannabinoids and their stereochemical and structural requirements for binding to brain tissue predicted the discovery of the brain’s endogenous cannabinoid system. Fifteen years ago, the first endocannabinoid was discovered and named anandamide, from the Sanskrit word ananda meaning “internal bliss.” Anandamide and a second endogenous cannabinoid,

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Amino Acids 93

2-AG (2-arachidonoyl-glycerol), are enzymatically produced within the postsynaptic neural membrane (Wilson & Nicoll, 2002). Their production is dependent on the entry of calcium ions following membrane depolarization. Therefore, unlike most classical transmitters, endocannabinoids are produced in response to neural activity and released immediately without being stored in vesicles. The endocannabinoid receptor CB1 is linked to a G-protein that inhibits the activation of adenylate cyclase and the formation of ATP (Mackie, 2006). The CB1 receptor is the most abundant G-protein coupled receptor in the brain; its abundance and presynaptic location on glutamate and GABA-releasing terminals gives an indication of its importance and potential role in the regulation of the brain’s principle excitatory and inhibitory neural systems. Anandamide inhibits the release of glutamate and acetylcholine within the cortex and hippocampus, an action that may underlie the effects of exogenous cannabinoids on memory. Anandamide also inhibits the release, and sometimes the re-uptake, of GABA; its complex actions with the basal ganglia and the presence of cannabinoid receptors in the cerebellum may underlie the ataxia occasionally seen in cannabis users (Hajos et al., 2000; Hajós, Ledent, & Freund, 2001; Tzavara, Wade, & Nomikos, 2003). Continued stimulation of CB1 receptors will produce the expected desensitization by G-protein uncoupling and the internalization of the receptor. Anandamide and 2-AG are inactivated by re-uptake into the presynaptic terminal by a specific transport protein and then hydrolyzed by specific enzymes (Fowler et al., 2005). Pharmacological antagonism of the endocannabinoid system may be therapeutic for major depressive disorders (Witkin, Tzavara, Davis, Li, & Nomikos, 2005). In addition, prolonged inhibition of the actions of these endocannabinoids may enhance the release of glutamate and increase the probability of excitotoxicity (Sang, Zhang, & Chen, 2007). Clinical trials using an antagonist of CB1 receptors demonstrated a reliable effect on appetite regulation, providing help to overweight patients. Interestingly, due to the relative omnipresence of CB1 receptors in the human brain, the use of endocannabinoid receptor antagonists might also prove helpful in relieving addiction to alcohol and nicotine. Endocannabinoids are catabolized by cyclooxygenase; which raises an interesting concern regarding the influence of long-term treatment with anti-inflammatory drugs that inhibit this enzyme, such as aspirin or ibuprofen, on mood and memory. We have speculated that the apparently complex actions of cannabinoids within the brain may be interrelated via the function of glutamate (an amino acid neurotransmitter that will be discussed later). Stimulation of endocannabinoid receptors may reduce brain inflammation in young and aged animals by restoring the proper calcium influx via glutamate

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receptor channels (Marchalant, Cerbai, Brothers, & Wenk, 2007). In addition to the role of the endocannabinoid system in neuroprotection and modulation of neuroplasticity and neurotoxicity, cannabinoid receptor stimulation may also modulate neurogenesis (Galve-Roperh, Aguado, Palazuelos, & Guzmán, 2007). Hippocampal neurogenesis declines with normal aging, and this correlates with the onset of depression (Olariu, Cleaver, & Cameron, 2007; Taylor et al., 2005); stimulation of the endocannabinoid system may provide clinical benefit by reversing this decline (Marchalant, Cerbai, Brothers, & Wenk, 2008).

AMINO ACIDS The brain holds relatively high concentrations of amino acids, as compared to other body tissues, which are used primarily for protein and neurotransmitter synthesis. Although glucose is utilized extensively by the brain for energy, it does not use amino acids for gluconeogenesis. Neurons respond to the amino acid neurotransmitters with either excitation or inhibition. The principal neurotransmitters in this group include glutamate, aspartate, gamma amino butyric acid (GABA), glycine, and N-acetyl aspartate. Glutamate, aspartate, and N-acetyl aspartate are the major excitatory amino acid neurotransmitters, while GABA and glycine are the major inhibitory amino acid neurotransmitters (Siegel et al., 2006). Glutamate and GABA have similar patterns of innervation and occur in greater concentration than the other amino acids. The excitatory glutamate and inhibitory GABA transmitter molecules differ by only the presence of a carbon dioxide group; a situation mimicked in nature. For example, the excitatory ibotenic acid and inhibitory muscimol occur together in the Amanita muscaria mushroom, differing only by the presence of a carbon dioxide group, and have been used extensively as pharmacological tools to study brain physiology. Glutamatergic neurotransmission is mediated through ionotropic glutamate receptors such as alpha-amino3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA), kainate, and N-methyl-d-aspartate (NMDA) receptors. Additionally, glutamate activates G-protein coupled metabotropic glutamate receptors that are believed to have a more modulatory function. Most AMPA receptors are impermeable to calcium ions and contribute to fast synaptic transmission. In contrast, NMDA receptors are characterized by high permeability to calcium ions, voltagedependent blockade by magnesium ions, and slower gating kinetics. At normal resting potentials, the transmembrane electric field (negative on the inside of the cell) favors entry of positively charged Mg++ into the pore of

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the receptor so that the NMDA channel is blocked. Under such resting conditions, NMDA receptors do not conduct ions. However, with sufficient, and actually rather significant, postsynaptic depolarization within the neuronal membrane surrounding the channel, the magnesium ion is no longer strongly attracted into the pore of the channel and dissociates. Under such depolarized conditions, NMDA receptors activated by synaptically released glutamate are able to allow the influx of sodium ions and, in particular, calcium ions, and contribute to postsynaptic excitation and activation of second messenger systems. These features make NMDA receptors quite suitable for mediating plastic changes in the brain, such as learning and cognition (Castner & Williams, 2007). However, these features may also contribute to neurotoxicity due to excessive unregulated calcium ion influx through the channel. If, due to a variety of factors that may contribute to many neurodegenerative diseases, the NMDA channels remain open (Wenk, Danysz, & Roice, 1996), it is possible that ambient levels of glutamate associated with normal synaptic activity could activate NMDA receptors, allowing excessive calcium ion influx, which, if sufficiently prolonged, may trigger a cascade of events leading to neuronal injury and death. Pharmacological blockade of NMDA receptors can produce protection from excessive calcium ion influx; however, if the channel blockade is too complete, this could lead to a profound loss of neuroplasticity. Memantine is more potent and slightly less voltage-dependent than magnesium and it may serve as a more effective surrogate for magnesium ions (Rogawski & Wenk, 2003). As a result, of its somewhat less pronounced voltage-dependency, memantine is more effective than magnesium ions in blocking tonic pathological activation of NMDA receptors at moderately depolarized membrane potentials. However, following strong synaptic activation, memantine like magnesium ions can leave the NMDA receptor channel with voltage-dependent, fast-unblocking kinetics. In turn, memantine suppresses synaptic noise but allows the relevant physiological synaptic signal to be detected. This provides both neuroprotection and symptomatic restoration of synaptic plasticity by one and the same mechanism (Danysz & Parsons, 2003). Antagonists that have “too high” affinity for the channel or “too little” voltage-dependence, such as dizocilpine (5-methyl-10,11-dihydro-5H-dibenzocyclohepten-5,10-imine maleate, MK-801), do not have a favorable therapeutic profile and produce numerous side effects because they essentially act as an irreversible plug of the NMDA receptor channel and block both pathological and physiological function. Deficits in energy metabolism associated with aging play an important role in the vulnerability of neurons in neurodegenerative diseases (Emerit, Edeas, & Bricaire,

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2004; see Chapter 61). A defect in energy production would make neurons that express glutamatergic receptors more vulnerable to elevated synaptic levels of glutamate. Decreased levels of intracellular ATP would lead to a partial, and chronic, membrane depolarization, the relief of the voltage-dependent magnesium ion blockade at NMDA receptors, and a persistent increase in the influx of calcium ions into the cells; ultimately, the accumulation of intracellular calcium ions following the activation of NMDA receptors, by glutamate would lead to neuronal death. Chronic neuroinflammation, oxidative stress, or impaired intracellular calcium buffering may also result in impaired energy production, possibly leading to impaired function of the membrane ion pumps required for maintenance of the resting potential. In any of these situations, excessive calcium ion influx through NMDA receptors could activate a host of calcium ion–dependent signaling pathways and stimulate nitric oxide production through closely associated neuronal nitric oxide synthase. This gaseous neurotransmitter, nitric oxide, can react with a superoxide anion to form peroxynitrite, which disintegrates into extremely toxic hydroxyl free radicals that can further impair mitochondrial function and energy production. Intracellular calcium may become concentrated within the postsynaptic mitochondria further contributing to the impaired energy production within the region of the NMDA channels (Duchen, 2000). Mitochondrial dysfunction coupled with activation of glutamatergic receptors could underlie the selective vulnerability of neural systems during normal aging. Mitochondrial failure and neurochemical processes involving glutamate NMDA receptor in the presence of chronic neuroinflammation and oxidative stress may underlie the pathogenesis of many different neurodegenerative diseases (Barnham, Masters, & Bush, 2004; Wenk et al., 1996). GABA and glycine bind to their respective receptors that are primarily chloride ion channels; the opening of these channels allows chloride ions to move across the membrane to achieve an equilibrium that makes the membrane resistant to depolarization. The receptors for these two inhibitory amino acid transmitters are structurally similar and may share a common evolutionary history. The glycine receptor is antagonized by strychnine; the loss of this important inhibitory system leads to excitation of the glycine-sensitive neurons principally within the ventral (motor) spinal cord. GABA has two, or possibly three, receptors. The best studied is the GABAA receptor which is the site of action of many popular drugs, including alcohol and the benzodiazepines (Martin & Olsen, 2000; Wild & Benzel, 1994). When these drugs bind to the GABAA receptor, they enhance the ability of GABA to stabilize the membrane, altering the balance between excitation by

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Neuropeptides

glutamate and inhibition by GABA within local neuronal circuits. The GABAA receptor has been difficult to completely characterize because it is a multi-subunit, heteromeric ion channel: the receptor is essentially built using a combinatorial principle where the functional unit is not just multiple copies of a single polypeptide; rather, the channel is formed by many different polypeptides that contribute unique functional properties that can vary depending on its interactions with the other resident polypeptides. This multifunctional approach offers the opportunity for a more sophisticated and complex control that is common to other ionotropic receptors such as the NMDA glutamate and nicotinic acetylcholine receptors. Therefore, pharmacological manipulation of GABA receptor function has produced therapeutic benefit for a wide range of disorders, particularly anxiety and insomnia (Olsen, 2001).

ADENOSINE Adenosine is probably produced by all neurons in proportion with their firing rate (Siegel et al., 2006). It is the enzymatic product of ATP metabolism within the synapse. ATP can be stored within the synaptic vesicles and released with the neurotransmitter in an activity-dependent manner. Adenosine, in contrast, is not considered a classical neurotransmitter because it is not stored in vesicles and not released in quantal fashion. Adenosine acts as a local vasodilator within the extracellular space of the brain, providing a direct link between neuronal activity and regional blood flow. Extracellular levels must therefore be carefully controlled; adenosine is rapidly removed by re-uptake into cells and degraded to inosine by adenosine deaminase. Four adenosine receptor subtypes have been characterized; they are typical seven transmembranespanning G-protein coupled receptors that have been highly conserved throughout evolution and primarily produce a postsynaptic inhibition. Following tissue injury or the presence of inflammatory proteins, the extracellular level of adenosine will increase and likely plays an important role in neuroprotection. Stimulation of the A2A subtype has an anti-inflammatory effect. A1 and A2A adenosine receptors are the most common type found in the brain, and these receptors are blocked by caffeine at doses that are likely achieved by a person drinking a cup of coffee. Brain inflammation, hypoxia, and ischemia are associated with increased extracellular levels of adenosine. Adenosine may regulate aspects of the brain’s inflammatory processes, including the release of pro-inflammatory cytokines and modulation of microglial activation (Rosi, McGann, Hauss-Wegrzyniak, & Wenk, 2004) via A1 and

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A2A receptors, which may provoke neuroprotection or injury, respectively (Dunwiddie & Masino, 2001; Mayne et al., 2001); not surprisingly, the blockade of A2A receptors is neuroprotective (Kalda, Yu, Oztas, & Chen, 2006). Caffeine is a nonselective A1 and A2A adenosine receptor antagonist. Chronic caffeine intake may delay the onset of Alzheimer’s disease (Maia & de Mendonca, 2002) and Parkinson’s disease (Schwarzschild & Chen, 2002). Adenosine may direct both proinflammatory and antiinflammatory functions, depending on which subtype of adenosine receptor and which type of inflammatory cell are involved and the duration of the neuroinflammation (Dunwiddie & Masino, 2001; Farber & Kettenmann, 2006). The mechanism underlying the effect of adenosine A1 and A2A receptor antagonism on the level of microglial activation in the current study is unknown. A1 receptors on glutamatergic terminals form heteromeric complexes with A2A receptors; this A1–A2A receptor heteromer may provide a mechanism by which adenosine can dosedependently control glutamate release (Sitkovsky & Ohta, 2005). A2A receptors are located on glutamatergic terminals; A2A receptor antagonists can attenuate the release of glutamate in the presence of ischemia (Marcoli et al., 2003) possibly by acting on A2A receptors on astrocytes (Pintor et al., 2004). The reduced release of glutamate and the subsequent reduction in activation of the NMDA channel on neurons may underlie the effects of caffeine in the current study. Consistent with this hypothesis, we have recently shown that selective antagonism of NMDA receptors can reduce microglia activation in the DG (Rosi et al., 2003; Wenk, Parson, & Danysz, 2006) suggesting an influence of adenosine receptors on microglia activation that might be linked to the modulation of glutamate synaptic transmission or neuronal activity. The reduction in glutamatergic signaling may also contribute to the neuroprotective effects of caffeine (Kalda et al., 2006) and its therapeutic potential for preventing neurodegenerative diseases associated with neuroinflammation (Wenk & Hauss-Wegrzyniak, 2003; Wenk et al., 2006).

NEUROPEPTIDES The tissue concentration of neuropeptides is usually about three orders of magnitude lower than the classical neurotransmitters, such as acetylcholine and the catecholamines (Siegel et al., 2006). The synthesis, postproduction modifications, and inactivation of neuropeptides stand in contrast to the way the brain metabolizes the neurotransmitters discussed thus far. Neuropeptides are initially produced as large precursor molecules called proproteins that are further processes to preproproteins that are then further

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enzymatically processed to the final chemically stable polypeptide product that is stored in the axon terminal in large, dense-core vesicles until released. Once released, neuropeptides are hydrolyzed in the extracellular space to individual amino acids or small polypeptides; these are further catabolized rather than being recycled by re-uptake and then reused again. Therefore, the life cycle of neuropeptides is quite costly to the cell because the process is energetically inefficient. Neuropeptides commonly coexist within, and are released by, the same neuron however the rules governing their activity-dependent release may be different; for example, the release of the neuropeptide is usually from a different part of the terminal and may require a burst of high frequency stimulation and the subsequent influx of greater numbers of calcium ions. In addition, neuropeptides often coexist with other, structurally unrelated, neuropeptides, particularly within the hypothalamus and brain stem regions. Some small polypeptide products of the neuropeptides catabolism may be able to influence the interaction of coexisting neurotransmitters. Many neuropeptides found in invertebrates are insulinlike peptides, which suggests a shared evolutionary history, particularly since insulin has been found in protozoa, bacteria, fungi, invertebrates, and vertebrates. The neuropeptides growth hormone and prolactin may have diverged from a common ancestor about 350 million years ago (Miller et al., 1983). The primitive multicellular Hydra has a nervous system that uses neuropeptides as neurotransmitters, suggesting that neuropeptides were the first signaling molecules used by primitive nervous systems (Grimmelikhuijzen, Leviev, & Carstensen, 1996). Neuropeptides in primitive animals are constructed and stored in a manner similar to that found in vertebrates (Westfall, Sayyar, Elliott, & Grimmelikhuijzen, 1995); they produce excitatory or inhibitory actions when tested on vertebrate tissues. Postsynaptically, neuropeptides are more likely to produce a slow, longer-lasting change in membrane conductance than the classical amine transmitters. Pharmacological manipulation of neuropeptide systems has been most successful with the endogenous opiate neurotransmitter receptors, primarily because Mother Nature provided the first example, morphine, for the subsequent guidance of synthetic chemists. The number and variety of neuropeptide receptor agonists and antagonists for nonopiate systems remains comparatively limited. Pharmacological manipulation of neuropeptide receptors is also complicated by the fact that active neuropeptide receptors are not confined to the synapse and there is not always a direct corresponding relationship between the presence of a neuropeptide receptor and the neuropeptide itself.

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SUMMARY Given the limitations of space, the interested reader may want to examine some excellent texts on brain chemistry and pharmacology to further supplement their knowledge (Cooper et al., 1996; Meyer & Quenzer, 2005; Siegel et al., 2006; Wild & Benzel, 1994).

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Chapter 6

Neuroendocrinology: Mechanisms by Which Hormones Affect Behaviors DONALD W. PFAFF, MARC TETEL, AND JUSTINE SCHOBER

PRINCIPLE 1: IT IS POSSIBLE TO DISCERN MECHANISMS BY WHICH HORMONES AFFECT MAMMALIAN BEHAVIORS

The field of work that discovers how endocrine signaling agents affect the brain in order to regulate behavior has become highly developed during the past 50 years. As a result, it is possible to state principles of hormone/behavior relations and their mechanisms, rather than simply presenting a compendium of facts. For the latter, consult Hormones, Brain and Behavior, 5 volumes and 4,100 pages long, written by over 100 experts. Here we will try to make points that relate to the general features of neuroendocrine systems relevant for behavioral science. Most of our examples come from reproductive neuroendocrine mechanisms because progress has been most rapid in this area for four reasons: (1) Mating behaviors are specific, well-defined, and easily studied in the laboratory in their natural form; (2) the molecular biology of steroid sex hormones is best understood; (3) the stimuli eliciting these behaviors are relatively simple; and (4) the motoric responses themselves are stereotyped, do not need to be learned, and of relatively simple topography. Another very well-developed area of work covers mechanisms of stress hormones. For that topic, see Chapter 62. We have organized several principles of neuroendocrine mechanisms related to behavior, stated in the most general form we feel is justifiable. While this chapter is restricted in scope in order to maintain coherence, the range of hormone/behavior phenomena is breathtaking. Hormones do not “cause” behaviors; they alter the probabilities of given responses to fixed stimuli. And, the probabilities of a given response may be raised or lowered by hormone treatment. Further, the behavioral response to a hormone can depend on stage of development and environmental context. That said, the general principles discussed next remain true under a very wide variety of circumstances.

The proof that it is possible to work out detailed neuroanatomical, neurophysiological, and genomic mechanisms in a neuroendocrine system that regulates behavior came in four steps. 1. The localization of hormone target neurons in the brain was determined and estrogen-binding neurons in a limbic/hypothalamic system were discovered. The discovery initially was made in rat brain (Figure 6.1), but work on fish CNS through monkey CNS showed it to be a general vertebrate system. The neuroanatomical research was followed up by histochemical findings that demonstrated consequences of hormone binding for electrophysiological activity and neuronal growth. 2. The neural circuit for a hormone-dependent vertebrate behavior (Figure 6.2), the estrogen-dependent lordosis behavior (Pfaff, 1980), was worked out. 3. We found hormone-dependent genes in the brain (Figure 6.3). Their induction by estrogenic hormones has temporal, spatial, and gender specificities appropriate to reproductive behavior. 4. In turn, the products of some of these hormonedependent genes are required for hormone-dependent lordosis behavior (Pfaff, 1999). Taken together, these four findings showed that specific neurochemical reactions in specific parts of the brain determine a specific mammalian behavior.

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PRINCIPLE 2: THESE MECHANISMS WORK AT SEVERAL OVERLAPPING LEVELS OF CELLULAR FUNCTION, COVERING A WIDE RANGE OF PHYSICAL DIMENSIONS The four steps listed take a problem from genomic transcriptional alterations to physiological alterations to mammalian behavior. Starting with mechanisms at the level of the very smallest physical dimensions, it is known that lordosis behavior depends on estrogen receptor-alpha, but not on its gene duplication product, estrogen receptor-beta. The differences between the ligand-binding lipophilic pits of these two hormone receptors is measured in angstrom units. Hormone-dependent currents in the ventromedial hypothalamic neurons at the top of the circuit for lordosis behavior (Figure 6.2) are carried by ions such as sodium and potassium. Hormone-dependent gene expression is understood down to the single DNA nucleotide level, when the estrogen receptors bind to specific nucleotide sequences, estrogen response elements, on the chromosome. At the biochemical level, neuroendocrinologists understand many of the cellular pathways involved in transducing effects of sex hormones into cellular changes that underlie sex behavior. For example, estrogens not only affect the gene for the neuropeptide oxytocin, but also increase

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Figure 6.1 Looking at two schematic sagittal sections of the rat brain from the left side, the black dots indicate the distribution of nerve cells expressing estrogen receptors. Note. From Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation, by D. W. Pfaff, 1999, Cambridge, MA: MIT Press. Reprinted with permission.

transduction for the oxytocin receptor (Figure 6.3). Thus, the neuropeptide, in order to be behaviorally active, must leave the rough endoplasmic reticulum where it is synthesized, directed toward the axon hillock, travel down the axon, be released from the presynaptic ending, and bind to its receptor. Likewise, molecular neuroendocrinologists have worked out some of the protein phosphorylation cascades that mediate membrane-initiated effects of steroid sex hormones (Rønnekleiv & Kelly, 2005). At the level of neuronal circuitry, the production of lordosis behavior is understood from the lumbosacral spinal cord up to the hypothalamus (Figure 6.2). The primary role of the telencephalon (e.g., the preoptic area, the septum, and the amygdala) is to inhibit lordosis behavior. What about the local environment of the female, and how might it affect lordosis? First and most obvious, signals from a reproductively competent conspecific are required. Among rodents, testosterone-dependent signals from a potential mating partner are likely to be carried by the olfactory or vomeronasal systems. Other animals might use other sensory systems, with the greatest variety and subtlety being used by the species with the highest capacity for information transfer, humans. In addition, however, the local environment has permissive and suppressive effects. The environment must afford the basis for

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Principle 2 Medial preoptic Medial ant. hypothalamus Hypothal Ventromedial Module nuclear hypothalamus

Estradiol Midbrain central gray

Midbrain reticular form

Figure 6.2 Drawing of the basic working circuit for the production of lordosis behavior in female quadripeds. Note. The circuitry is bilaterally symmetric and is plotted on just one side for visual clarity. From Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation, by D. W. Pfaff, 1999, Cambridge, MA: MIT Press. Adapted with permission.

Lat. vestib. nuclear Lower Brainstem Module

Medullary reticular formation

Spinal cord

Midbrain Module

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Lateral vestibulosp. and reticulosp. tracts

Dorsal roots L1, L2

Stimuli

Spinal Module

L5, L6, S1

Pr Re essu cep re tor s

Flanks

Skin of rump tailbase perineum

Lat. longissimus and transverso – spinalis

Lordosis response

Figure 6.3 List of genes discovered to have two properties: that estrogens (E) having bound to estrogen receptors (ER) elevate their mRNA transcript levels; and that their gene products foster female reproductive behaviors.

Gene turned on (in hypothalamus) rRNA and growth

Note. The exception is prostaglandin D synthase, where hormones work to foster the behavior by disinhibition. From Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation, by D. W. Pfaff, 1999, Cambridge, MA: MIT Press. Adapted with permission.

Progesterone receptor Nitric oxide synthase

ER-α E binds

ER-β

Adrenergic α1 receptor Muscarinic receptors

Female reproductive behaviors

Enkephalin X Opioid receptors Oxytocin X Oxytocin receptor

(in preoptic area) GnRH X GnRH receptor Prostaglandin-D synthase ( )

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(a)

(b)

IFF Testosterone: flank gland, scent marks

Olfaction (Long time, wide space)

IFF Estradiol: scent marks

E2

Testosterone Activity increased

Up the pheromone gradient

Activity increased

Ultrasound

Odor preference

(Limited t,s)

Cutaneous Stimuli

(Lordosis)

(Time)

Very rapid forward move and sudden stop

(Space)

Figure 6.4 Feed-forward mechanisms in reproductive behavior help to guarantee that reproductively competent conspecifics mate. Note. Signaling among hamsters over a long time period (A) and between rats over a short time period (B) has this character. In both cases, the “hormone-dependent behavioral funnels” help to move males and females into the same place at the same time so that they can mate. From Pfaff, Kow, Loose, and Flanagan-Cato (in preparation). Adapted with permission.

Move closer to burrow

. sion Ten port Sup ght. i We Lordosis

Follow

Encourage Proper mount

Mount

Time Fertilization

adequate nutrition of the female. Otherwise (a) she will not ovulate, and (b) electrical activity in her ventromedial hypothalamic neurons will not be high enough to trigger the rest of the lordosis behavior circuitry. The environment may also be a source of marked stress, which would inhibit lordosis behavior both directly and indirectly, the latter being shown by the female not leaving her home nest to engage in courtship behaviors that attract males. At the level of social behaviors, neuroendocrinologists have worked out how courtship behaviors by the female laboratory rat lead to successful mounting by the male and subsequent lordosis by the female. The female runs forward very rapidly then, equally suddenly, brakes to a full stop. This braking tenses her leg and postural muscles so that she is braced and ready to support the weight of the much larger male. Second, that very high degree of muscular tension itself primes lordosis behavior circuitry. Third, that topography of movement by the female causes the male that is following to bump into the female in the correct position for mounting so that even an inexperienced male will mount the braked female properly (Figure 6.4). Finally, reproduction in many animals is seasonal. It is important for offspring to be born in seasons that offer adequate food supplies. Since the distance to the sun is measured in light-years, this is the largest dimension bearing on neuroendocrine mechanisms regulating behavior. It is important to emphasize that all of these levels of mechanisms, from molecular reactions and ion flows

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measured in angstrom units to seasonal fluctuations dependent on day length, must work together for neuroendocrine regulation of reproduction to work in a biologically adaptive fashion—that is, for reproduction to occur successfully, but not to be attempted when it would be fruitless or dangerous.

PRINCIPLE 3: MOLECULAR ASPECTS OF HORMONE ACTION IN THE BRAIN USE THE SAME TYPES OF BIOCHEMISTRY AS IN OTHER HORMONE-DEPENDENT ORGANS The basic features of steroid hormone actions on neuroendocrine cells are indistinguishable from those discovered in tissues outside the brain. Nuclear receptors represent a superfamily of transcriptional activators that can be divided into subfamilies based on phylogenetic analysis (Evans, 1988; Mangelsdorf et al., 1995; Tsai & O’Malley, 1994). The classic steroid receptors represent the type I subfamily and include receptors for estrogens, progestins, androgens, glucocorticoids, and mineralocorticoids. Receptors for thyroid hormone, vitamin D3, all-trans retinoic acid, and 9-cis retinoic acid comprise the type II receptors. The third subfamily includes the orphan nuclear receptors, which have no known ligands (Benoit et al., 2006). Although this discussion focuses on ligand-dependent genomic mechanisms of action of the type I steroid receptors, studies are revealing an increasing role for nongenomic mechanisms

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Principle 3

via membrane-associated receptors in steroid action (Lange, 2007; Vasudevan & Pfaff, 2007). Mutagenesis studies reveal that steroid receptors share a modular domain structure (Evans, 1988; Figure 6.5). The amino terminal domain is highly variable and contains an activation function (AF-1) that regulates the level and specificity of activation of target genes (Tora, Gronemeyer, Turcotte, Gaub, & Chambon, 1988). The centrally located and conserved DNA-binding domain (DBD) contains two zinc fingers to facilitate receptor binding to DNA (Freedman & Luisi, 1993). The flexible hinge region is important in dimerization (Tetel et al., 1997) and in some receptors contains nuclear localization sequences (Ylikomi, Bocquel, Berry, Gronemeyer, & Chambon, 1992). The carboxyl-terminal ligand-binding domain (LBD) contains another activation function (AF-2) and is essential for ligand-dependent activation, dimerization, and binding of many cofactors (Lees, Fawell, & Parker, 1989; Oñate, Tsai, Tsai, & O’Malley, 1995; Shiau et al., 1998; Tetel et al., 1997). Some of the steroid receptors exist in two forms. For example, ER and ER are transcribed from different genes (Kuiper, Enmark, Pelto-Huikko, Nilsson, & Gustafsson, 1996), while the full-length PR-B and truncated PR-A are encoded by the same gene (Kastner et al., 1990). In both cases, the ER (Nomura et al., 2006) and PR

N

NTD

DBD

h

LBD

AF-1

AF-2

C Activation domains

Figure 6.5 Modular domain structure of steroid receptors. Note. AF  Activation function; DBD  DNA-binding domain; h  Hinge region; LBD  Ligand-binding domain; NTD  N-terminal domain.

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(Mani, Reyna, Chen, Mulac-Jericevic, & Conneely, 2006) subtypes have different biological functions. Since the discovery of estrogen receptors almost 4 decades ago (Gorski, Toft, Shyamala, Smith, & Notides, 1968; Jensen et al., 1968), a variety of in vitro and cell culture studies have elucidated much about the molecular mechanisms of steroid receptor action. The classic, ligand-dependent, genomic mechanism of action of steroid receptors is shown in Figure 6.6. In the absence of ligands, inactive steroid receptors are bound to heat-shock proteins and other immunophilins (Pratt, Galigniana, Morishima, & Murphy, 2004). Upon binding hormone, steroid receptors undergo a conformational change that causes dissociation of these heat-shock proteins and immunophilins, which allows receptors to dimerize (DeMarzo, Beck, Oñate, & Edwards, 1991). Activated receptor dimers bind preferentially to steroid response elements (SRE) in the promoter region of steroid-responsive target genes (Beato & Sánchez-Pacheco, 1996; Evans, 1988). These SREs consist of partial palindromic hexanucleotide sequences that are separated by an invariant three-nucleotide spacer (Beato & Sánchez-Pacheco, 1996). Binding of receptors to DNA increases or decreases gene transcription by altering the rate of recruitment of general transcription factors and influencing the recruitment of RNA polymerase II to the initiation site (Kininis et al., 2007; Klein-Hitpass et al., 1990). It is thought that steroids elicit many of their biological effects in brain by acting through their respective receptors to alter neuronal gene transcription, via mechanisms similar to those described previously, and cause changes in hormone-dependent behavior and physiology (Blaustein & Mani, 2006; Pfaff, 1997). For example, estrogens elevate transcription from the genes that encode the progesterone

Steroid p/CAF CBP SRCs

hsp 

SR

SR SR

Inactive steroid receptor

Active receptor dimers

Expression of steroid-induced genes

SR SR Steroid response element

Cytoplasm

Pol II

Nucleus

Figure 6.6 Ligand-dependent genomic mechanism of action of steroid receptors. Note. CBP  CREB-binding protein; hsp  Heat-shock proteins; p/CAF  p300/CBP-associated factor; Pol II RNA polymerase II; SR Steroid receptor; SRCs  Steroid receptor coactivator family (p160s); SRE  Steroid response element.

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receptor and the opioid peptide enkephalin in a manner that shows the neuroanatomical specificity, the temporal features, and the sex dimorphism that permits those gene products to foster the female sex behavior lordosis in the female but not the male (Pfaff, 1999). Coactivators of Steroid Receptors In contrast to the historical perspective of nuclear receptors acting alone by binding to their appropriate response elements on DNA, contemporary research reveals a wide set of nuclear proteins, called coactivators, that modulate behavioral and other physiological actions of hormones according to context. A critical component of efficient steroid receptor transcription is the recruitment of nuclear receptor coactivators, which dramatically enhance transcriptional activity (Lonard & O’Malley, 2006; O’Malley, 2006; Rosenfeld, Lunyak, & Glass, 2006). Under most conditions, steroid receptors interact with coactivators in the presence of an agonist, but not in the absence of ligands or in the presence of an antagonist (McInerney, Tsai, O’Malley, & Katzenellenbogen, 1996; Oñate et al., 1995; Shiau et al., 1998; Tanenbaum, Wang, Williams, & Sigler, 1998; but see also Dutertre & Smith, 2003; Oñate et al., 1998; Webb et al., 1998). It has been proposed that nuclear receptor coactivators influence receptor transcription through a variety of mechanisms, including acetylation of histones, methylation, phosphorylation, and chromatin remodeling (Lonard & O’Malley, 2006; Rosenfeld et al., 2006). The first steroid receptor coactivator to be cloned was steroid receptor coactivator-1 (SRC-1/NcoA-1; Oñate et al., 1995), which was later found to be a member of a larger family of p160 proteins that includes SRC-2 (also known as GRIP1, TIF2 and NCoA-2; Voegel, Heine, Zechel, Chambon, & Gronemeyer, 1996) and SRC-3 (AIB1, TRAM-1, p/CIP, ACTR, RAC3; Anzick et al., 1997). The SRC family of coactivators physically interacts with steroid receptors, including ER and PR, in a ligand-dependent manner (Oñate et al., 1995; Lonard & O’Malley, 2006; Rosenfeld et al., 2006). In cell culture, hormone-induced transactivation of PR is reduced by coexpression of ER, presumably due to squelching or sequestering of shared coactivators. This squelching effect can be reversed by over-expression of SRC-1, suggesting that coactivators are a limiting factor necessary for full transcriptional activation of receptors (Oñate et al., 1995). In further support of this concept, over-expression of SRC-1 relieves thyroid hormone receptor inhibition of ER-mediated transcription in a neuroendocrine model (Vasudevan et al., 2001). It has

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been suggested that the SRC family of coactivators acts as a platform to allow the recruitment of other coactivators, including CREB-binding protein (CBP) and p300/ CBP-associated protein (p/CAF), that possess histone acetyltransferase activity and aid in chromatin remodeling (Kamei et al., 1996; McKenna, Nawaz, Tsai, Tsai, & O’Malley, 1998; Smith, Oñate, Tsai, & O’Malley, 1996; Figure 6.2). Finally, cell culture studies provide evidence that steroid receptor-mediated recruitment of distinct coactivators (e.g., SRC-1 versus SRC-2) can result in different chromatin modifications and modulate the transcription of steroid-responsive genes (Li, Wong, Tsai, & O’Malley, 2003). Steroid Receptor Coactivator Function in the Brain While much is known about the molecular mechanisms of nuclear receptor coactivators from a variety of cell culture studies (Lonard & O’Malley, 2006; O’Malley, 2006; Rosenfeld et al., 2006), we are just beginning to understand their role in hormone action in brain (Molenda, Kilts, Allen, & Tetel, 2003). SRC-1 mRNA and protein are expressed at high levels in the cortex, hypothalamus, and hippocampus of rodents (Auger, Tetel, & McCarthy, 2000; Martinez de Arrieta, Koibuchi, & Chin, 2000; Meijer, Steenbergen, & de Kloet, 2000; Misiti, Schomburg, Yen, & Chin, 1998; Molenda, Griffin, Auger, McCarthy, & Tetel, 2002; Ogawa, Nishi, & Kawata, 2001; Shearman, Zylka, Reppert, & Weaver, 1999) and birds (Charlier, Lakaye, Ball, & Balthazart, 2002). In order for coactivators to function with steroid receptors, they must be expressed in the same cells. Indeed, SRC-1 is expressed in the majority of estrogeninduced PR cells in reproductively relevant brain regions, including the VMN, medial preoptic area, and arcuate nucleus (Tetel, Siegal, & Murphy, 2007). The expression of the SRC family of coactivators in brain appears to be regulated by a variety of factors, including hormones (Camacho-Arroyo, Neri-Gomez, Gonzalez-Arenas, & Guerra-Araiza, 2005; Charlier, Ball, & Balthazart, 2006; Iannacone, Yan, Gauger, Dowling, & Zoeller, 2002; Maerkel, Durrer, Henseler, Schlumpf, & Lichtensteiger, 2007; McGinnis, Lumia, Tetel, Molenda-Figuiera, & Possidente, 2007; Mitev, Wolf, Almeida, & Patchev, 2003; Ramos & Weiss, 2006), day length (Tetel, Ungar, Hassan, & Bittman, 2004), and stress (Bousios, Karandrea, Kittas, & Kitraki, 2001; Charlier et al., 2006; Meijer, van der Laan, Lachize, Steenbergen, & de Kloet, 2006). More recently, the function of nuclear receptor coactivators in hormone action in brain and behavior has been

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Principle 4

investigated. One clever set of experiments used “antisense” DNA oligomers. These are short sequences of DNA, usually 15 to 30 nucleotide bases, that are complementary to a chosen sequence in the messenger RNA being targeted. They ruin that messenger RNA’s function by two mechanisms: by rendering the messenger RNA too fat to fit into the ribosome to be translated, and by rendering the messenger RNA susceptible to breakdown by a degrading enzyme. In the developing rodent brain, antisense to SRC-1 reduced masculinization of the sexually dimorphic nucleus, indicating SRC-1 is involved in hormone-mediated sexual differentiation of the brain (Auger etal., 2000). This is important because this nucleus is one of a set of structures that shows hormonally and developmentally dependent sexual dimorphisms and may play an important role in the sexual differentiation and physiological regulation of brain and behavior. In the adult brain, SRC-1 and SRC-2 function in the VMN to modulate ER-mediated transactivation of the behaviorally relevant PR gene (Apostolakis, Ramamurphy, Zhou, Oñate, & O’Malley, 2002; Molenda et al., 2002). In addition, SRC-1 acts in the VMN to regulate both ER- and PR-dependent aspects of female sexual behavior in rats (Molenda-Figueira et al., 2006). In the adult quail brain, SRC-1 modulates hormone-dependent gene expression, brain plasticity, and behavior (Charlier, Ball, & Balthazart, 2005). Finally, the p160 coactivators function in glucocorticoid receptor action in glial cells (Grenier et al., 2005). Taken together, these findings indicate that the SRC family of coactivators has profound effects on hormone action in brain and the regulation of behavior. The mechanisms by which steroids act in a tissuespecific manner comprise a fundamental issue in steroid hormone action. The field of Neuroendocrinology is poised to make dramatic gains in understanding how steroids regulate gene expression in brain. Recent investigations indicate that, in addition to the bioavailability of hormone and receptor levels, nuclear receptor coactivators are critical regulatory molecules in hormone-dependent activation of genes in the brain and the regulation of behavior.

reverse(!)—the nature of the causal relation between a hormone-dependent gene and the behavior (Ogawa, Choleris, & Pfaff, 2004). Here are three examples:

PRINCIPLE 4: GENES CODING FOR HORMONE RECEPTORS DO NOT DRIVE BEHAVIOR DIRECTLY; THEY ARE MODULATED BY SEVERAL TYPES OF ORGANISMIC AND ENVIRONMENTAL FACTORS

Female mice tested for their vigor in nest defense showed a marked effect of the null deletion of ER-. At the beginning of each test, beta-ERKOs showed a much greater number of attacks than wildtype controls. On the other hand, when tested for testosterone-facilitated aggression beta-ERKOs responded with significantly less frequent aggression for a given dose of testosterone. This comparison suggests that the effect of a specific gene on aggressive behavior can depend on the type of aggression tested.

For sex behavior and for aggressive behaviors, factors such as the gender of the animal, the age of the animal, and the nature of the aggressive encounter may alter—and even

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1. Gender: Deleting the ER- gene permanently in a male mouse abolishes aggression (Ogawa, Washburn, Taylor, Lubahn, Korach, & Pfaff, 1998). However, in a female mouse, the same type of mutation increases aggression (Ogawa, Eng, et al., 1998). The results for the two sexes are opposite. Now consider deletion of the gene coding for ER-. In males, such a gene knockout can increase aggression, but in female mice, knocking out ER-beta reduces testosterone-facilitated aggression. From these two sets of contrasts between male and female mice, we infer that the effect of a given gene on aggression depends on the gender in which that gene is expressed. 2. Age: It is obvious that with respect to aggressive behaviors, the magnitude of the phenotype in the ER- knockout male mouse declines with age (Nomura et al., 2002). The strongest increase in aggression consequent to an ER-β gene deletion is just after puberty. While the mechanism for this is still obscure, nevertheless, it is clear that the effect of a specific gene on aggressive behaviors can depend on the age of the animals at which the behavioral assay is conducted. 3. Nature of the aggression: During the resident-intruder paradigm for testing aggression, ER- knockout female mice display high levels of increased aggression toward female intruders. Their aggression persists well beyond that shown by their wildtype littermate controls, whether the intruder is a female mouse treated with estrogens and progestins, or not. In contrast, if the intruder is an olfactory bulbectomized male mouse, the alpha-ERKO female’s aggression is at a very low level, not any different than the wildtype female mouse’s. This comparison shows that the effect of a specific gene on aggressive behavior can depend on the nature of the opponent.

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PRINCIPLE 5: BEHAVIORALLY RELEVANT HORMONE-SENSITIVE GENES GOVERN FUNCTIONAL MODULES FOR THE BIOLOGICALLY ADAPTIVE REGULATION OF BEHAVIOR Because the genes induced by sex hormones and important for lordosis behavior are not all of the same type—they include growth related genes, a transcription factor, genes for neurotransmitter receptors, neuropeptides and their receptors (Figure 6.3), a different kind of organizing principle had to be envisioned. Mong and Pfaff (2004) have conceived of functional modules governed by estrogens acting in the brain. The easiest to understand are the direct effects, from gene induction to neural circuit to behavioral change. Hormone effects on neurotransmitter receptors in ventromedial hypothalamic neurons directly trigger the rest of the lordosis circuit to operate, using the following modules. Noradrenergic -1b receptors, associated with generalized CNS arousal, are induced by estrogen treatment in ventromedial hypothalamic (VMH) cells which govern the rest of the lordosis behavior circuit. Noradrenergic (NA) ascending afferents come into the VMH from the ventral noradrenergic bundle, which originates in arousalrelated neuronal groups A1 and A2, and signals heightened arousal upon stimulation from the male. In biophysical studies, directly applied NA increases the electrical activity of VMH neurons. These VMH neurons are at the top of the lordosis behavior circuit (Figure 6.2), thus fostering reproductive behavior. Muscarinic cholinergic receptors responding to the neurotransmitter acetylcholine are also found on VMH neurons. Estrogen treatment increases their activities as well. Inputs to the VMH come from, among other places, the lateral dorsal nucleus of the tegmentum. Neurons there are part of the ascending arousal pathways, and would signal stimulation from the male upon mounting the female. In any case, inducing muscarinic receptors increases the VMH electrophysiological response to acetylcholine. The enhanced VMH output primes lower pathways in the circuit for lordosis behavior. There are also indirect effects, from gene induction to downstream genes to behavioral change. Some hormone effects occur early, long before the onset of reproductive behaviors, and set the stage for later developments. Neuronal Growth Growth promotion by estrogens in VMH neurons follows from the stimulation of synthesis of ribosomal RNA, which precedes the elaboration of dendrites and synapses on VMH neurons observed after hormonal treatment. The earliest

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estrogen effect is the increase of transcription of ribosomal RNA, followed rapidly by morphological effects, including those in the nucleolus itself and a striking elaboration of rough endoplasmic reticulum in the cytoplasm. Wooley and Cohen (2002) have shown, probably consequent to the phenomena described previously, a stimulatory effect of estrogen treatment on dendritic growth. In the hypothalamus, others have reported that estrogens foster dendritic growth and an increased number of synapses. Therefore, in VMH cells that control lordosis behavior circuitry, estrogens provide the structural basis for increased synaptic activity and, therefore, greater sex-behavior-facilitating output.

Amplification by Progesterone Administration of progesterone 24 or 48 hours after estrogen priming greatly amplifies the effect of estradiol on mating behavior. This effect requires the nuclear progesterone receptor (PR), as mating behavior disappears after antisense DNA against PR mRNA has been administered in the VMH. This behavior also disappears in PR knockout mice. Importantly, PR itself is a transcription factor, so it will be possible to explore downstream progesteronesensitive genes.

GnRH The physiological importance of estrogenic elevation of gonadotropin-releasing hormone (GnRH, LHRH) mRNA levels under positive feedback conditions—as well as elevation of the receptor mRNA for GnRH—must be to synchronize reproductive behavior with the ovulatory surge of luteinizing hormone (LH). The same GnRH decapeptide which stimulates the ovulatory release of gonadotropins also facilitates mating behavior. In many small animals, synchrony of sex behavior with ovulation would be biologically adaptive because it eliminates unnecessary exposure to predation. In this respect, the behavioral effect of this neuropeptide is consonant with its peripheral physiological action. The case of GnRH also brings up a rare, unambiguous proof of an individual gene causally related to a human social behavior. During development in vertebrates ranging from fish to humans, GnRH neurons migrate from their birth place in the olfactory placode into the brain. A human with damage at the Kallmann’s syndrome locus on the X chromosome did not fail to express the GnRH gene in the appropriate neurons. Instead, the neurons failed to migrate out of the olfactory placode (Schwanzel-Fukuda & Pfaff, 1989; Schwanzel-Fukuda, Bick, & Pfaff, 1989). A single gene for the Kall protein accounts for the deficit.

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Principle 6

It is for an extracellular matrix protein that is necessary for the GnRH neuronal migration and that, in fact, decorates the migration route. A striking feature of the phenotype in men is important to note. They have no libido. Here is the causal route. The men have no sexual drive (1) because they have little testosterone, (2) because they have little LH and FSH circulating from the pituitary gland, (3) because no GNRH is coming down the portal circulation to the pituitary from the hypothalamus, (4) because there is no GnRH in the hypothalamus, (5) because the GnRH neurons did not migrate during development into the brain and, (6) because of a mutation in the gene for the Kall protein. Therefore, we can causally connect, step-by-step, an individual gene to an important human social behavior, but at least six causal links are required. This causal route illustrates the complexity of gene/behavior relationships in humans. Some of the indirect effects have a causal route from gene induction to intermediate behaviors (Mong & Pfaff, 2004). That is, some of the genes affected by estrogens work by altering other behaviors which then prepare the animal for the behavior in question, in this case mating.

Analgesia The enkephalin gene is turned on rapidly by estrogens, within about 30 minutes, and this is proven to represent a hormone-facilitated transcriptional facilitation. The route of action on lordosis, of the enkephalin gene product, is indirect, through other behaviors. That is, we propose that, through the reduction of pain, enkephalins help to allow the female to engage in mating behavior despite the mauling she receives from the male. The strong somatosensory and interoceptive stimuli that ordinarily would be treated by the female as noxious are now tolerable and allow successful mating to proceed.

Anxiety Reduction The oxytocin gene and the gene for its receptor are both expressed by hypothalamic neurons at higher levels in the presence of estrogens. The indirect route of action of this multiplicative set of gene inductions, on mating behavior, is likely through a behavioral link: anxiety reduction allows courtship and mating. This proposal is consistent with previous formulations: oxytocin has been conceived as protecting instinctive behaviors connected with reproduction, maternity, and other social behaviors from the disruptive effects of stress. Indeed, oxytocin has an anxiolytic action in the presence of estrogens (which presumably elevate the oxytocin receptor gene product; McCarthy, McDonald, Brooks, & Goldman, 1996).

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Individual-Specific Olfactory Cues Nonvolatile Volatile Hypothalamus PVN and SON Vomeronasal organ

Main olfactory bulb/system

Accessory olfactory bulb/system

OT

ER β

OT E

OT

Blood stream OTR

Amygdala

ER α

E E

Social recognition

Ovaries: estrogen (E) production

Figure 6.7 Estrogens increasing oxytocin transcription in the hypothalamus and increasing oxytocin receptor transcription in the amygdala help to foster social recognition in mice. Note. Thus, four genes (estrogen receptor-alpha, estrogen receptor-beta, oxytocin, and oxytocin receptor) and their products form a functional module that supports social recognition. From “An Estrogen-Dependent Four-Gene Micronet Regulating Social Recognition: A Study with Oxytocin and Estrogen Receptor- and: Knockout Mice,” by E. Choleris, Gustafsson, et al., 2003, Proceedings of the National Academy of Sciences, p. 6196. Adapted with permission.

Social Recognition The induction of the oxytocin gene by estrogens is an ER- dependent, behaviorally significant phenomenon, reassuring since only ER- gene expression is found in oxytocinergic cells In turn, oxytocinergic projections to the amygdala, where oxytocin receptor gene transcription under the control of ER-alpha, are thought to be important for social recognition in mice, which helps to prevent aggression. Altogether, these data invoke the idea of a four-gene micronet (Choleris, Gustafsson, et al., 2003; Choleris, Little, etal., 2007) important for social behaviors (Figure 6.7). All of these modules support reproductive behaviors from the earliest estrogenic actions on the brain to the occurrences of mating behaviors themselves.

PRINCIPLE 6: TEMPORAL ASPECTS OF HORMONE ACTION IN THE BRAIN ARE IMPORTANT By temporal aspects, we mean questions of both duration and order of events. For some steroid sex hormone effects on a wide variety of behaviors, longer durations of hormone administration make

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for greater behavioral increases. A case in point is the effect of testosterone on male-typical sex behaviors in male rodents. Typically, for increased mounting behavior, and especially for the pelvic thrusting, penile insertions, and ejaculation, many days or several weeks of continuous testosterone treatment would be required for high levels of response. Hormone effects on female sex behaviors are more diverse. Consider first the role of estrogenic hormones. A long priming period is useful for high levels of lordosis behavior. If the female is cycling normally or has been exposed recently to substantial levels of estrogens, then 48 hours is enough. The longer the female is without ovarian estrogens, the longer the priming period required for female sex behavior. A molecular interpretation of the mechanism operating in this case is the following: That prolonged absence of estrogens allows the decline of nuclear coactivator protein levels (see Chapter 18), which are needed to transduce the nuclear binding of ERs into behaviorally relevant transcriptional facilitations. A surprising development with respect to long priming actions of estrogens came from biochemical work in the uterus. A priming period of 24 hours could be substituted for by two brief exposures to estrogen, suitably timed. We followed this up for the CNS and behavior. Again, an estrogen priming of 24 hours could be replaced by two 1-hour exposures, the first from hour 0 to 1 and the second beginning between 4 and 13 hours after the first. This pulse schedule was effective for inducing PR and as well as for lordosis behavior. Questions of Order of Administration In females, estrogen priming must be followed by progesterone for optimal behavioral facilitation. The progesterone, if it is timed correctly, amplifies the estrogenic effect. The required temporal parameters for progesterone are much different from those for estradiol—there is a biphasic action of progesterone both on pituitary release of LH and on behavior. In female rats, for example, 2 to 5 hours after progesterone injection, LH release and lordosis behavior are facilitated mightily. But the continued presence of progesterone, several hours later, actually inhibits both the endocrine output (LH) and the behavioral output (lordosis). In other neuroendocrine cases, brevity of hormone action is not only effective but also is actually required. Gonadotropin-releasing hormone (GnRH, also known as LHRH) is well known to show a pulsatile pattern of release. It is fascinating that populations of identical GnRH neurons can manage pulsatile output. In fact, pulsatile outputs of GnRH are absolutely necessary for the pituitary to respond with substantial gonadotropin release into the

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blood. A steady, high level of administration of GnRH actually turns off the gonadotropin system and thus can be used either (a) as a birth control device or (b) to ramp down the system in case of gonadal cancers. Some of these phenomena are beginning to be understood at the molecular level. The GnRH promoter is activated in an episodic fashion in GnRH neuronal cultures. A specific 410 base region of the GnRH promoter is required for pulsatile GnRH promoter activity. Within that region, a small five-base site that represents a binding site for the transcription factor Oct-1 is likewise required. In order to achieve fertility, LH has to be given in a pulsatile fashion.

PRINCIPLE 7: IN SOME CASES, A HORMONE REQUIRES METABOLISM OR NEEDS COMBINATIONS WITH OTHER HORMONE(S) TO BE EFFECTIVE BEHAVIORALLY First consider the case of testosterone as a “prohormone”— a steroid whose enzymatically regulated metabolism produces other steroids that are active in actually exerting the behavioral effect. Testosterone, produced mainly by the testis in men and the adrenals in women, is a potent androgen (male sex hormone), that influences many tissues throughout the body, including the CNS, in which testosterone receptors are present. Metabolites, of testosterone, however, also are important hormones and have specific effects that are different from the effects of testosterone itself. Two important testosterone metabolites are dihydrotestosterone (DHT) and estradiol (E2), which are male and female sex steroids, respectively. It is the conversion of testosterone to these two active hormonal metabolites that is critical for male sex behavior. There are specific receptors for each; for DHT these are in peripheral tissues related to the development of secondary sex characteristics, and for E2 these are in both peripheral tissues and the CNS. The major structural changes inherent in the enzymatic conversions of testosterone to DHT and E2 confer receptor specificity to these steroid compounds. The conversion of testosterone to E2 by the sequence of three enzymatic steps that are called aromatase, within the CNS, is considered to be important for the early development of the masculine brain. The aromatase enzyme is concentrated in areas of the brain related to sexual differentiation of the CNS, such as the hypothalamus. In the male, exposure of the developing brain to high concentrations of testosterone, and therefore to high concentrations of E2 converted from testosterone in specific regions, leads to, for example, the tonic secretion of gonadotropins in the adult male versus the cyclic secretion of these hormones in the adult

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Principle 8

female. Many differences in behavior, especially in aggressive behavior, also result from the differential exposure of the developing male and female CNS to testosterone and E2. While testosterone has many androgenic effects throughout the body and is responsible for virilization of internal structures, for example, testicular development, DHT is required for virilization of the external genitalia, that is, penile growth and scrotal development. If the enzyme converting testosterone to DHT, 5-alpha reductase, is genetically deficient, the external genitalia of the newborn infant appear ambiguous (male pseudohermaphroditism). Figure 6.4B schematically illustrates the genitalia of a normal man (left) and the genitalia of a 5-alpha reductase-deficient prepubertal boy. The female-appearing external genitalia belie the presence of an XY sex chromosome complement, functioning testes, although undescended, and a masculinized brain secondary to fetal testosterone exposure. The behavioral consequences of this anatomical alteration are obvious, and they also illustrate how hormone metabolites can influence behaviors through indirect routes. Before it was recognized that this syndrome is inherited and therefore is concentrated in certain families, affected infants were raised as girls. On reaching puberty, however, the increased secretion of testosterone from the pubertal testis led to some DHT being produced, so that many prepubertal “girls” developed a male phallus with erections, scrotal testes, male hair distribution, deepened voice, and male body habitus and psychological characteristics, to the initial consternation of parents and family members. However, the syndrome was quickly recognized to occur in affected families in several parts of the world, for example, in the Dominican Republic, so that newborns with ambiguous genitalia in these families were not forced to grow up as girls. Indeed, some individuals have entered into heterosexual relationships and have been able to function physically and emotionally as men. Others have led isolated lives or retained some female gender identity. The outward switch of sex and gender identity from female to male at puberty in 5-alpha reductase-deficient individuals was initially interpreted as the primacy of nature over nurture; that is, sex hormones being more influential than psychosocial factors imposed since infancy. However, the early recognition that newborn from affected families and with ambiguous genitalia might have masculine pubertal development led to these infants’ being raised either as boys or ambiguously as girls. The nature–versus– nurture dichotomy, therefore, is not as straightforward as some would believe. As already mentioned, for some hormone-dependent behaviors, one hormone is not enough: a combination is required. And for hormone combinations, order of administration can be crucial. Consider the combination of estrogens

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and progestins acting to facilitate reproductive and maternal behaviors. Even for the same pair of hormones, different temporal patterns of combinatorial action will be important for different behaviors. For example, in many female laboratory animals, a long priming treatment of estrogens—48 hours or more—followed by a brief progesterone exposure will promote female-typical sexual behaviors. The neural circuit and some of the genes supporting the mechanisms for this type of behavior have been worked out (see earlier discussion). In a biologically adaptive fashion, the requirement of E followed by P for female sex behavior synchronizes it with ovulation, which has the same combinatorial effects. However, following the estrogen priming, if the progesterone is allowed to stay around for a long time, the opposite effect is seen on sexual behavior. Finally, in a pregnant animal, estrogen levels are high and remain high during parturition. In contrast, progesterone levels start high but decline around the time of giving birth. High levels of estrogens and a declination in progestins make the optimal combination for maternal behavior (Numan & Insel, 2003). A different kind of combination of hormone actions underlies social recognition, motivation, and memory in female laboratory animals. Several labs have shown the powerful effects of oxytocin, and in some species vasopressin, on this cluster of behaviors. In addition, estrogens have an overriding effect in two ways, as noted in Figure 6.7. Working through estrogen receptor- estrogens turn on the oxytocin gene; and working through ER- they turn on the oxytocin receptor gene. Therefore, in this combination of hormones, estradiol and oxytocin, one has a superordinate relation to the other. Estrogens have to come first, to turn on the gene for the oxytocin receptor, for instance, and oxytocin later.

PRINCIPLE 8: SOME OF THE INFLUENCES ON SPECIFIC HORMONE-BEHAVIOR SYSTEMS ARE REMARKABLY NONSPECIFIC Decades of work have gone into the elucidation of the neuroanatomical pathways and neurophysiological mechanisms related to arousal. Mechanisms for generalized arousal of the CNS (Pfaff, 2006) are fairly well known at the neuroanatomical level. Their most important features emphasize multiplicity and redundancy of ascending arousal pathways in such a way as to prevent failure. Five major neurochemically distinct systems work together to increase arousal. They use norepinephrine, dopamine, serotonin, acetylcholine, and histamine as transmitters. They all begin in the brain stem and converge in the thalamus or in the basal forebrain (Figure 6.8). They overlap and cooperate. Their very multiplicity ensures against failure.

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Neuroendocrinology: Mechanisms by Which Hormones Affect Behaviors A

P

)

cts rA o t x Mo rte ed al co t c ire nt (D Fro

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Limbic cortex Striatum ACh Basal forebrain

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Figure 6.8 Classical ascending systems elevate CNS arousal. Note. From Brain Arousal and Information Theory, by D. Pfaff, 2006, Cambridge, MA: Harvard University Press. Adapted with permission.

Four sensory systems feed ascending arousal pathways in a straightforward fashion. These clearly show how vestibular stimuli, somatosensory, auditory, and taste stimuli on the tongue could arouse an animal or human being. Pain mechanisms further dramatize how a vastly amplified somatosensory signal from the skin or the viscera can wake up and alert an individual. Moreover, pain pathways and sexual cutaneous signals overlap, and share the ability to cause states of high arousal (Figure 6.9). In contrast, electrical impulses triggered by odor stimuli enter the brain through tracts in the basal forebrain, and project to the amygdala, a primary receiving zone which itself is connected with high degrees of arousal, during both sex and fear. Visual stimuli impact CNS arousal pathways both through the outer layers of the superior colliculus and through the reticular and medial cell groups of the thalamus. An important point to reiterate is that these various arousal-related transmitter systems and sensory signals converge. Whether in the basal forebrain or in the medial thalamus, a strong signal for cortical arousal is generated and must be distributed broadly in the cerebral cortex to command the attention of a wide variety of higher level perceptual processers and motor control cell groups. In terms of the general principles illustrated by CNS arousal pathways, it is eminently clear that arousal mechanisms impact neuroendocrine processes by their projections into the hypothalamus. They are bilateral. Unilateral damage in the animal brain or human brain has little effect on

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Heightened arousal Altered motivational state

Shared ascending pathways in A-L columns of spinal cord Stimulusproduced analgesia Sexually relevant

Painful

Somatosensory stimuli

Figure 6.9 Painful and sexually relevant cutaneous stimuli both increase CNS arousal but have different behavioral consequences.

generalized arousal or consciousness. Second, they are bidirectional. In addition to the classical aminergic ascending pathways just mentioned, there are crucial descending pathways (e.g., vasopressin, histamine, orexin). Third, these pathways have been conserved across a variety of species, including humans. Finally, these pathways always potentiate an animal’s or human’s behavioral responsivity.

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Principle 8

These responses may be active approach responses, but in the case of fearful or stressful inputs, these are avoidance responses. It has been hypothesized that some of the most important cells stimulating CNS arousal are medullary gigantocellular neurons that have bifurcating axons, both ascending and descending in the brain stem (Figure 6.10) and that likely contribute to cortical and to autonomic arousal, respectively. Electrical stimulation of these neurons in the rat medullary reticular formation stimulates EEG arousal, decreasing spectral power in the low-frequency delta range and increasing power in the high-frequency gamma range (Wu, Stavarache, Pfaff, & Kow, 2007). Even with these data in hand, further work will be necessary to sort out contradictions in the literature and to specify the exact molecular characteristics of these “master cells” for arousal and the exact mechanisms by which they influence CNS arousal. In neurophysiological terms, cells involved in generalized arousal of the CNS would be expected to respond to a variety of stimuli in several sensory modalities. During electrophysiological recordings from reticular and raphe neurons in the medulla, such neurons have been found (Hubscher & Johnson, 2002; Leung & Mason, 1998, 1999; Martin, Pavlides, & Pfaff, unpublished work). Moving anterior in the brain stem, certain ponto-medullary reticular neurons (Peterson, Anderson, & Filion, 1974) as well as

POA PVNp (OT, AVP) SCN TMN (HA) LHA (Orexin/ Hypocretin)

Brainstem

Autonomic Controls

Figure 6.10 CNS arousal depends on descending systems as well as ascending systems. Note. From Brain Arousal and Information Theory, by D. Pfaff, 2006, Cambridge, MA: Harvard University Press. Adapted with permission.

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the omnipause neurons of Phillips, Ling, and Fuchs (1999) recorded in the pons, also fit the requirement that cells be multimodal in their range of sensitivities and have firing rates correlated with arousal and visual attention. In the midbrain, the work of Horvitz, Stewart, and Jacobs (1997), recording from dopaminergic neurons, revealed responses that were correlated with the activation of behavior, that is, the initiation of motor responses directed toward salient stimuli. These and many other reports supply the neurophysiological basis of generalized arousal responses. Functional Genomics Data are accumulating rapidly with respect to neurochemical and genomic mechanisms for both arousal and stress. A large number of genes, more than 120, participate in regulating generalized CNS arousal. The large number is due to the inclusion of gene-encoding synthetic enzymes, receptors (for serotonin, alone, there are 14), transporters, and catabolic enzymes for both the relevant neurotransmitters and neuropeptides; both those increasing and those decreasing arousal (Pfaff, 2006). As might be expected, sex hormones are involved in CNS arousal. Disruption of the gene-encoding estrogen receptor alpha severely reduced arousal measures in female mice, compared to their wildtype littermate controls (Figure 6.11). Interestingly, disruption of the gene for estrogen receptor beta, a likely gene duplication product, had no significant effect (Garey et al., 2003). There are additional implications of having so many genes controlling arousal mechanisms. The heterogeneity among the genes involved presumably provides for great flexibility of response. The very multiplicity yields the possibility of large numbers of meaningful patterns of gene expression. In a neuroendocrine context, we have shown that one never could understand gene/behavior relations on a one-by-one basis. Moving beyond Beadle and Tatum’s concept from their work with the fungus Neurospora—their classical “one gene/one enzyme” concept—we reached the conclusion that different patterns of gene expression yield different patterns of sociosexual behaviors (Pfaff, Ogawa, Kia, Frohlich, & Kow, 2002). Finally, how do these generalized arousal forces impact specific neuroendocrine mechanisms? The answer lies in the exact neuroanatomical localizations of the receptors for arousal-related transmitters and neuropeptides in brain regions controlling the pituitary and hormone-dependent behaviors. For female sexual behavior, two arousalenhancing transmitters, norepinephrine and histamine, can influence sexual arousal and lordosis behavior by their excitatory effects on the electrical activity of ventromedial hypothalamic neurons (see earlier discussion). Conversely, mu-receptor opioid agonists (Devidze, Lee, Martin, &

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Note. There is no statistically significant effect of ERKO- gene deletion, compared to their respective WT controls. From “Genetic Contributions to Generalized Arousal of Brain and Behavior,” by Garey et al., 2003, Proceedings of the National Academy of Sciences, 100, 11019–11022. Adapted with permission.

Figure 6.11 Estrogen receptor alpha knockout (ERKO-) mice show decreased arousal responses to stimuli in several sensory modalities, compared to their wildtype (WT) littermate controls.

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Principle 9

Pfaff, submitted) and prostaglandin D (Mong et al., 2003), reduce generalized arousal, and as a consequence reduce lordosis behavior. The logical relations among generalized arousal, sexual arousal, and sexual behavior are shown in Table 6.1. More detail on CNS arousal and the activation of behavior can be found in Chapter 23.

PRINCIPLE 9: NEUROENDOCRINE MECHANISMS HAVE BEEN CONSERVED AND CONTINUE TO PROVIDE ADAPTIVE COORDINATION OF BRAIN WITH BODY TO REGULATE BEHAVIOR A modern neuroendocrinologist may not try to explain the psychological side of human mentation—the full range of mental, artistic, self-conscious expressions of the person in states of emotion that involve hormonal changes. However, a tremendous number of neuroanatomical, neurophysiological, genetic, and endocrine mechanisms related to sex behavior has been conserved from the animal brain into the human brain (Figure 6.12). To give just a few examples, the steroid hormones are the same in the human brain as in lower mammals. Steroid receptor chemistry, neuroanatomy, and molecular mechanisms are also conserved. Neuroendocrine system anatomy is much

TABLE 6.1

113

more highly conserved than other parts of the forebrain, and the tendency of hormone-responsive neurons to project to other hormone-responsive neurons is also preserved. The neuroendocrine neuropeptide par excellence is gonadotropin releasing hormone (GnRH, also called LHRH). Its human brain chemistry is identical to that in lower animals, its physiology, its release mechanisms, and its receptor physiology (e.g., in the anterior pituitary gland) are likewise conserved. Most strikingly, the unique migration during development of GnRH neurons from the olfactory pit to their final functional positions in the basal forebrain, discovered in mice (Schwanzel-Fukuda & Pfaff, 1989) also occurs in humans. In fact, as stated earlier, a failure of GnRH neuronal migration in men accounts for the loss of libido in X-chromosome-linked Kallmann’s disease (Schwanzel-Fukuda et al., 1989). Basic mechanisms have remained the same. Transcriptional biology, molecular biology of neurons, chemistries of a variety of neurotransmitters and neuropeptides, electrophysiological mechanisms, and many facets of cellular neuropharmacological effects all link human neuroendocrine systems to those of laboratory mammals. Finally, in terms of the neuroendocrinology of reproduction, the chromosomal biology of sex differences, the mechanisms of sex differentiation of the brain, and the basic requirements of behaviors necessary for sperm to meet egg and fertilize have remained very similar as we move in our

Relations among arousal mechanisms and sex behaviors (male and female). Requires

CNS Features

Mechanisms Require

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VMN/POA neuronal excitation

Sex hormones

Genes for nonspecific ascend systems

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Genes in CNS. molecular biology of neurons. Hypothalamic neuroanatomy Neuronal connections

Hormone receptors Chemistry neuroanatomy

GnRH neuron migration GnRH physiology

Neurotransmitters Neuropeptides Neurochemistry Neuroanatomy Cellular neuroanatomy Cellular neurophysiology Cellular neuropharmacology

Biology of sex differences Requirements for fertilization Steroid Hormones

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Figure 6.12 Hormone-dependent mechanisms conserved from animal to human brains (implications for understanding sexual arousal). Note. Since a remarkable number of neuroendocrine mechanisms have been conserved from animal brains into the human brain, modern neuroendocrine research contributes to the understanding of human neuroendocrine functions and their maladies. All of the structures and functions portrayed in this figure are identical or very similar in the human brain compared to a variety of laboratory animal mammalian subjects. From Drive: Neurobiological and Molecular Mechanisms of Sexual Motivation by D. W. Pfaff, 1999, Cambridge, MA: MIT Press.

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thinking from lower mammals to humans. Likewise, sex differences in aggression provide the same types of statistics among humans as in many lower mammals. Therefore, unless nature, having evolved a full set of working mechanisms for mammalian hormone-dependent behaviors, started an entire new set for humans, then we understand well the most primitive neuroendocrine mechanisms that drive hormone-influenced emotional expressions and behaviors in humans.

PRINCIPLE 10: HORMONE EFFECTS ON BRAIN ARE RELEVANT FOR HUMAN BEHAVIORAL PATHOLOGY Examples of disorders in neuroendocrine mechanisms that lead to serious medical conditions of behavioral pathology include hyperthyroid patients who are extremely jittery and nervous hypothyroid patients who are sluggish and dull, and patients with over-expression of CRF who “burn out” in a mixture of anxiety and depression. Many more examples could be given, but we choose to emphasize a neuroendocrine/behavior example that is so extreme that it constitutes a criminal condition. In the following example, the etiology of the disease is complex but its solution represents an application of neuroendocrine engineering. Pedophilia is classified as a psychosexual disorder. Strong obsession and compulsion components of pedophilia make incorporation of cognitive behavioral skills difficult. Medications that lower sex drive may enhance voluntary control (Berlin, 1983). Testosterone-lowering agents, serotonin re-uptake inhibitors, surgical castration, and stereotaxic neurosurgery have been used to reduce libido, deviant sexual arousal and fantasy, and the frequency of deviant sexual behavior. Pharmacotherapy has included testosterone-lowering agents such as medroxyprogesterone acetate (MPA), cyproterone acetate (CPA) and luteinizing hormone-releasing hormone (LHRH, aka GnRH) inhibitors, and gonadotropin-releasing hormone agonists (GnRH), as well as selective serotonin re-uptake inhibitors (SSRIs). Luprolide acetate (LA), one of several synthesized agonist analogs of LHRH, (aka GnRH), the hypothalamic factor that stimulates gonadotropin release from the pituitary (Vance & Smith, 1984) and produces a paradoxical effect on the pituitary, with initial stimulation of the release of luteinizing hormone (LH) and follicle-stimulating hormone (FSH), followed by inhibition after repeated administration (Belchetz, Plant, Nakai, Keogh, & Knobil, 1978; Bergquist, Nillius, & Wide, 1979; Evans, Doelle, Alexander, Uderman, & Rabin, 1984; Vilchez-Martinez et al., 1974) has been used for this type of therapy. LA causes a reduction in sex hormone

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release (a decrease in testicular steroidogenesis) that is probably secondary to a primary reduction in LH levels (Vance & Smith, 1984). Agonist analogs have also been shown to decrease the number of LH receptors in Leydig’s cells of hypophysectomized rats (Bambino, Schreiber, & Hsueh, 1980; Vance & Smith, 1984). Testosterone levels attained with continued administration of LA were lower than those attained with other medications and may result in LA being a more potent inhibitor of erectile responses compared with other agents. Patients generally become sexually impotent when plasma testosterone levels are less than one quarter of their initial value. A placebo controlled, blinded, multidisciplinary study of LA in pedophiles, detailed the objective effects of this drug on measurable aspects of the arousal response (Schober et al., 2005). One year of therapy on LA, followed by 1 year on a saline-placebo detailed testosterone levels during polygraph testing, plethysmography (PPG) with audio and visual stimuli, viewing time for visual stimuli, and selfreport of urges and masturbatory frequency toward children. Testosterone levels were reduced to castrate levels (less than 50 ng/dI) or one tenth the mean average level. By the second week of treatment, a sustained, profound suppression of testosterone, FSH, and LH remained for duration of treatment with LA. On saline placebo, testosterone levels rose slowly over a 3-month period to approach baseline. During the initial testosterone rise, no patient reported an increase in pedophilic urges, sex drive, or masturbation. Subsequently, levels fell to a mean of 11.6 ng/dL 1 month after the initial injection and remained low until LA was withdrawn. After testosterone fell to castrate levels, the study subjects reported decreased sex drive, decreased pedophilic sexual urges, and decreased masturbation frequency. When LA was replaced with saline placebo, all subjects initially reported no increase in sex drive, pedophilic sexual urges, or masturbation frequency. After 3 months on placebo, testosterone averaged 195 ng/dL, some subjects continued to report no increase in sex drive, pedophilic sexual urges, or masturbation frequency, but others expressed great distress that the medication was losing effectiveness and they were fearful of reoffense. At this time the placebo was revealed to the subjects. All chose to return to LA therapy. Throughout the study, modified visual reaction time results detected the subject’s self-reported choice of interest/preference, and indicated no consistent change in pedophilic interest preference as measured by visual reaction time. Overall, interest preference, as measured by PPG, indicated no consistent change in pedophilic interest preferences with LA therapy. However, the degree to which subjects responded decreased significantly, demonstrating that LA significantly decreased arousal. Penile plethysmography verified self-reported claims of lowered libido, in that LA therapy

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References

caused significant reductions in the magnitude of their sexual arousal pattern. LA significantly reduced pedophilic fantasies, urges, and masturbation on self-report. On all polygraphic assessments, at baseline, almost all responses were classified as deceptive. Deceptive responses about masturbation frequency, urges to initiate sexual contact, and sexual thoughts, decreased dramatically with LA therapy and deceptive responses increased dramatically on placebo. A direct and readily apparent correlation existed between deceptive responses and LA injection. When urges and masturbation frequency decreased on LA, a polygraph indicated almost no deceptive responses. Even with profound testosterone suppression, a complete suppression of arousal did not occur. Low levels of arousal and erectile ability persisted with sufficient tumescence to generate detectable levels on PPG. While on LA, subjects indicated they were better able to focus on employment, relaxation activities, and life planning without continual interruptions by sexual thoughts. All subjects noted a decrease in anxiety, better ability to regulate or control their actions, and increased motivation for work/school activities.

SUMMARY Considerable progress has been made in understanding the neuroanatomical, neurophysiological, and genomic mechanisms of neuroendocrine systems that regulate behavior. We expect that as our knowledge of neuroendocrine mechanisms deepens, it will be even easier to see how their functions contribute to behavioral regulation and their disorders lead to behavioral pathologies.

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Chapter 7

Neuroimmunology STEVEN F. MAIER AND LINDA R. WATKINS

by the discoveries that the activity of immune processes can be classically conditioned (Ader & Cohen, 1975) and that exposure to stressors can alter immune responding (Solomon, 1969). Since learning and stress are “in the brain,” these discoveries suggested that the brain must be able to regulate immune processes. For this reason, this area of investigation has become known as psychoneuroimmunology. These seminal findings were soon followed by work indicating that immune organs are innervated by the sympathetic nervous system (Felten et al., 1987) and that immune cells express receptors for a variety of hormones controlled by the brain (Fahey, Gure, & Munck, 1981), thereby providing pathways by which the brain can contact immune organs and cells and mediate the effects of conditioning and stressors. More recently, it has become clear that communication between the brain and immune systems is bidirectional, with products of the immune system potently regulating the nervous system and phenomena that are the result of neural activity. We focus on this arm of the brain-immune interaction in this chapter because it is immune-to-nervous system communication that has the greatest implications for understanding behavior.

Disciplines within the biological and biomedical sciences have often been defined by organ systems: cardiology, audiology, endocrinology, are but a few examples. This has naturally led investigators to view the system that is the focus of their study as being disconnected from, and operating independently of, other systems. The tendency to think of systems as disconnected has nowhere been more apparent than in the fields of immunology and neuroscience. The immune system and the nervous system have been traditionally thought to operate independently of one another, and even after the research of the past several decades that has documented the close interplay and interaction between these two systems, the most recent texts in immunology do not even contain a reference to the nervous system in their indices, and conversely neuroscience texts typically do not contain even a reference to the immune system. However, living organisms are integrated wholes, and parts do not function in isolation. Because the nervous system is the command and integrative center of the organism, it would be most unlikely for important functions to operate without neural regulation. Functions such as digestion, excretion, the production of energy, and so on are all regulated by the nervous system. For example, the sympathetic nervous system regulates the rate of digestion, and adrenal hormones whose production is under the ultimate control of the hypothalamus regulate energy balance. Host defense against infection and injury is a key function needed for survival and involves widespread mechanisms throughout the body, therefore, it would be unlikely to operate without control from the nervous system. For the nervous system to exercise such control, it must “know” about the status and functioning of the immune system. Thus, it would be unlikely for neural activity to proceed independently of events in the immune system. These two systems are indeed in close communication and potently modulate each other ’s activities. The early research examining the possibility that the nervous and immune systems interact focused primarily on neural regulation of immunity. This emphasis was fueled

THE IMMUNE SYSTEM Defense against infection by microorganisms has been crucial for survival since the earliest periods of evolution. As a result, organisms have developed a complex array of defensive mechanisms to fight infection, and relatedly, to promote tissue repair after injury. If a microorganism succeeds in evading a set of passive bodily defenses (e.g., the epithelial surfaces of the body) it must first be recognized as “nonself ” and/or “dangerous.” You are most likely familiar with what is called “adaptive” or “acquired” immunity. Here, several types of leukocytes (T-cells and B-cells) recognize specific molecular sites on foreign invaders called antigenic sites (an antigen is defined as a molecule that can 119

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lead to the generation of antibodies). These leukocytes can do so because they express very specific antigen receptors on their surface. Because there are an enormous number of different antigens (roughly 106 for humans), there can be only a small number of T- or B-cells that express the receptor for any particular antigen. This small number of cells would not be sufficient for immune defense, and so the first step in adaptive immunity after antigen presentation to the T-cells is a clonal expansion of cells that express the antigen receptor in question. It takes 3 or 4 days to go through a large enough number of cell cycles to generate a sufficient number of T-cells that recognize the invading microbe, and only then can the effector processes that rid the organism of the invader develop. Innate Immunity Because it takes 3 or 4 days to even begin to generate the effectors of the adaptive immune system (cytotoxic T-cells, antibody, etc.) that can attack the bacteria, virus, and so on, the adaptive immune system cannot be the first line of immune defense. This more rapid defense is accomplished by the innate immune system. If a microorganism crosses an epithelial barrier, it rapidly encounters mononuclear phagocytes called macrophages (literally “big eaters”) that reside in submucosal tissues (see Figure 7.1). Macrophages mature continuously from monocytes that leave the circulation. As do T-cells and B-cells, they recognize nonself via surface receptors, but the macrophage receptors are quite different from those on the cells of the adaptive immune system. Instead of recognizing highly specific antigens, they instead recognize very general features of microorganisms called pathogen-associated molecular patterns (PAMPs). These PAMPs are general molecular motifs that are present on many microorganisms, but are not present in host tissue. For example, viruses almost always express double-stranded RNA, and so macrophages have a receptor (TLR-3) that is ligated by double-stranded RNA. There are only roughly 20 different receptors on macrophages that are used to recognize PAMPs and discriminate self from nonself, rather than the 106 on cells of the adaptive immune system. Thus, all macrophages express all of the receptors, and so clonal expansion to generate more macrophages is not necessary. That is, macrophages are ready right away to engage in immune defense. The binding of microorganisms by the macrophage receptors initiates a number of responses. First, some of the receptors lead the macrophage to engulf the microorganism, leading to its destruction. A variety of toxic substances are also produced (e.g., nitric oxide) that can kill microbes that are engulfed and that are near the macrophage. This process of phagocytosis is very rapid, beginning almost

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Figure 7.1 A macrophage extending toward a bacterium.

immediately after contact with an invader. Second, other receptors initiate signaling cascades that produce substances that initiate and maintain an inflammatory response at the site of the infection. Inflammation plays three roles: it (1) delivers additional cells (e.g., neutrophils) that can aid in killing, (2) produces a physical barrier that prevents the spread of infection to the bloodstream, and (3) aids in tissue repair. The most important of the substances that are produced are the pro-inflammatory cytokines, chemokines, and prostaglandins. These alter the local blood vessels and cooperate to lead a number of cell types to migrate to the site of infection, producing the redness, swelling, heat, and pain characteristic of local inflammation. Cytokines are small soluble proteins, first discovered to be released by immune cells, which diffuse away from the producing immune cell and serve as communication molecules between cells. The most important cytokines for producing local inflammation are interleukin-1 (interleukin stands for “between leukocytes”), interleukin-6, and tumor-necrosis factor alpha (IL-1, IL-6, TNF). Chemokines are chemoattractant proteins that stimulate the migration and activation of cells. There are many excellent textbooks you can consult to learn more about the immune system (e.g., Janeway, Travers, Walport, & Shlomchik, 2005). To this point, only local responses at the site of infection have been described. However, the very same cytokines that are critical for local innate immune reactions orchestrate a complex set of changes throughout the body that aid

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Role of the Brain in Host Defense

in protecting the host. This widespread set of changes is often called the acute-phase response (APR; Baumann & Gauldie, 1994). There are increases in the number of circulating leukocytes (leukocytosis), changes in plasma ions (e.g., iron), the liver shifts from producing the proteins that it normally makes (e.g., carrier proteins) to proteins that aid in fighting infection (e.g., haptoglobin), and so on. These changes are all adaptive. For example, plasma iron is reduced, and bacteria need iron in order to replicate. Again, these all occur within a few hours of infection.

ROLE OF THE BRAIN IN HOST DEFENSE The APR and Sickness Up to this point, none of the host responses to infection described have involved the brain. However, the fever that occurs during infection has long been considered to be part of the APR. Fever is an adaptive defensive mechanism because many microorganisms replicate poorly at elevated core body temperatures, and a number of enzymatic processes involved in the killing and removal of invading pathogens increase in activity at elevated temperatures (Kluger, Kozak, Conn, Leon, & Soszynski, 1996). The important point here, though, is that fever is mediated by the brain. Fever occurs because the set point of temperature-sensitive cells in the hypothalamus is raised, thereby leading to behaviors designed to drive up core body temperature (huddling to conserve heat, shivering to produce heat, etc.; Boulant, 2000). A number of other brain-mediated changes have come to be recognized as being part of the host response to infection. These include alterations in sleep patterns, reductions in activity, a loss of interest in social and sexual activities, increased sensitivity to pain, reduced food and water intake, and altered cognitive functions such as impaired hippocampal-dependent memory formation, among others (Dantzer, 2004). This pattern, which should be familiar to anyone who has had the flu, has been called “sickness.” In addition, there are several brain-mediated changes that are less obvious. The most important are activation of the hypothalamo-pituitary-adrenal (HPA) axis and the sympathetic nervous system (Berkenbosch, de Goeij, Rey, & Besedovsky, 1989). That is, there is a classic physiological “stress response” during sickness. Adaptiveness of Sickness It is important to understand that these sickness responses are not pathological or a reflection of weakness or debilitation produced by infection, but rather represent an adaptive evolved strategy to help combat the infection (Hart, 1988).

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For example, fever is quite energy intensive, requiring an extra 10% to 13% of metabolism for each degree rise in core body temperature. Thus, it would make sense for the sick organism to use its available energy stores to fight the infection. Reduced activity, reduced social behavior, and so on can be viewed as changes that reduce the energy used by behavior. Foraging for food would also use considerable energy and would expose the organism to an increased risk of predation during the illness, a time of decreased defensive abilities. Finally, the stress response that occurs can be understood in this context. The peripheral endpoints of the HPA axis and the sympathetic nervous system, cortisol and catecholamines, respectively, function to liberate energy from bodily stores, converting glycogen to glucose, and so forth. See Maier and Watkins (1998) for a more extended form of this argument. The manner in which interfering with hippocampaldependent memory formation could be adaptive is not obvious. It may not be adaptive per se. However, a plausible argument can be made. Some forms of memory depend critically on the hippocampus, and some do not. In particular, the hippocampus is involved in the formation of memories involving places or contexts, as opposed to discrete cues such as a tone or a light (Squire, 2004). Thus, for example, if a footshock is paired with a tone in some apparatus, fear will become conditioned to both the tone and the context of the apparatus so that fear responses will occur later to either the tone or the apparatus context. However, the memory of fear to the context requires the hippocampus, but memory of fear to the tone does not. It is thought that this is because the hippocampus is the part of the brain that forms representations of places and contexts (Rudy & Sutherland, 1995). Thus, animals and humans learn to be fearful of and, consequently, to avoid places where aversive events such as footshocks have occurred. This makes good adaptive sense. If a predator has been encountered or detected in a particular place, it is likely that the predator will be there again, and so the place is a good one to be wary about and perhaps avoid altogether. If an organism becomes ill or sick rather than being externally threatened, the symptoms will still appear while the individual is in some context or place, perhaps while it is foraging away from its home territory. However, it is likely that this place had nothing to do with producing the sickness or illness, particularly since the encounter with the microbe or poison will have been several hours before symptom manifestation. Thus, it would not be adaptive for the organism to become fearful of, or to avoid the place where sickness symptoms occurred. Indeed, organisms do not readily learn to avoid places where they became ill, but they readily learn to avoid tastes that precede illness (Garcia, Brett, & Rusiniak, 1989). This pattern

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would be accomplished if the hippocampus were taken “off line” during sickness. Motivational System Each of these aspects of sickness could be an independent brain-mediated response to microbial invasion of the body. However, it has been convincingly argued (Dantzer et al., 2005) that instead, they are the reflection of a common motivational change. That is, sickness is a motivational state (a central state that organizes the actions of the individual) that then drives these behaviors. That the innate immune response to infection produces brain-mediated changes that involve a shift in motivational state has a number of important implications. First, there must be mechanisms by which the innate immune system can signal the brain, informing the brain that viruses, bacteria, and so on, have entered the body. If there were not pathways by which the immune system could communicate to the brain, the brain would have no way to respond to peripheral infection. Second, discrete areas of the brain must change activity during sickness, otherwise the behavioral and other alterations would not occur. Regions of the brain almost never regulate only a single activity or process, and thus other behavioral and psychological activities not generally thought to be part of sickness may well be profoundly affected during sickness. A number of poorly understood phenomena may be understandable in this way (see the following discussion). Moreover, motivational states are often thought to compete with each other in a hierarchy. For example, fear competes with hunger—a person does not want to forage for food while a threat is present. Since fighting infection and promoting repair is a life and death issue, sickness should command the motivational stage and alter the expression of many other motivational systems. The next several sections examine these issues. Immune-to-Brain Communication Pathways As noted previously, the fact that a number of components of sickness are mediated by the brain implies that the immune system must have a way to “talk” to the brain. From this perspective, in addition to its other well-known functions, the immune system is a diffuse sense organ, scattered throughout the body, whose job it is to inform the nervous system about peripheral infection and injury (Blalock, 1984). This raises two key questions. First, what are the signals that arise from the immune system that serve to communicate to the CNS, and second, by what mechanism(s) do they communicate? The very same pro-inflammatory cytokines that participate in the orchestration of the peripheral inflammatory response also initiate immune communication to the CNS.

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This conclusion comes from findings indicating that (a) the peripheral administration of IL-1 in the absence of infection produces many of the brain-mediated sickness responses (Dinarello, 1991) and (b) peripheral blockade of pro-inflammatory cytokine receptors and the peripheral immunoneutralization and/or inhibition of synthesis of these cytokines, reduces or eliminates brain-mediated sickness responses as well as the activation of brain structures during infection (Bluthe, Dantzer, & Kelley, 1992). That is, the CNS does now not “know.” Of the cytokines, IL-1 appears to play the most prominent role, and the discussed manipulations are not always effective (Dunn & Swiergiel, 1998); other molecules may also be involved. This leaves the question of exactly how cytokines such as IL-1 signal the CNS. When considering this issue, a number of facts need to be kept in mind. First, mRNA for IL-1 and other cytokine receptors are widely distributed in the brain (Ericsson, Liu, Hart, & Sawchenko, 1995) and receptors are present on both neurons and glial cells. Second, activation of the innate immune response leads to the de novo synthesis of IL-1 within the brain. That is, cells within the brain, make (Buttini & Boddeke, 1995) and release (Ma, Chen, Oliver, Horvath, & Phelps, 2000) IL-1 in response to peripheral infection. Furthermore, simply injecting IL-1 peripherally leads cells within the brain to synthesize IL-1 (Hansen, Taishi, Chen, & Krueger, 1998). This brain IL-1 production is largely by microglial cells (Van Dam, Bauer, Tilders, & Berkenbosch, 1995; see discussion that follows). The nature of the signal(s) within the brain that leads microglia to become active in response to peripheral infection is still being explored, but adenosine triphosphate (ATP) and heat shock proteins (HSPs) are likely candidates (Mingam et al., 2007). Because IL-1 and other cytokine receptors are expressed on cells within the brain, the most obvious idea would be that cytokines accumulate in the bloodstream during infection, travel to the brain in the blood, then enter the brain parenchyma from the cerebral vasculature and then bind to their receptors. The difficulty is that cytokines are fairly large (IL-1 is roughly 15 kDa) and hydrophilic, and so would not be expected to be able to passively diffuse across the tight junctions involved in the cells of the cerebral blood vessels (the blood-brain barrier). However, a number of other mechanisms allow bloodborne cytokines to signal the brain: (a) Cytokines enter the brain from the blood at the circumventricular organs (Blatteis, 1990), such as the organum vasculosum lamina terminalis (OVLT), where the blood-brain barrier is absent or weak. Since cytokines cannot diffuse very far, they bind to receptors on astrocytes and other cells resident in the region, leading to the production of prostaglandins that can then travel to neural targets (Lin & Lin, 1996). Circulating

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pathogens themselves can bind to PAMP-recognizing receptors on macrophage-like cells residing in these structures (Lacroix, Feinstein, & Rivest, 1998). (b) Even though cytokines cannot passively diffuse across the blood-brain barrier, there are active transport mechanisms that carry cytokines across the barrier (Banks, 2005) where they can bind with receptors on cells near the blood vessels, such as perivascular macrophages. These macrophages then produce products, such as prostaglandins and nitric oxide, that can activate neurons. (c) Cytokines such as IL-1 bind to receptors on the luminal side of cerebral blood vessels, inducing the production of soluble mediators within the vessels, such as prostaglandins, which then diffuse into the brain parenchyma (Konsman, Vigues, Mackerlova, Bristow, & Blomqvist, 2004). Although the evidence for each of these blood-borne routes of communication is convincing, there are aspects of immune-to-brain signaling that these mechanisms cannot readily explain. First, the brain’s initial response to peripheral immune activation is rapid, with activation of brain structures. For example, Ericsson, Kovacs, and Sawchenko (1994) observed nuclear Fos protein (the gene c-fos is an immediate-early gene that is used as a neuronal activation marker) at 1 hour—the first time point tested. Since it takes considerable time for the Fos protein to be produced after the c-fos gene is activated, communication to the brain must have occurred within minutes. The bloodborne mechanisms almost certainly take longer than this to operate. Second, brain-mediated responses occur after the peripheral injection of quantities of immune activators that are too small to produce increases in blood levels of cytokines or other measured mediators. For example, the injection of very small quantities of lipopolysaccharide (LPS) that do not elevate peripheral circulating cytokines nevertheless produce fever (Hansen, O’Connor, Goehler, Watkins, & Maier, 2001). LPS is a constituent of the cell walls of gram-negative bacteria and is recognized by one of the PAMP-recognizing receptors (TLR-4). Third, it is difficult to understand how blood-borne cytokines can provide the brain with detailed information, such as the site of infection. All of these suggest that there ought to be neural as well as blood-borne mechanisms of immune-to-brain communication. Indeed, if the idea that the immune system functions as a sense organ is more than whimsical, then there ought to be communication from the immune system to the CNS over peripheral nerves—sense organs communicate to the CNS via nerves. The search for such a peripheral nerve can begin by inquiring as to whether there is a nerve that innervates sites in the body where immune responses happen (e.g., lymph nodes) and that sends afferent fibers to the brain. The vagus nerve is prominent in this regard

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because it innervates sites where pathogens enter the body (e.g., the lungs, the peritoneal cavity) and organs important for innate immunity (e.g., the liver). Indeed, peripheral immune activation, such as is produced by an intraperitoneal injection of LPS, activates afferent vagal nerves (Goehler, Gaykema, Hammack, Maier, & Watkins, 1998). Sensory structures associated with afferent vagal terminals (paraganglia) express IL-1 receptors, providing a mechanism by which peripheral cytokines can activate the vagus (Goehler et al., 1997). The vagus terminates in the nucleus tractus solitarius (NTS) in the brain stem, and during infection a neural cascade of activation spreads from the NTS to regions to which it projects (e.g., the parabrachial nucleus; Ericsson et al., 1994). Furthermore, electrical stimulation of afferent vagal fibers produces neural changes characteristic of sickness (Roosevelt, Smith, Clough, Jensen, & Browning, 2006). Supporting the importance of vagal signaling, severing the vagus often reduces or eliminates the brain activation (Wan, Wetmore, Sorensen, Greenberg, & Nance, 1994) and brain-mediated sickness responses (produced by injection of LPS, IL-1, and so on (Watkins et al., 1994). However, vagotomy does not always do so. For example, vagotomy blocks the fever produced by low, but not high, doses of LPS (Hansen et al., 2001). The most reasonable conclusion to date is that the vagus is an especially important signaling route early during infection, before high blood levels of cytokines have developed. Once blood levels are high, blood-borne routes are especially important. There also appears to be specificity of signaling to different regions of the brain, so that some behavioral endpoints lean more heavily on vagal signaling than do others. For example, Konsman, Luheshi, Bluthe, and Dantzer (2000) reported that severing the vagus blocks the reduction in social exploration, but not the fever produced by the very same dose of peripheral LPS. Finally, it should be noted that the vagus is not the only peripheral nerve that can signal the occurrence of immune activation. The vagus does not innervate regions such as the skin and oral cavity, and other nerves that innervate these regions may function to communicate immune activation to the CNS (Romeo, Tio, & Taylor, 2003). In sum, the immune system communicates to the CNS over multiple pathways. This multiplicity of communication routes likely functions to provide the CNS with detailed information concerning the immune status of the body, rather than simply that an infection is, or is not, present. Indeed, the pattern of brain activation differs after the injection of different immune activators. For example, Serrats and Sawchenko (2006) have reported that the peripheral injection of the bacterial superantigen staphylococcal enterotoxin B (SEB), which produces a peripheral cytokine pattern different from LPS, produces a different

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pattern of brain activation than does LPS. Thus, it may be that that the brain is able to construct an “image” of immune patterns in the periphery. The bidirectional pathways between the brain and immune system are depicted in Figure 7.2. Brain IL-1 Microglia As already noted, immune signals to the CNS lead cells in the brain to synthesize IL-1. That is, peripheral IL-1 “begets” central IL-1. This is a most unusual arrangement that has important implications. The IL-1 induction occurs largely in glial cells, with the initial response being most prominent in microglia (Van Dam et al., 1995). Because brain IL-1 is critical in immune-brain interactions (see the discussion that follows), microglia are key cells in these processes. This is entirely fitting because microglia

CRH

Pituitary

B NE, Enkephalins, SP, NPY Autonomic nervous system

β-Endorphin prolactin, GH other hormones Adrenal

ACTH

C NE Epi Enk

A CORT Spleen, thymus, and other immune organs

Brain IL-1 as a Mediator Immune cells (B & T cells, Mφ, etc.)

Proinflammatory cytokines (IL1, TNF, IL6)

Figure 7.2 Bidirectional pathways between the brain and the immune system. Note. A and B schematize the major outflow pathways from the brain to the immune system, the autonomic nervous and hypothalamo-pituitary-adrenal systems, respectively. C schematizes communication from the immune system to the brain.

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can be considered to be the brain’s resident immune cells, or at least “immune-like.” Microglia constitute 6% to 12% of all of the cells in the CNS and are of hematopoietic origin (Cuadros & Navascues, 2001), as are peripheral immune cells. In the adult, resident microglia arise from two sources. Resident microglia continue to divide throughout life, and peripheral blood monocytes migrate into the CNS and mature into microglia. Microglia express many or all of the same receptors as do peripheral macrophages, and thus can recognize bacteria, viruses, and so on. They are constantly monitoring the CNS environment and are responsible for immune surveillance within the CNS (Nimmerjahn, Kirchhoff, & Helmchen, 2005), and when they detect an invader, signs of cell damage via heat shock protein receptors, or other changes in the brain microenvironment, they change their morphology and function. When microglia are not stimulated, they have a highly ramified stellate morphology with little or no expression of activation markers such as CR3. They have often been described as appearing to be “damped” macrophages, held in check by the CNS microenvironment (Kreutzberg, 1996). When microglia detect danger, they change morphology, upregulate a variety of cell surface receptors, and synthesize and release a variety of pro-inflammatory mediators such as IL-1, prostaglandins, reactive oxygen species, and chemokines. That is, they initiate an inflammatory response in the CNS. Although it is common to think of microglia as being in either an inactivated or an activated state, microglia can actually be in a continuum of activational states. Of particular interest in the following discussion, microglia can be in a state that has been called “primed” (Perry, Newman, & Cunningham, 2003). In this state, they have a quiescent morphology and do not express upregulated activation markers. However, if stimulated the microglia overproduce inflammatory products such as IL-1. Microglia can remain in this primed or sensitized state for prolonged periods of time (Felton & Perry, 2005).

The fact that IL-1 is made in the CNS in response to peripheral immune activation is more than a curiosity. Importantly, the IL-1 that is synthesized in the brain is a key mediator of brain-mediated sickness responses. Many sickness responses can be prevented by injecting an IL-1 receptor antagonist (IL-1ra) into the brain (Maier, Watkins, & Nance, 2001). However, because IL-1 can enter the brain in small amounts, this finding could be explained by arguing that sickness responses are prevented by IL-1ra because it blocks the binding of this IL-1 that has entered from the outside. It is also the case that injecting IL-1 into the brain produces most of the brain-mediated sickness responses (Maier et al., 2001), but this also does not conclusively

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implicate brain-made IL-1. IL-1, like many peptides, is synthesized as a larger prohormone. Mature IL-1 is formed by cleavage of the prohormone by an enzyme called the IL-1 converting enzyme (ICE). Thus, inhibiting the actions of ICE would prevent new IL-1 from being manufactured. The injection of ICE inhibitors into the brain would prevent the brain from making new IL-1, but would have no effect on IL-1 that has entered from outside the brain. In the few experiments that have been conducted, intracerebral injection of ICE inhibitors has blocked or reduced brain-mediated sickness responses (Imeri, Bianchi, & Opp, 2006). More remarkably, IL-1 within the brain is also involved in the mediation of peripheral innate immune responses. For example, the blockade of IL-1 receptors in the brain reduces the gastrointestinal hypomotility produced by peripheral LPS (Plaza, Fioramonti, & Bueno, 1997). Conversely, elevating IL-1 in the brain can stimulate peripheral immune changes. Injecting IL-1 into the striatum leads the liver to manufacture acute phase proteins (Campbell et al., 2005). Since peripheral IL-6 is critical in regulating the liver APR, it would then be expected that brain IL-1 would lead to increases in blood levels of IL-6. Indeed, the intracerebral administration of IL-1 produces large increases in blood levels of IL-6, as well as IL-1. This increase in plasma IL-6 is not mediated by the injected IL-1 leaking to the periphery because a peripheral injection of the same small amount of IL-1 did not increase plasma IL-6. Furthermore, intracerebral administration of IL-1ra blocked the effects of intracerebral IL-1 (De Simoni et al., 1993), indicating that brain IL-1 receptors are the site at which the increase in blood IL-6 (as well as IL-1) is mediated. Thus, brain cytokines “beget” peripheral cytokines, as well as the other way around. Although the precise pathways are not well understood, it is clear that IL-1 in the brain initiates a communication process to the periphery

Sickness behaviors IL-1 Fever Liver IL-1

H I IL-1 P P

Hypo HPA activation IL-1 IL-1 IL-1 Paraganglia

LPS

NTS

s Vagu

Hyperalgesia

Macrophage

Figure 7.3 The complete brain-immune loop, with IL-1 in the brain regulating behavior and initiating an outflow from brain to the periphery.

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that signals the immune system. This arrangement is schematized in Figure 7.3. There is also an anti-inflammatory pathway from the brain to the immune system. The activation of efferent vagal fibers originating in the dorsal motor nucleus of the vagus can inhibit the release of peripheral pro-inflammatory cytokines via a cholinergic mechanism (Tracey, 2007). Finally, microglia can also release anti-inflammatory products (Rasley, Tranguch, Rati, & Marriott, 2006). Thus, there is truly bidirectional communication between the CNS and the innate immune system, involving both pro- and anti-inflammatory influences, with the balance between them doubtlessly critical. Downstream Mediators The fact that IL-1 and other cytokines within the brain are critical to the mediation of a variety of behavioral phenomena does not mean that IL-1 does so via a direct action on neuronal activity. Consider the memory impairments that are produced during peripheral infection. It is known that IL-1 induced in the hippocampus mediates the memory impairment because blocking IL-1 receptors in the hippocampus prevents the memory impairment from occurring (Pugh et al., 1999) and the microinjection of IL-1 into the hippocampus produces memory impairment (Barrientos et al., 2002). However, IL-1 may alter memory not because increases in IL-1 directly interfere with memory consolidation, but because IL-1 influences other molecules that are critical in memory formation. For example, the neurotrophin brain-derived neurotrophic factor (BDNF) within the hippocampus is critical in forming long-term hippocampal memories. Experiences such as contextual fear conditioning, that engage the hippocampus, induce the production of BDNF within specific layers of the hippocampus (Hall, Thomas, & Everitt, 2000). Moreover, this BDNF increase is necessary to form long-term hippocampally based memories (Alonso et al., 2002). This is noted because high levels of IL-1 in the hippocampus prevent the learning experience from increasing BDNF (Barrientos et al., 2004), and this may be the basis for the memory impairment, not the increased IL-1 per se. It is not known how IL-1 interferes with BDNF induction. However, the prostaglandins provide an intriguing possibility. The binding of IL-1 to its receptor activates the nuclear factor-kappa B (NF-kappaB) intracellular signaling pathway. NF-kappaB is a transcription factor that, among other things, increases the expression of the COX-2 enzyme. COX-2 induction, in turn, leads to the production of prostaglandins such as PGE2 (Hoozemans, Veerhuis, Janssen, Rozemuller, & Eikelenboom, 2001). There are four known subtypes of prostaglandin receptors (EP1–EP4), and one, the EP3 receptor, is primarily localized to neurons and is abundantly expressed in the hippocampus (Sugimoto & Narumiya, 2007).

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The EP3 receptor inhibits cAMP, and BDNF gene transcription is dependent on the cAMP-CREB pathway. Thus, PGE2 may decrease BDNF levels by binding to the EP3 receptor and reducing intracellular cAMP. The scenario would then be one in which IL-1 interferes with memory because it interferes with experience-dependent BDNF induction in the hippocampus, and IL-1 interferes with BDNF because it induces prostaglandins. Indeed, as this hypothesis would predict, infusion of PGE2 into the hippocampus after a learning experience interferes with both the induction of BDNF and the formation of memory, and the pharmacological blockade of prostaglandin production in the hippocampus prevents the memory impairment normally produced by intra-hippocampal injection of IL-1 (Hein et al., 2007). This discussion illustrates the complexity of the cascades involved. Indeed, the previous discussion is likely a gross oversimplification because IL-1 also activates mitogenactivated protein kinase pathways (MAPkinase), and these are also involved in the effects of IL-1 on plasticity (Kelly et al., 2003). There are doubtlessly many pathways and interactions initiated by IL-1 that mediate the behavioral changes that occur. It is likely that different behavioral endpoints (e.g., reduced food and water intake instead of memory formation) are mediated by different molecular cascades of IL-1-initiated events. However, it is interesting to note that the reduction in sexual behavior that is produced by immune activation is also blocked by prostaglandin inhibitors (Avitsur, Weidenfeld, & Yirmiya, 1999). Basal versus Elevated IL-1 The discussion to this point considered the impact of levels of IL-1 in the brain that are elevated above basal levels. However, basal IL-1 function may be necessary for the proper function of some of the very processes that are impaired by high levels. For example, memory is actually impaired if basal IL-1 function is removed, either by genetic deletion or by receptor blockade in the absence of manipulations that increase IL-1 above normal (Goshen & Yirmiya, 2005). Thus, the relationship between IL-1 and memory is really U shaped, and this may be the case for endpoints other than memory. Relationship between Peripheral and Brain IL-1 Before turning to a consideration of what all of this implies for phenomena of interest to behavioral scientists, it helps rationalize what is to follow to speculate on why there is this peculiar relationship between peripheral and brain IL-1. Elements of the innate immune system are phylogenetically quite old, whereas the adaptive immune system is a more recent evolutionary development. Even organisms as primitive as sponges (the most primitive multicellular

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organisms) can distinguish self from nonself, and contain phagocytic cells that can defend against invading microorganisms. Cells called amoebocytes in these invertebrates accomplish phagocytosis and PAMP recognition. These cells migrate to sites of infection in organisms such as mollusks and contain enzymes that are very similar to those in vertebrate macrophages. Importantly for the present discussion, amoebocytes synthesize and release cytokines such as IL-1 (Beck et al., 1993), and in vertebrates, they orchestrate innate immune defense (Clatworthy, 1998). In these organisms, the amoebocyte-produced cytokines communicate with and regulate neural processes, altering neural excitability (Clatworthy & Grose, 1999). This cytokine-to-neural tissue communication is involved not only in defense against pathogens, but also in defense against predators or external threats. For example, organisms such as mollusks identify predators or external threats via contact with their body surface. This body contact elicits withdrawal reflexes and locomotion, moving the body surface away from the threat. The cytokines released by amoebocytes can sensitize these withdrawal reflexes by increasing the excitability of the neurons involved (Clatworthy, 1998). The amoebocytes, in turn, receive signals from the neural tissue, providing bidirectional immune-neural communication even in these primitive organisms. Cells within the mollusk neural structures express cytokine-like molecules, and so cytokines might communicate in both directions. It is important to understand that organisms such as mollusks have a series of separate ganglia rather than a discrete brain and there is no blood-brain or blood-ganglia barrier. That is, immune-derived cytokines can communicate directly to neural tissue in the service of host defense in these organisms. With the development of a blood-brain barrier in vertebrates, IL-1 released by immune cells could no longer communicate directly with neural tissue. This may help to explain the very peculiar arrangement in vertebrates in which peripheral IL-1 induces the production of the very same molecule in the brain. Under this arrangement, when immune cells release IL-1, IL-1 still makes contact with neural tissue, but it is IL-1 that has been induced within the brain. One final point with regard to the role of cytokines in organisms such as mollusks: As previously noted, host defense requires the production of energy. In mollusks, the amoebocyte and cytokines are critical to this process. In mammals, the hypothalamus is key, with the hypothalamus being activated during infection by IL-1 (Schiltz & Sawchenko, 2007). The hypothalamus (a) releases corticotropin-releasing hormone (CRH) into the portal blood. The CRH travels to the pituitary gland where it stimulates the release of glucocorticoids into the bloodstream, the glucocorticoids then leading to energy production, and

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(b) initiates sympathetic activation that releases catecholamines into the blood, then leading to energy production. Mollusks do not have hypothalami, pituitaries, adrenals, or a sympathetic nervous system. However, all of these molecules are contained within the amoebocyte, and the amoebocyte releases glucocorticoids and catecholamines under the control of IL-1 (Ottaviani & Franchini, 1995).

IMPLICATIONS OF IMMUNE-BRAIN RELATIONSHIPS FOR BEHAVIOR In considering the implications of immune-brain relationships for behavior, we begin by discussing behavioral changes that occur during infection. We then move to a discussion of whether there are any implications of what will have been described for circumstances other than infection. Infection-Induced Changes As noted, microbial stimulation of innate immune cells initiates a cascade that communicates the presence of infection to the brain, with the brain then orchestrating a coordinated set of sickness responses that include increased sleep (particularly NREM sleep), social withdrawal, decreased food and water intake, impaired cognitive function, and so on. It is interesting that in other research domains many of these behaviors would be viewed as indicative of either depressed mood or anxiety. For example, the tendency to engage in social interaction with a conspecific is a well-validated “animal model of anxiety” (File & Seth, 2003). Manipulations that decrease anxiety in humans (e.g., anxiolytic drugs such as benzodiazepines) increase interaction, and manipulations that increase anxiety (e.g., anxiogenic drugs such as beta-carbolines) decrease interaction. Peripheral immune activation, as mentioned, decreases social interaction (Yirmiya, 1996). For these reasons, a number of investigators have explored whether peripheral immune activation might not also produce other behavioral changes that are used to implicate the presence of anxiety or depressed mood. Anxiety is often defined as fear in the absence of clearly threatening stimuli. Thus, assessments of anxiety typically involve the presentation of ambiguous or potentially threatening situations that are not overtly dangerous, with the behavioral measure being the animal’s tendency to avoid the potentially threatening circumstance. For example, in the elevated plus maze (EPM) there are four arms at right angles from a central area, with two being enclosed and two open (no sides or top). The entire maze is raised above the floor, so there is a potential drop from the open arms. Being in the open puts

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animals at greater risk, but animals also have a tendency to explore, so a typical subject (rat or mouse) spends considerable time in both open and closed arms when first placed in the apparatus. Manipulations that increase anxiety decrease the time that subjects spend in the open arms, and manipulations that decrease anxiety do the reverse (Carobrez & Bertoglio, 2005). The peripheral injection of either immune activators such as LPS, or the administration of IL-1 itself, decrease time spent in the open arms (Swiergiel & Dunn, 2007). As for depressed mood, a cardinal symptom of depression is adhedonia, a loss of interest in normal pleasures. Yirmiya and his colleagues have conducted an extensive series of studies exploring whether peripheral immune stimulation leads to behavioral changes that indicate a loss of pleasure. These involve an examination of sexual behavior and preference for sweet tastes. The peripheral administration of LPS and/or IL-1 reduces sexual behavior in females (Avitsur & Yirmiya, 1999) and the rat’s normal preference for sweet solutions (Yirmiya, 1996). Immune activation reduces motor activity and fluid intake in general. Simple reduction in motor activity doesn’t explain the reduced sexual behavior because the immune activation alters quite specific aspects of sexual behavior. A reduction in drinking cannot explain the sweet solution data because in these experiments the rats have two drinking tubes, one containing water and one the sweet solution. Immune stimulation causes a reduction in the percentage of total fluid intake that is the sweet solution. It can also be noted that these effects of immune activation can be prevented by chronic treatment with antidepressant drugs such as the SSRIs (Yirmiya et al., 2001). Nevertheless, the difficulty of disentangling effects that reflect anxiety or depressed mood from general reductions in motor activity and reduced food and water intake has been noted (Swiergiel & Dunn, 2007) and caution should be exercised. However, it has been demonstrated that lowlevel infection with the bacterium Campylobacter jejuni in mice, that does not produce overt signs of sickness, nevertheless leads to anxious behavior (Goehler, Lyte, & Gaykema, 2007). Research in this area with humans has the great advantage that anxiety and depressed mood can be assessed in ways that are not confounded with the behavioral changes, such as reduced motor activity characteristic of sickness. Very low doses of LPS, so low that humans cannot even discriminate whether they have been administered LPS or the vehicle, produce self-reports of increased anxiety (Krabbe et al., 2005). The most extensive studies here have been conducted in the context of interferon-alpha (IFN␣) administration. IFN␣ has antiviral and antitumor properties and is used in the treatment of hepatitis C infection and cancer.

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IFN␣ is itself a cytokine with properties that overlap those of the classic pro-inflammatory cytokines, and in addition, IFN␣ induces the production of IL-1 (Taylor & Grossberg, 1998). Chronic IFN␣ administration in these patients produces depressed mood that is so severe that roughly 50% of the patients come to meet the diagnostic criteria for major depression (Musselman et al., 2001). Importantly, prior administration of antidepressant drugs can prevent the depressogenic effect of the IFN␣ treatment (Raison et al., 2007). Anxiety and depressed mood are not mediated in the periphery, but in the brain. This means that immune activation produces these phenomena because it leads to neural changes. Because peripheral cytokines induce the production of cytokines within the brain, it is natural to inquire whether it is these cytokines induced in the brain that produce anxious and depressed behavior. IL-1 in the brain leads to alterations in serotonergic, noradrenergic, and dopaminergic function (Dunn, Wang, & Ando, 1999), thereby providing a tie to neurotransmitter systems traditionally thought to be involved in these processes. The research to date directed at determining the role of cytokines within the brain on anxiety- and depression-related behaviors produced by infection reveals a complex picture. For example, the reduction in social interaction produced by peripheral immune activation is prevented by blockade of IL-1 receptors in the brain, but the reduction in food and water intake is only partially blunted (Kent et al., 1992). The effects of brain IL-1 receptor blockade may also depend on the particular immune stimulus (Bluthe et al., 1992). This issue is the topic of current investigation and will be further discussed in the context of stress, rather than infection induced behavioral change. Findings that peripheral immune activation in animals and humans lead to depressed mood and anxiety lead naturally to an inquiry into whether peripheral immune activation and it’s consequent induction of brain cytokines might not be involved in mood and anxiety disorders. This issue is beyond the scope of this chapter and has been the subject of numerous reviews (e.g., Anisman, Merali, Poulter, & Hayley, 2005). Microglial Priming The research reviewed previously indicates that peripheral inflammatory events initiate a process that ultimately alters many aspects of behavior, mood, and cognition, and that the induction of cytokines within the brain is critical to the neural cascade that mediates these changes. Any organismic or other variable that would magnify this process would amplify the impact of infection or injury on these behavioral, emotional, and cognitive processes. It may

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well be that exaggerated immune-CNS interactions are involved in the mediation of a number of poorly understood phenomena of societal importance. It will be recalled that cytokines within the CNS are manufactured primarily by glial cells, with microglia being more reactive than astrocytes. As described previously, microglia can be in a continuum of activation states, passing from inactivated, to primed or sensitized, to fully activated. In the fully activated state, microglia synthesize and secrete high levels of pro-inflammatory molecules such as IL-1 on a continuous basis, whereas in the primed state they do not do so, but will produce exaggerated levels if they receive stimulating input. Aging Many of the changes that occur with aging are poorly understood. There has been considerable interest in the role of brain cytokines in the cognitive declines that occur with aging, largely because elevated levels of cytokines are associated with neurodegenerative disorders such as Alzheimer ’s disease (Cacquevel, Lebeurrier, Cheenne, & Vivien, 2004). However, with improvements in health care, many individuals are undergoing “normal healthy aging.” By the year 2030, roughly 20% of the population will be over 65 years of age. As life expectancy continues to increase, it is important to understand the factors underlying the decline in memory and cognition that occurs with normal aging, in addition to the processes associated with the more devastating pathological neurodegenerative disorders. Although there is disagreement about the extent to which memory and other cognitive functions decline during normal aging, there is agreement that the variability in individual performance increases with age, with some individuals suffering large declines (Laursen, 1997). In addition, vulnerability to cognitive declines associated with a variety of challenges, such as surgery and heart attacks, increases as people age. It is commonplace for an aging individual to be functioning at a high level, but then to display poor cognitive function after surgery, for example, hip replacement, that is seemingly unrelated to cognition or the brain. Indeed, the term postoperative cognitive dysfunction (POCD) has been used to identify this phenomenon (Bekker & Weeks, 2003). These precipitous declines in mental function have been difficult to understand. However, consider the possibility that glial cells are primed by normal healthy aging. Events such as surgery are inflammatory and produce high levels of circulating cytokines such as IL-1 (Carter & Whelan, 2001). The research reviewed previously indicates that these peripheral cytokines will communicate to the brain, leading to a neural cascade that includes the activation of glial cells. But if the glial cells are primed by aging,

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then the result should be exaggerated production of proinflammatory cytokines such as IL-1. This exaggerated production of brain IL-1 after peripheral inflammatory events such as surgery could then be triggering the memory and other cognitive impairments that follow. So, do glial cells change with age? With normal aging, there are indeed characteristic changes in microglial and astrocytic morphology, expression of activation makers, and function. For example, aging increases the microglial expression of complement type 3 receptor (CD11b), major histocompatibility complex (MHC) Class II, leukocyte common antigen (LCA; CD45), and CD4 (Perry et al., 2003). Functional changes such as increased phagocytic activity have also been reported (Sheng, Mrak, & Griffin, 1998). In general terms, this pattern can be described as a loss of the normal suppression that is produced by the CNS microenvironment. There are numerous possible sources for this loss of suppression, such as neuroendocrine dysregulation, cumulative oxidative stress, and mitochondrial changes. Neurons inhibit glial function via cell-to-cell contact (Neumann, 2001), and neuronal loss or reduced function may, itself, increase glial activation. The contact suppression of microglial activation by neurons is accomplished by a number of proteins that are expressed on the surface of neurons that bind to specific receptors for those proteins on microglia. For example, neurons express a glycoprotein identified as CD200 ligand, and it binds to a receptor, CD200R, that, within the brain, is expressed only by microglia (Hoek et al., 2000). Frank et al. (2006) reported that the expression of CD200 by neurons decreases in aging animals, providing a mechanism by which microglia are released from their normal inhibitory restraints. Whether CD200 is reduced because of neuronal loss or function is not known. It should be noted, however, that there are likely a number of mechanisms involved in age-related glial changes. For example, IL-10, an anti-inflammatory cytokine, decreases with age (Ye & Johnson, 2001), and IL-10 functions to inhibit glia. The important point is that glia do take on an activated morphology with age. This leaves the issue of whether the glial cells are fully activated or primed in animals that are aging, but not senescent. The results from studies that have examined pro-inflammatory cytokine expression with aging are somewhat inconsistent, but the available data suggest that PIC expression is at most elevated to only a minor degree in aged humans (Sheng et al., 1998) or rodents (Kyrkanides, O’Banion, Whiteley, Daeschner, & Olschowka, 2001). IL6 may be an exception because about a 20% increase in basal protein expression has been reported in older animals (Ye & Johnson, 2001). However, even 20% is a small fraction of the increases in brain IL-6 that occur after peripheral immune stimulation.

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The morphological changes in glia with age described here do not, in themselves, indicate that there would be an exaggerated pro-inflammatory response to peripheral immune stimulation. To examine this issue, Barrientos et al. (2006) injected both aging and young rats with live, replicating E. coli bacteria. This bacteria causes only a mild infection, and E. coli is cleared in both old and young rats in less than 24 hours. Importantly, E. coli did not produce a larger peripheral increase in pro-inflammatory cytokines in aging than in young rats, so any differences in brain responses could not be attributed to an augmented peripheral response. Protein levels of IL-1 within the hippocampus increased rapidly after infection in both young and old animals, but in the young rats levels returned to baseline between 4 and 24 hours after infection. In aging rats, IL-1 levels were elevated for 10, but not 14 days. This is an enormous difference in the duration of hippocampal IL-1 increase produced by peripheral infection. It is always possible that the IL-1 measured by Barrientos et al. (2006) did not derive from microglia. To further explore this issue, Frank et al. (2006) isolated microglia from the hippocampi of aging and young rats and studied them in vitro. The microglia from aging rats exhibited features of activation (e.g., increased expression of MHCII mRNA), but did not secrete more IL-1 than did microglia from young rats. However, when LPS was added to the culture to stimulate the microglia, the microglia from the aging animals produced much more IL-1 than did the microglia from the young subjects. Because there were no other cells in the culture, the microglia had to be the source of the IL-1, and because there were the same number of cells in culture for both groups, it had to be that individual microglia from aging rats secreted more IL-1 when stimulated. The foregoing makes clear that microglia can be primed or sensitized by aging. If the sensitized IL-1 response that results from primed glia produces cognitive impairments such as memory, then memory should be impaired in aging animals following infection or other inflammatory events for roughly the same duration as the IL-1 increase. To test this idea, aging and young rats of exactly the same ages and strain as used in the previous experiments were given either a peripheral infection with E. coli or vehicle injection. Learning tasks were conducted from 4 to 14 days later, with memory being tested 48 hours after learning. In young animals, exposure to E. coli had no effect at any of the E.coli-to-learning intervals. This was expected because the mild E. coli infection only increases hippocampal IL-1 for between 4 and 24 hours in young rats. In the aging animals, hippocampal memory formation was very poor when learning was 4 to 10 days after E. coli exposure. However, if 14 days intervened, memory was now normal. Thus, the

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time course of interference with the formation of longterm memory mirrored the time course of hippocampal IL-1 elevation. Importantly, memory for fear of the tone from the fear-conditioning task was not impaired, suggesting selectivity to the hippocampus. To determine whether these effects in the aging animals represented impairments of long-term memory formation, as opposed to deficits in learning or the processing of the information during the fearconditioning task, short-term memory 1 hour after training was also assessed. Short-term memory was unaffected by prior E. coli exposure in the aging subjects. That is, fear of the context was perfectly intact 1 hour after conditioning, supporting the idea that it is the consolidation of longterm memories, a process that requires the hippocampus, which is impaired. Finally, inhibition of prostaglandin synthesis within the hippocampus has been reported to block the effects of E. coli exposure on memory in aging animals (Hein et al., 2007), supporting the conclusion that the same cascade as described earlier is involved in the effects of peripheral immune stimulation on memory in aging. The effects of infection on memory and hippocampal IL-1 levels in aging animals persisted for only 10 days in the E. coli experiments. However, E. coli exposure in rats produces a very mild infection, fever persisting in the old animals for only 2 days. It may well be that a more powerful inflammatory stimulus would produce a much more prolonged increase in brain IL-1 in aging animals. There is also some evidence that inflammatory challenges may cumulate in aging animals—a “multiple hit” hypothesis. For example, surgery occurring 2 weeks after infection produces memory impairments for several months (Barrientos et al., unpublished data). Before leaving the discussion of aging it should be noted that there is no reason to expect that the impact of glial priming should be restricted to the effects of infection and inflammation on cognitive processes. Indeed, Huang, Henry, Dantzer, Johnson, and Godbout (2007) have reported that a central administration of LPS produces exaggerated sickness behavior in old animals. The only cells in the brain that express the receptor for LPS (TLR-4 receptor) are microglial cells and perivascular macrophages, supporting the priming hypothesis. It should also be noted that any factors that activate glia other than peripheral inflammation, should also have a potentiated effect in aged individuals. As discussed next, stressors may activate microglia, and brain IL-1 may mediate some of the effects of exposure to stressors. Thus, the experience of stressors would be expected to have an exaggerated impact on the aging individual. Early Infection Aging may be but one of many factors that prime glial cells. Maternal infection during the third trimester in

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humans has been associated with a number of neuropsychological difficulties in adulthood. Brain development in the rat and human are quite different, and the rat is born at a much more immature stage. At roughly postnatal day 4, the rat is considered to be equivalent in development to the third trimester in humans. Bilbo, Biedenkapp, et al. (2005) infected rats with E. coli on postnatal day 4 and examined their behavior in adulthood. The rats appeared normal until challenged with a peripheral injection of LPS under conditions that had no effect on control subjects. Now, the early-infected rats showed hippocampal-dependent memory impairments identical to those that occur in aging rats. An examination of both glial morphology and IL-1 in the brain indicated that the early infection did prime glia into adulthood, and inhibition of IL-1 synthesis by an ICE inhibitor in the adult subjects blocked the deleterious effects of peripheral LPS on memory (Bilbo, Levkoff, et al., 2005). Clearly, the existence of glial priming would predict that a broad range of behaviors would be altered by early infection. Whether this is so is under investigation. Stress It is not obvious how stress would be related to the processes so far discussed in this chapter. Several factors led a number of investigators to explore whether cytokines within the brain might be involved in mediating some of the consequences of exposure to stressors. First, some of the behavioral consequences of stressor exposure appear to be quite similar to those of infection (Maier & Watkins, 1998). Second, both infection and stressors activate the HPA axis and sympathetic nervous systems. This can be rationalized by considering that stressors or external threats evoke fight/flight, and fight/flight requires energy production, as does host defense against infection. As discussed previously, even the most primitive organisms engage in host defense against infection, and this involves the production of energy. Fight/flight evolved later, as it requires an organism complex enough to detect predators or threats at a distance, direct motor responses in complex ways, and integrate the two. Since evolution often works by cooptation of existing solutions to solve related problems (Gould, 1982), it may be that as the fight/flight response evolved it used the mechanism that was already present to produce energy—the sickness machinery. These considerations led a number of investigators to determine whether (a) stressors induce the production of cytokines in the brain as does infection, and (b) blockade of IL-1 or other cytokines in the brain would block the behavioral or endocrine effects of exposure to stressors. The literature is not extensive, but a variety of stressors do

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lead to IL-1 increases in specific regions of the brain (O’Connor et al., 2003). This suggests that stressors activate glial cells, and Nair and Bonneau (2006) found that restraint increased microglial proliferation. The literature on the blockade of IL-1 in the brain is even less extensive, but intracerebral injection of IL1-ra has been reported to reduce both the endocrine (Shintani et al., 1995) and behavioral (Maier & Watkins, 1995) effects of exposure to stressors. Stressors only induce IL-1 increases for a brief period of time (Nguyen et al., 2000). However, stressors might be able to prime microglia for a longer period of time. Microglia isolated from the hippocampi of rats stressed 24 hours earlier do not produce more IL-1 than do microglia from nonstressed controls, but produce exaggerated levels of IL-1 when LPS is added as a stimulus (Frank, Baratta, Sprunger, Watkins, & Maier, 2007). That is, stress produces the same pattern of microglial changes as does aging. The duration of this glial priming is not known. Cross-Sensitization The existence of stress-induced glial activation and priming has a number of implications (see Figure 7.4). First, if both stressors and peripheral inflammatory events activate and prime microglia, then there should be crosssensitization between stress and infection. That is, individuals that have experienced a stressor in the recent past should over-respond to infectious agents, and conversely, with the duration of such effects depending on the persistence of glial priming. Indeed, exposure to a stressor (inescapable tail shock in rats) has been shown to potentiate a variety of sickness-related responses to peripheral LPS (Johnson, O’Connor, Hansen, Watkins, & Maier, 2003), with the sensitization persisting for 4 to 10 days after only a single stressor session. For example, the HPA response to LPS is potentiated for 4, but not 10 days following tail shock (Johnson, O’Connor, Deak, Spencer, et al., 2002). Conversely, immune activation also senitizes responses to stresssors. For example, a single peripheral administration of IL-1 sensitizes the HPA response

Signal ATP, HSPs, etc.

Cytokines Cytokines

Behavior, mood, and cognition

Aging, early infection, stress

Figure 7.4 A variety of conditions can prime the glial response to normal input, thereby producing exaggerated cytokine responses and the products of brain cytokines.

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to novelty 22, but not 42 days later (Schmidt, Aguilera, Binnekade, & Tilders, 2003). The existence of cross-sensitization between stressors and immune stimuli does not indicate that glial priming and/or brain cytokines are key mediators of the process. There has been only a small amount of research directed at this issue. However, three findings do support this possibility. First, exposure to tail shock exaggerates the increase in brain IL-1 that is produced later by LPS (Johnson, O’Connor, Deak, Stark, et al., 2002). Second, the intracerebral administration of the IL-1 receptor antagonist blocks the sensitizing effect of tail shock to later LPS (Johnson, O’Connor, Watkins, & Maier, 2004), and third, the intracerebral injection of IL-1 produces sensitization to subsequent LPS (Johnson et al., 2004). Thus, IL-1 in the brain is both necessary and sufficient to produce cross-sensitzation, but the role of glia has not been explored. Stress and Peripheral Responses The idea that stressors and stimuli to the immune system ultimately act on overlapping neural circuitry involving IL-1 has another implication. This implication is that stressors should produce some of the same peripheral changes as occur during infection because the induction of IL-1 in the brain leads to signals to the periphery that impact on peripheral immune function (see previous discussion). Indeed, stressors as mild as exposure to a novel environment increase plasma IL-6 (LeMay, Vander, & Kluger, 1990), and more potent stressors lead to increases in plasma IL-1 as well (Johnson et al., 2003). As previously noted, peripheral IL-6 is a primary mediator of the acute phase response during infection. Thus, it should be the case that stressors, which induce IL-1 in the brain, should produce a peripheral acute phase response similar to that produced by infection. Not much research has been directed at this question, but it can be noted that exposure to a single session of tail shock produces various aspects of the APR including fever and shifts in liver function characteristic of infection—a reduction in the production of carrier proteins and an increase in production of acute phase proteins (haptoglobin and alpha 1-acid glycoprotein; Deak et al., 1997). This stressor-induced peripheral APR might have adaptive consequences. The period after a fight /flight emergency is likely a high probability period for infection from an injury, and exposure to tail shock has been shown to lead to enhanced recovery from a subcutaneous bacterial infection, as might occur during an injury (Deak, Nguyen, Fleshner, Watkins, & Maier, 1999). Thus, a number of hard-to-understand consequences of exposure to stressors may be understandable from the present perspective.

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SUMMARY In this chapter, we described the existence of a bidirectional immune-brain network with extensive communication pathways going in each direction. We focused on immuneto-brain communication because immune modulation of the brain has has the most important implications for behavior. We summarized a broad array of research indicating that processes within the immune system, via communication to the brain and consequent modulation of neural activity, potently influence and direct aspects of behavior, mood, and cognition. We concentrated on acute rather than chronic immune activation and related phenomena such as stress in order to highlight the adaptive nature of the processes involved, and it is likely that it is with regard to acute events that these processes are adaptive or beneficial. During evolution, organisms that experienced chronic infection or chronic stress would seem to have been less likely to reproduce or survive. The mechanisms that we described may not be beneficial when infection or stress becomes chronic. It is here that physiology may shade into pathology, with outcomes such as neurodegeneration and clinical depression. The existing experimental research has tended to employ immune activators that are quite potent, but it should be recognized that nonhuman and human animals frequently encounter molecules that are recognized as nonself by the immune system, but that are not infectious and of which the organism is unaware. These molecules might also initiate immune-to-brain signaling, and the types of behavioral changes described here. For example, Besedovsky, Sorkin, Keller, and Muller (1975) exposed subjects to sheep red blood cells (SRBC). SRBCs are not bacteria, viruses, or the like, but they are foreign proteins and thus will activate immune responses. At the peak of the immune response to the SRBCs, the subjects exhibited increased HPA responding, just as if there were a stressor present. Thus, the SRBCs initiated signaling to the brain, and the brain altered its pattern of activity. There is much unexplained variability across time in an individual’s behavior, mood, and cognition, and immune-to-brain signaling initiated by encounters with foreign proteins that are not overtly infectious could be an important source of such variation. Moreover, consider the implications of the crosssensitization phenomenon described combined with immune-to-brain signaling initiated simply by substances that are foreign. Individuals who have experienced stress in the recent past might now undergo a large change in behavior, mood, or cognition after exposure to a benign but foreign protein. Conversely, individuals who have recently been exposed to a substance of which they are unaware but is foreign might show exaggerated reactions to a stressful

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Hoek, R. M., Ruuls, S. R., Murphy, C. A., Wright, G. J., Goddard, R., Zurawski, S. M., et al. (2000). Down-regulation of the macrophage lineage through interaction with OX2 (CD200). Science, 290, 1768–1771. Hoozemans, J. J., Veerhuis, R., Janssen, I., Rozemuller, A. J., & Eikelenboom, P. (2001). Interleukin-1beta induced cyclooxygenase 2 expression and prostaglandin E2 secretion by human neuroblastoma cells: Implications for Alzheimer ’s disease. Experimental Gerontology, 36, 559–570. Huang, Y., Henry, C. J., Dantzer, R., Johnson, R. W., & Godbout, J. P. (2007). Exaggerated sickness behavior and brain proinflammatory cytokine expression in aged mice in response to intracerebroventricular lipopolysaccharide. Neurobiology of Aging, 29, 1744–1753. Imeri, L., Bianchi, S., & Opp, M. R. (2006). Inhibition of caspase-1 in rat brain reduces spontaneous nonrapid eye movement sleep and nonrapid eye movement sleep enhancement induced by lipopolysaccharide. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 291, R197–R204. Janeway, C. A., Travers, P., Walport, M., & Shlomchik, M. (2005). Immunobiology: The immune system in health and disease (6th ed.). London: Garland Science. Johnson, J. D., O’Connor, K. A., Deak, T., Spencer, R. L., Watkins, L. R., & Maier, S. F. (2002). Prior stressor exposure primes the HPA axis. Psych oneuroendocrinology, 27(3), 353–365. Johnson, J. D., O’Connor, K. A., Deak, T., Stark, M., Watkins, L. R., & Maier, S. F. (2002). Prior stressor exposure sensitizes LPS-induced cytokine production. Brain Behavioral Immunity, 16, 461–476. Johnson, J. D., O’Connor, K. A., Hansen, M. K., Watkins, L. R., & Maier, S. F. (2003). Effects of prior stress on LPS-induced cytokine and sickness responses. American Journal of Physiology: Regulatory, Integrative and Comparative Physiology, 284, R422–R432. Johnson, J. D., O’Connor, K. A., Watkins, L. R., & Maier, S. F. (2004). The role of IL-1beta in stress-induced sensitization of proinflammatory cytokine and corticosterone responses. Neuroscience, 127, 569–577. Kelly, A., Vereker, E., Nolan, Y., Brady, M., Barry, C., Loscher, C. E., et al. (2003). Activation of p38 plays a pivotal role in the inhibitory effect of lipopolysaccharide and interleukin-1 beta on long term potentiation in rat dentate gyrus. Journal of Biological Chemistry, 278, 19453–19462. Kent, S., Bluthe, R. M., Dantzer, R., Hardwick, A. J., Kelley, K. W., Rothwell, N. J., et al. (1992). Different receptor mechanisms mediate the pyrogenic and behavioral effects of interleukin 1. Proceedings of the National Academy of Sciences, USA, 89, 9117–9120. Kluger, M. J., Kozak, W., Conn, C. A., Leon, L. R., & Soszynski, D. (1996). The adaptive value of fever. Infectious Disease Clinic of North America, 10, 1–20. Konsman, J. P., Luheshi, G. N., Bluthe, R. M., & Dantzer, R. (2000). The vagus nerve mediates behavioural depression, but not fever, in response to peripheral immune signals; a functional anatomical analysis. European Journal of Neuroscience, 12, 4434–4446. Konsman, J. P., Vigues, S., Mackerlova, L., Bristow, A., & Blomqvist, A. (2004). Rat brain vascular distribution of interleukin-1 type-1 receptor immunoreactivity: Relationship to patterns of inducible cyclooxygenase expression by peripheral inflammatory stimuli. Journal of Comparative Neurology, 472, 113–129. Krabbe, K. S., Reichenberg, A., Yirmiya, R., Smed, A., Pedersen, B. K., & Bruunsgaard, H. (2005). Low-dose endotoxemia and human neuropsychological functions. Brain Behavioral Immunity, 19, 453–460. Kreutzberg, G. W. (1996). Microglia: A sensor for pathological events in the CNS. Trends in Neuroscience, 19, 312–318. Kyrkanides, S., O’Banion, M. K., Whiteley, P. E., Daeschner, J. C., & Olschowka, J. A. (2001). Enhanced glial activation and expression of specific CNS inflammation-related molecules in aged versus young rats following cortical stab injury. Journal of Neuroimmunology, 119, 269–277. Lacroix, S., Feinstein, D., & Rivest, S. (1998). The bacterial endotoxin lipopolysaccharide has the ability to target the brain in upregulating its

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Chapter 8

Neuroanatomy/Neuropsychology BRYAN E. KOLB AND IAN Q. WHISHAW

recognized that the body is complex and operates much like a machine, but he proposed that the mind was separate and was nonmaterial and nonspatial. Descartes argued that the mind and body are separate but interact to produce thought and behavior, a position referred to as dualism. The problem with the dualistic view is that for the mind to affect the body requires that energy be created, a proposition that violates fundamental laws of physics. Nonetheless, even today many people have a dualistic view of the mind and body and appear to believe that there is more to behavior than the brain. One example is the belief in a nonmaterial soul that influences behavior during life and exits after the body dies.

A challenge for science over the past 150 years has been to identify a general conceptual framework for how the human brain is organized to produce the amazing complexity of human behaviors ranging from movement to emotion to language. The human brain is composed of more than 180 billion cells, more than 80 billion of which are directly engaged in information processing. Given that each nerve cell receives up to 15,000 connections from other nerve cells, there is a challenge in understanding such complexity. The challenge is made simpler by understanding the brain’s underlying organization. Our understanding of brain organization starts with an examination of the historical development of current thinking. We then review how the brain is organized and identify rules that govern its operation. Finally, we consider how complex psychological functions emerge from the anatomical complexity.

The emergence of evolutionary theory in the mid-nineteenth century provided a different perspective: Rational behavior can be fully explained by the activity of the nervous system without the need for a nonmaterial mind. Darwin emphasized the important idea of “descent with modification” in which all living animals are descended from a common ancestor. He thus identified the principle that the workings of the human brain reflect a long history of adaptation of an early primitive brain. One implication of this view is that the human brain is not special but rather represents an elaboration of the brains of our nearest relatives such as the great apes and, likewise, the ape brains are elaborations of more primitive mammalian brains. The recognition that mammalian brains are fundamentally similar in general organization was an important step because it meant that the organization of the human brain could be studied using relatively simpler surrogate brains such as those of monkeys, carnivores, and rodents.

HISTORICAL IDEAS OF BRAIN ORGANIZATION People knew what the brain looked like long before they had any idea what it might do. Early humans must have noticed that all animals had a brain and that it was connected to other parts of the body by what we now know to be nerves. Understanding what the brain does requires the important philosophical leap, however, of first recognizing that the production of thought and behavior is based on biology rather than on some sort of “will” or energy force. Recognizing that behavior is related to biological activity is only the first step because there also needs to be a recognition that the nervous system, and not other organs such as the heart or liver, produces behavior. Although Alcmaeon of Croton (Greece) located mental processes in the brain about 2,500 years ago (and thus developed what is now called the brain hypothesis), the merit of this hypothesis has been hotly debated ever since and is still not universally accepted. The modern views were first clearly identified by Descartes in the seventeenth century. Descartes

Recognizing that the brain controls behavior was an important step, but the question of how the brain did this remained unanswered. One important philosophical issue that emerged in the late nineteenth century revolved around the question of whether the brain can be divided into separate parts representing separate functions or whether the brain operated as a whole and thus was indivisible. The identification of Broca’s area, and later Wernicke’s area, 136

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Methods of Studying Brain and Behavior Relations

137

Figure 8.1 Neurons. Note. A spiny stellate neuron from the nucleus accumbens showing the cell body and dendrites. The enlargement of a dendrite on the right shows the dendritic spines that provide the location of excitatory synapses.

as key to specific aspects of language suggested that functions were localized and led many neurologists to seek specific functions for every brain region. Parallel investigations revealed that the severity of cognitive loss was related to the extent of brain injury rather than the precise locus. Thus, other neurologists argued that functions like language could not be localized because so much of the brain was involved. One problem is that it is clear that behaviors such as emotion, memory, or language emerge from the activity of the entire nervous system and thus do not respect specific anatomical structures. This is not to say that specific regions do not play larger (or smaller) roles in different psychological functions but rather that all psychological functions require the contribution of many different neural systems, which by their nature are not precisely housed in single places. The final step in the historical development of current thoughts about brain organization and function was the recognition that the nervous system is composed of discrete autonomous units, neurons and glia, that are not physically connected but interact to generate nervous activity (Figure 8.1). The neuron hypothesis stipulates that neurons carry out the brain’s major functions whereas glia aid and modulate the neuron’s activities—for example, forming the fatty covering, or insulation, over neurons, as well as producing various chemicals that influence neuronal functions. The idea that neurons represented individual units in brain function dates back to Ramon y Cajal early in the twentieth century but it was only later that it was discovered that there are two distinctly different types of neurons that function to excite or inhibit the activity of other neurons. This dichotomy is philosophically very important because we now can see that not only does the brain produce thought and behavior but it also inhibits thoughts

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and behaviors. The recognition of this distinction is fundamental to understanding both brain function and its dysfunction.

METHODS OF STUDYING BRAIN AND BEHAVIOR RELATIONS The first insights into brain function were based on observations of naturally occurring injuries in people. It was not until the mid-1800s that experimental techniques, such as using electrical stimulation of the brain to determine functions, began to emerge. It was well into the twentieth century before a systematic science of brain function began to emerge both experimentally and clinically. We next review the principal methods used to outline the general anatomical and functional organization of the brain. Human Neuropsychology Human neuropsychology is the science that relates brain function to cognitive behavior. There is a rich history of clinical neurology dating well before Broca’s 1861 description of his patient Tan who had lost the ability to speak, but Broca’s patient was particularly important because it was the first time that a brain function was placed in a particular location in the brain—in this case “Broca’s area.” It was not until World War I that systematic descriptions of large numbers of cases of head-injured soldiers began to provide a basis for modern neuropsychology (e.g., Holmes, 1918). Soldiers with gunshot or shrapnel wounds to the brain showed specific symptoms, many of which were unexpected. For example, soldiers who were unable to respond to stimuli on their left side seemed unaware that

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there was any problem and would even deny that they had any difficulties. Neuropsychology became a systematic field of investigation only after World War II, however, and the term neuropsychology was first formally used in 1949 (Hebb, 1949). A study of an amnesic patient, who retained past memories but lost the ability to form new memories (Scoville & Milner, 1957), marked an important step in the development of modern neuropsychology because it led the systematic investigation of memory. The patient, referred to as H.M., had a selective removal of the hippocampus, a structure in the temporal lobe, to reduce his epileptic seizures. Immediately after the surgery, H.M. exhibited a severe amnesia in which he could recall virtually nothing that happened to him after his surgery—even though his memory of presurgical events appeared to be intact. Although amnesic patients had been described before, H.M. was the first case in which the symptoms could be attributed to a localized cerebral injury. Indeed, up until the description of H.M., studies on laboratory animals had been unable to find any injury that produced a specific memory loss, so H.M.’s condition was a major breakthrough. A further advance occurred with the studies of splitbrain patients by Sperry (1974). Patients who had the corpus callosum, which connects the two cerebral hemispheres, cut to relieve epileptic seizures revealed that each hemisphere makes complementary but different contributions to behavior. In particular, it became clear that not only was the left hemisphere verbal but that the left and right hemispheres had a different opinion about the world, and in the absence of the corpus callosum they acted pretty much independently of one another. The description of patient D.F. by Milner and Goodale (2006) led to another important shift in thinking about the organization of visual perception and action. D.F. suffered carbon monoxide poisoning and appeared to be essentially blind. Curiously, however, when she made arm and hand movements, she sometimes acted as though she could see. Thus, although she was unable to identify objects, she could reach and grasp the objects as if she knew what they were. Consider that our hand position in reaching for a glass is quite different from the position for picking up a pencil, but to make different movements would seem to require a recognition of the object. D.F.’s actions showed that some of our actions are conscious (such as identifying objects) and others are unconscious (such as making movements to manipulate objects). D.F. had no conscious vision but still had unconscious vision. Most neuropsychological studies in the past 50 years have not been case studies but rather studies of groups of patients with fairly circumscribed lesions, perhaps best exemplified by studies of patients with frontal or temporal

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lobectomies for the treatment of intractable epilepsy (e.g., Kolb & Whishaw, 2008). Sixty years of human neuropsychology have now clearly shown not only that functions are relatively localized in the cerebral cortex, but they have laid the groundwork for our current understanding of how the brain is organized. Electrophysiological Confirmation of Localization In 1870, Fristch and Hitzig (1956) described an extraordinary finding that electrical stimulation of the small portion of the cortex of a rabbit and a dog produced movements. Importantly, not only were there movements but the movements were selective such that stimulating different points led to different movements. Later studies showed similar effects in humans, other primates, and many laboratory animals (Woolsey, 1958). These studies led to the development of detailed somatosensory, motor, and language maps in the cerebral cortex (Penfield & Roberts, 1956). One of the difficulties of the early studies was that the cranial bones had to be opened to allow access to the brain, but in the past decade noninvasive techniques have been developed using magnetic stimulation on normal waking subjects. Laboratory Animal Studies of Brain Organization Although there were early isolated studies of animals such as dogs and birds with cerebral injuries, the first systematic studies of large numbers of animals were begun in the early part of the twentieth century. These studies had three distinct foci: (1) studies of how nerves sent messages using “simple” models such as the squid (e.g., Hodgkin & Huxley, 1952); (2) electrophysiological studies of spinal cord worked (e.g., Sherrington, 1948); and (3) behavioral studies of animals with discrete cerebral injuries (Lashley, 1960) directed toward studying how animals thought and remembered. By the 1960s, both the electrophysiological and behavioral techniques had evolved to a point that a new field of behavioral neuroscience began to emerge that paralleled the human studies described earlier. The recognition that the rodent is an excellent model for understanding basic principles of cerebral organization in primates allowed researchers to expand investigations of a wide range of topics ranging from motor control to memory, as well as allowing investigations of factors influencing recovery from brain injury such as stroke (e.g., Kolb & Tees, 1990; Whishaw & Kolb, 2006). Noninvasive Imaging One of the historical impediments to studying brain organization in humans has been the difficulty in studying brain

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function in normal volunteers. Although the electroenceophalogram (EEG), which measures electrical activity of the brain through the skull, was developed in the 1930s, its primary use was in identifying states of consciousness or brain pathology. The major advances in computer technology in the latter part of the twentieth century allowed EEG, to be used to identify discrete neural processing that is best exemplified by event-related potentials (ERPs). ERPs are signals that are correlated with specific forms of sensory processing, such as the recognition of some specific information. The difficulty with ERPs, however, is that although the temporal resolution of the electrical signal is good, the spatial localization of the signal is difficult because the recording is through the skull. The development of metabolic and blood flow measures of brain activity using positron emission tomography (PET) and functional magnetic resonance imaging (fMRI) provide a more precise view of brain function (see Kolb & Whishaw, 2008). The logic of these methods is that regions of the brain that are more active have a higher metabolic activity that can be seen by the brain’s use of glucose, oxygen, and blood. Thus, just as somatic muscles that are more active have higher metabolic demands than when they are less active, brain regions that are active use more resources than regions that are less active. It is now possible to do parallel studies using ERP and fMRI measures and thus allow better-refined temporal and spatial resolution. The development of computer-based data collection and sophisticated statistical procedures to average signals across subjects has allowed researchers to compare the brain activity of multiple subjects. For example, Hasson, Nir, Levy, Fuhrmann, & Malach (2004) allowed five subjects to freely view a 30-minute segment of a feature film, The Good, The Bad, and The Ugly, while cortical activity was monitored via fMRI. The authors reasoned that such a rich and complex visual stimulation would be far more similar to natural vision than the highly constrained visual stimuli normally used in the laboratory. Comparison of brain activation across the subjects showed the brains of different individuals tended to act in unison during the free viewing. This surprising activity coherence suggests that a large expanse of the human cortex is stereotypically responsive to naturalistic audiovisual stimuli. Furthermore, although overall there was widespread activity in the cerebrum during the viewing, there also were selective activations related to the precise moment-to-moment film content. For example, specific regions were activated by faces or places, suggesting relatively localized processing. The generalized and specific nature of the cerebral processing is clearly relevant to the

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debates regarding localization of functions in the brain discussed earlier. Molecular Studies With the description of the genome of both humans and laboratory animals in the early part of the twenty-first century, there has been a radical shift toward studying the genetic bases of brain function. Although these studies are likely to provide considerable insight into the detailed molecular mechanisms underlying neuronal functions, such studies have had little time to provide much impact on our general understanding of how the brain is organized functionally. One exception to this generalization comes from work showing that experience can modify gene expression, which in turn influences how behavior is expressed. For example, Weaver et al. (2004) showed that the amount of time a mother rat spends licking and grooming her infants can influence gene expression related to functions of the hypothalamic-pituitary-adrenal (HPA) stress axis. This genetic expression later influences the reactivity of the offspring to stress in adulthood but also determine how the female offspring interact with their pups.

GENERAL BRAIN ORGANIZATION Understanding the basic organization of the brain can be most easily seen in the evolutionary and ontogenetic development of the brain. Figure 8.2 shows a basic three-part structural plan that divides the brain into front, middle, and back components. The front and back components expand greatly in mammals and become further subdivided into five regions. Historically, embryologists gave rather cumbersome names to the various regions and these names remain, although they are seldom used in behavioral studies. The three regions of the primitive developing brain are first recognizable as enlargements at the front end of a fluid-filled tube. In the simple brain the front region (the prosencephalon) is responsible for olfaction and basic body functions such as feeding and drinking; the middle region (mesencephalon) for hearing and vision; and the back region (rhombencephalon) controls movement, balance, and breathing. The back end of the tube extends to form the spinal cord. As the brain enlarges in mammals and birds, cerebral hemispheres develop and existing functions are elaborated in the prosencephalon. The original tube also becomes elaborated to form pockets of fluid known as ventricles, as shown in Figure 8.3.

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(A) Fish, amphibian, reptile, human embryo at 25 days

(B) Mammals such as rat, human embryo at 50 days

(C) Fully developed human brain Telencephalon

Telencephalon Diencephalon Mesencephalon

Prosencephalon Mesencephalon

Myelencephalon

Spinal cord

Rhombencephalon

Diencephalon Mesencephalon Metencephalon Myelencephalon Spinal cord

Metencephalon

Spinal cord

Telencephalon (end brain)

Neocortex, basal ganglia, limbic system olfactory bulb, lateral ventricles

Diencephalon (between brain)

Thalamus, epithalamus, hypothalamus, pineal body, third ventricle

Mesencephalon

Tectum, tegmentum, cerebral aqueduct

Metencephalon (across-brain)

Cerebellum, pons, fourth ventricle

Myelencephalon (spinal brain)

Medulla oblongata, fourth ventricle

Spinal cord

Spinal cord

Prosencephalon (forebrain)

Mesencephalon (midbrain)

Figure 8.2 Steps in the ontogenetic development of the brain. Forebrain

Brainstem

Rhombencephalon (hindbrain) Spinal cord

Spinal cord

Note. (A) A three-chambered brain; (B) a five-chambered brain; (C) side view through the center of a human brain. From Fundamentals of Human Neuropsychology, fifth edition, by B. Kolb and I. Q. Whishaw, 2003, New York: Worth. Reprinted with permission.

Cerebral cortex Lateral ventricle Epithalamus Third ventricle Hypothalamus Thalamus Optic chiasm Fourth ventricle Cerebellum Medulla Spinal cord

Tegmentum Superior colliculus Pons

Inferior colliculus

Figure 8.3 Medial view through the center of the brain showing structures of the brain stem. Note. From Fundamentals of Human Neuropsychology, fifth edition, by B. Kolb and I. Q. Whishaw, 2003, New York: Worth. Reprinted with permission.

Cerebral aqueduct Reticular formation

The fluid in the ventricles, (cerebrospinal fluid or CSF) is produced by cells that line the ventricular walls. The CSF flows from the ventricles to eventually enter the circulatory system. The expanded prosencephalon is now referred to as the forebrain, and the remaining brain is referred to as the brain stem (Table 8.1). The brain stem receives nerves from all of the body’s senses and it sends nerves to control all of the body’s movements except the most complex movements of the fingers and toes of mammals. The forebrain acts to elaborate on the basic functions of the brain stem. In relatively primitive animals such as frogs, the entire brain is

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Tectum

essentially equivalent to the mammalian brain stem. Given that such animals have complex behavioral repertoires, it is obvious that the brain stem is quite a sophisticated piece of machinery. Brain Stem The brain stem can be divided into three functional regions: hindbrain, midbrain, and diencephalon. The diencephalon can be conceived as a “between brain” because it acts as a border between the lower (brain stem) and upper (forebrain) parts of the brain. Each brain stem region contains

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General Brain Organization 141 Table 8.1 Anatomical Divisions of the Central Nervous System Anatomical division

Functional division

Principal structures

Forebrain

Forebrain

Cerebral cortex Basal ganglia Limbic system

Brain stem

Diencephalon

Thalamus Hypothalamus

Midbrain

Tectum Tegmentum

Hindbrain

Cerebellum Pons Medulla oblongata Reticular formation

Spinal cord

Spinal nerves

Cervical nerves Thoracic nerves Lumbar nerves Sacral nerves

various subparts and thus performs more than a single task. Although all three regions have both sensory and motor functions, the hindbrain is more important for motor functions and the midbrain for sensory functions. The diencephalon plays a role in regulatory behaviors such as temperature regulation and the control of eating and drinking. Hindbrain The hindbrain has four major subregions (see Figure 8.3). The largest region of the hindbrain is the cerebellum, a structure that becomes progressively larger and more complex as behaviors become more complex as the forebrain expands. The pons and medulla contain substructures that control vital body functions such as breathing and the cardiovascular system. The final hindbrain region, the reticular formation, is a netlike mixture of neurons and nerve fibers with specialized roles in stimulating the forebrain. Midbrain The midbrain is composed of the tectum (roof of the ventricle) and tegmentum (floor of the ventricle). The tectum receives major inputs from the eyes and ears, with the optic nerve going to a region called the superior colliculus and the auditory nerve going to the inferior colliculus. The

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colliculi function both to analyze sensory input as well as to produce orienting movements related to sensory inputs, such as turning the eyes or head toward a sound. To allow such orientation the colliculi have a “map” of the external world so that the head and eyes can be directed correctly. The auditory and visual maps must overlap so that the two systems can work together. The tegmentum is composed of multiple structures, largely with movement-related functions. The main nuclei are the red nucleus (for control of limbs), substantia nigra (for the initiation and inhibition of movements), and the periacquiductal gray matter (for species typical behavior such as sexual behavior and for the modulation of pain). Diencephalon The integrating functions of the diencephalon require more anatomical parts than the rest of the brain stem. The two major structures are the hypothalamus and thalamus, both of which are composed of about 20 subnuclei (Figure 8.4). The hypothalamus contains nuclei associated with eating, drinking, thermal regulation, sexual behavior, emotional behavior, and hormone function. The hypothalamus is connected both directly and hormonally with the pituitary gland, which in turn produces hormones that travel to other organs such as the adrenal gland. In contrast to most of the brain stem, which is the same in males and females, there

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Diencephalon

Thalamus

Dorsomedial nucleus (connects to frontal lobe)

Lateral geniculate nucleus to visual cortex Medial geniculate nucleus to auditory cortex Auditory input

Optic tract from left eye

Hypothalamus and pituitary gland

Figure 8.4 Medial view of the diencephalon.

Hypothalamus

Note. (Right) Enlargement of the thalamus. (Bottom) Enlargement of the hypothalamus and pituitary. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

Pituitary stalk Pituitary gland

are sex differences in the anatomy of the hypothalamus that are presumably related both to sex-related hormones as well as sex-related differences in sexual and parental behaviors. The thalamus acts as a gateway for information traveling to the cortex (bark or outer portion of the forebrain). Each sense sends its input to a specific thalamic nucleus, which in turn sends information to specific cortical regions. For example, the lateral geniculate nucleus receives input from the optic nerve, and is thus visual, and the medial geniculate nucleus receives input from the auditory nerve and is auditory. Some thalamic regions have motor functions or act in an integrative fashion. The dorsomedial nucleus is an integrative region that receives input from many subcortical structures, as well as the olfactory system, and passes this integrated information to the frontal lobe of the cortex.

Forebrain The forebrain is the largest region of the mammalian brain and like the brain stem, it is composed of multiple regions, the principle ones being the cerebral cortex, basal ganglia, and limbic lobe. One striking characteristic of the forebrain is that it consists of two nearly symmetrical hemispheres, the left and the right, which have both overlapping and specialized functions.

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Cerebral Cortex There are two types of cerebral cortex, the new and the old. The new cortex (neocortex) has six layers of gray matter (cell bodies) atop a layer of white matter (fibers). It is the neocortex that is visible when we view the brain from the outside. The neocortex is unique to mammals and is central to the emergence of mental functions such as language, memory, attention, and so on. The old cortex (sometimes called limbic cortex) has three or four layers of gray over white matter and is considered to be more primitive than neocortex. Although the limbic cortex does play an important role in emotional states, its functions also include other mental functions and thus there normally is little reason to draw much functional distinction between neo and limbic cortex. The cerebral cortex is divided into four lobes (frontal, parietal, temporal, occipital) that are named by the cranial bones that overlie the brain rather than by any particular functional characteristics of the regions (Figure 8.5). The frontal and parietal lobes are divided by a deep fissure known as the central sulcus, and the temporal lobe is divided from the frontal lobe by another fissure known as the lateral (or Sylvian) fissure. Although the lobes each have multiple functions, we can ascribe some gross functions to each lobe. The three posterior lobes all have sensory functions, occipital for vision, temporal for audition, and parietal for somatosensation. In addition, visual functions importantly influence

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General Brain Organization 143 Cingulate cortex (limbic cortex)

Lateral View Central sulcus

Frontal lobe

Parietal lobe

Lateral Temporal fissure lobe

Occipital lobe

Figure 8.5 A lateral view of the human brain illustrating the lobes of the brain and the different lobes.

Temporal lobe

Note. The lateral fissure separates the temporal and frontal lobes whereas the central sulcus separates the frontal and parietal lobes. From Fundamentals of Human Neuropsychology, fifth edition, by B. Kolb and I. Q. Whishaw, 2003, New York: Worth. Adapted with permission.

Amygdala Hippocampus

Figure 8.7 Medial view of the human brain showing the principal regions of the limbic system. Note. From Introduction to Brain and Behavior, second edition, by B. Kolb and I.Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

each of these lobes. The extensive distribution of visual functions speaks to the large amount of the cerebral cortex that is involved in some form of visual processing. The frontal lobe has motor, olfactory, and gustatory functions as well as an important integrative role that is sometimes referred to as “executive” function. The cerebral cortex can also be divided on the basis of its cellular architecture (called cytoarchitecture). Brodmann first described a detailed map of cortical regions early in the twentieth century (Kolb & Whishaw, 2003), assigning some 50 numbers to distinctly different regions (e.g., 1, 2, 3; Figure 8.6). Each Brodmann region can be associated with specific subfunctions of the lobes. With the development of newer anatomical methods over the past 100 years, the Brodmann map has been refined and expanded further (e.g., Petrides, 2005) and the general idea of distinct cytoarchitectonic zones that correspond to distinct functions has been enhanced.

evolutionary origin of these structures, some anatomists referred to these regions as the reptilian brain, but the term limbic (meaning lining) is more widely recognized. The limbic lobe is also sometimes referred to as the limbic system but given that the limbic regions do not function as a unified structure, the term limbic system is really a misnomer. The principal structures of the limbic lobe include the amygdala, hippocampus, and cingulate cortex (Figure 8.7). The amygdala plays a central role in emotion, and especially in fear. Removal of the amygdala completely removes fear, whereas overactivation of the amygdala can render a person highly anxious. The hippocampus has a central role in certain kinds of memory as well as in spatial navigation. Limbic regions are also partly responsible for the rewarding properties of experiences, including psychoactive drugs. The cingulate cortex has been associated with pain, emotion, and memory, and it connects extensively with the amygdala and hippocampus.

Limbic Lobe As the brain of amphibians and reptiles evolved, structures lining the brain stem began to emerge. In view of the

Lateral View

9 10

8

3 5 1 2

9

46 45 47

11 38

43

44 52

8

7 19

39

22 37

33

18 11

42 19

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31 30

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38

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19 18 17

25

20 20

3 1 2 5

24

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41 40

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36

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6

Medial View

37

19

18

Figure 8.6 Brodmann’s areas of the cortex. Note. From Fundamentals of Human Neuropsychology, fifth edition, by B. Kolb and I. Q. Whishaw, 2003, New York: Worth. Adapted with permission.

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Figure 8.8 Frontal section of the cerebral hemispheres showing the basal ganglia relative to surrounding structures. Note. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

Basal Ganglia The basal ganglia are a collection of nuclei that lie just below the white matter of the anterior region of the cerebral cortex (Figure 8.8). The three principal structures are the caudate nucleus, putamen, and globus pallidus. All cortical regions send connections to the basal ganglia, allowing the basal ganglia to be well informed about the activities of the cortex. The basal ganglia in turn send connections to the motor system, thus influencing movement. The functions of the basal ganglia can be observed by analyzing the behavior of patients with the many diseases that interfere with normal functioning of these regions. Among the most common disorders are Parkinson’s disease, in which movement becomes more difficult, and Huntington’s chorea, in which unwanted tics and gestures interfere with normal movement. Thus, basal ganglia damage does not produce a disorder in producing movements as in paralysis but rather produce a disorder in controlling movements. This distinction is important because it shows that movements are produced at lower levels (such as in the brain stem) but modulated by the forebrain.

RULES OF BRAIN FUNCTION Having considered the general organization of the brain, we are now in a position to look at the general principles that guide how the various parts of the brain work together. The Brain Produces Movement within a Perceptual World It Creates The simplest summary of brain function is that it produces behavior. To do so, however, it must have information about the world. Movements are not made in a vacuum but are related to objects, places, memories, and so on. The representation of the world is dependent on the nature of the information sent to the brain, however. A person who is

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color blind has a very different representation of the world than those who perceive color. Similarly, a person who has perfect pitch has a different world than those who do not. Furthermore, animals such as dogs have a rich olfactory world that humans do not share. In contrast, dogs have poor color vision. Our failure to perceive smells and the dogs’ failure to perceive colors does not mean that they are not there—only that the reality we create is different. Although we tend to think that the world that we perceive is what is actually there, it is clear that individual realities (both between and within species) are rough approximations of what is actually present. A special function of the brain of each animal species is to produce a reality that is adaptive for that species. In other words, the behavior that the brain produces is directly related to the details of the world that the brain has created. Dogs and people behave differently toward smells (or colors) because of the nature of the perceptual world that their respective brains have created. The Brain Creates “Maps” of the World Sensory information is represented in the brain in an orderly manner. Consider the feelings from your skin when a fly is walking along your arm. You perceive a place on your body and you can orient to it. Further, when the fly moves along the hand to the arm, you perceive it to be in different body locations. Similarly, when you want to move a finger, you can do so without making movements elsewhere. This specificity in perception and movement is enabled by sensory and motor maps of the body in your brain. Indeed, it was these body maps that Fritsch and Hitzig (1956) first found when they electrically stimulated the cortex. But maps are not just about the body. When we wander about the world we can identify places by sight and sound, so there must be visual and auditory maps as well, and these maps must somehow be coordinated because sights and sounds subjectively appear to be in the same place.

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Rules of Brain Function

Each sensory system has more than one map of the world. This is because maps are often quite specific. Although we perceive shape and color of objects to be a single thing, they are represented by separate maps (color versus shape) in the brain. We can make a similar distinction between the sensations of touch and pain in the skin. Similarly, sounds differ in their pitch as well as their meaning (language versus musical sounds). We therefore can think of the brain’s creation of sensory experience as a series of maps of different aspects of sensory information. One major change in the brain during evolution is the creation of more and more maps as the brain grows larger. Furthermore, species differences in sensory capacities reflect not only differences in the number of maps but also in the nature of the maps. Jerison (1991) suggested that the intelligence of a given species is related to the number of maps. As the brain develops more maps, it is necessary to bind these maps together to form single percepts from equivalent maps. One way to do this is to label the equivalencies to organize them. The labels would designate objects by their place and time in the external world. Labels can thus act to organize information and therefore form the basis of thought.

Sensory and Motor Functions Are Relatively Separated One of the oldest established laws of nervous system function is the law of Bell and Magendie (nineteenthcentury Scottish and French anatomists). They noticed in four-legged animals that sensory input to the spinal cord entered the top (dorsal) part of the cord, whereas motor outputs left via the bottom (ventral) part. (In upright-walking animals like humans, the dorsal region becomes the back and the ventral region the front but the principle remains.) This distinction between sensory and motor regions is maintained in the brain as well. Recall that the midbrain has a sensory region, the colliculi, and a motor region, the tegmentum. The sensorymotor distinction is obvious in the forebrain as well. We have separate maps for skin sensation and muscle movements. It is obvious that although sensory and motor functions are separated, they must also be closely related or we could not organize our movements to specific places and things. Recall, for example, that the sensory and motor regions of the midbrain act together to allow the brain to orient the body to visual and auditory stimuli. The integration of sensory and motor functions is therefore a critical function of the brain.

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145

Inputs and Outputs of the Brain Are Crossed One peculiar feature of brain organization is that most of the inputs and outputs are “crossed.” For example, the sensory inputs from the right side of the body, and thus information from the right side of the world, go to the left side of the brain, and the motor outputs of the left side of the brain go back to the right side of the body. This crossed organization explains why people with brain injury to the left side of the brain may have difficulty in moving the right side of the body. Animals with eyes on the sides of their heads, such as horses, have an arrangement of visual input with nearly all input from the left eye going to the right side of the brain and the right eye to the left side. Animals with the eyes located side-by-side at the front of the face, such as humans and cats, have a slightly different arrangement in which only about half of the projection crosses. The input from the left side of each eye goes to the right brain, and that from the right side goes to the left side. This arrangement shows that it is the views of each side of the viewer ’s world that are projected to the opposite brain hemisphere. A crossed brain must somehow join the two sides of the perceptual world together. As a result, innumerable connections link the two sides of the brain. The most prominent connection in the human brain is the corpus callosum, which is a large bundle of about 200 million nerve fibers that joins the cortex of the left and right hemispheres of the brain.

Brain Anatomy and Function Display Both Symmetry and Asymmetry Although the left and right hemispheres look very similar, there are some asymmetrical features in both the gross anatomy as well as the details of cytoarchitecture. Asymmetry is critical for certain mental functions because we require a single representation of sensory or motor functions to make appropriate behaviors. Consider language. If language were represented on both sides of the brain, we would have the disconcerting ability to speak out of both sides of our mouth at the same time. A simple solution is to locate language on one side of the brain, the left. The same organization holds for bird song—it is also located on the left side of the bird’s brain. The problem in processing of spatial information is handled in the same way. If we want to make a movement in space, we need to direct both sides of the body to the same place, and so one hemisphere organizes spatial behavior. Note, however, that we still need to be able to move our arms to different places and so exert motor control on both sides of the brain for these movements. Thus, although the hemispheres appear symmetrical structurally, they are asymmetrically involved

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in behavior with language normally found in the left hemisphere and various aspects of spatial behavior, located in the right hemisphere. We can now see why patients with surgery to cut the corpus callosum have difficulties and essentially have two minds, one in the left and one in the right hemisphere. Because language is in the left hemisphere, only the left side can speak, but, similarly, because many spatial functions are organized in the right hemisphere, only the right hemisphere can control some visuospatial functions “normally.” The Brain Works by a Juxtaposition of Excitation and Inhibition Although we have emphasized the brain’s role in making movements, we must also recognize that the brain acts to prevent movements as well. In order to make a directed movement such as picking up a glass of water, we must also not make other movements such as moving the hand back and forth. Thus, in producing movement, the brain through excitation produces some action and through inhibition prevents other action. One of the best examples of the control of excitation and inhibition can be seen in patients with Parkinson’s disease. Parkinson’s patients have an uncontrollable shaking of the hands because they have a failure in the system that inhibits such movements. Paradoxically, they often have difficulty in initiating movements and appear frozen because they are unable to generate the excitation needed to produce movements. This juxtaposition of excitation and inhibition is central to how the brain produces behavior and can be seen at the level of individual neurons. All neurons have a spontaneous rate of activity that can be either increased (excitation) or decreased (inhibition). Additionally, some neurons act to excite others, whereas other neurons are inhibitory. These excitatory and inhibitory actions are produced by specific neurochemicals via which neurons communicate. The primary excitatory chemical in the brain is glutamate and the primary inhibitory chemical in the brain is gamma-aminobutyric acid (GABA). Just as individual neurons can act in an excitatory or inhibitory manner, so can brain regions. This distinction can be seen in the effects of brain disease or injury. A brain injury to a region that normally initiates speech may render the person unable to talk whereas those with an injury to a region that inhibits inappropriate language (such as swearing) may be unable to inhibit this form of talking. Thus, brain injury can produce either a loss or a release of behavior via changes in the balance of excitation and inhibition.

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The Nervous System Functions on Multiple Levels Sensory and motor functions are carried out at many places in the brain. For example, both sensory processing and motor control occur in the spinal cord, the brain stem, as well as the forebrain. This multiplicity of functions results from the nature of brain evolution. Simple animals such as worms have mainly a spinal cord, more complex animals such as fish have a brain stem as well, and more complex animals have added a forebrain (Figure 8.9). Each new addition to the brain added a new level of behavioral complexity but did not discard previous levels of control. For example, as animals evolved legs, they also had to add forebrain area to move them and later when they developed independent digit movements this too required more forebrain area. The addition of new brain areas can be viewed as adding new levels of nervous system control. The new levels are not autonomous, however, but must be integrated into the existing neural systems. Adding the capacity to move fingers must be related to the prior capacity to move the limbs. Each new level can be conceived as a way of refining and elaborating the control provided by the earlier levels. The idea of levels can not only be seen in the addition of forebrain areas to refine the control of the brain stem but also within the forebrain we can see the addition of new areas. As mammals evolved, they developed an increased capacity to represent the world in the cortex, an ability that is related to the addition of more maps. The new maps must be related to the older ones, however, and again simply reflect an elaboration of the sensory world that was there before.

Brain Systems Are Organized Both Hierarchically and in Parallel A complication with adding multiple levels of brain area is that the levels must be extensively interconnected. There are two different solutions to the wiring problem: serial and parallel circuits (Figure 8.10). A serial circuit hooks up a linear series of all regions concerned with a particular function. Consider vision. In a serial system the information from the eyes goes to regions that detect the simplest properties such as color or brightness. This information would then be passed to another region that determines shape and then to another region that measures movement and so on until at the most complex level the information is understood to be your grandmother. Information therefore flows in a hierarchical manner sequentially from simpler to more complex regions. In addition to this hierarchy, there are parallel systems. Recall in our earlier example that patient D.F. could not

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Rules of Brain Function

Highest Remaining Functional Area

147

Behaviors

Reflexes: Responds by stretching, withdrawal, support, scratching, paw shaking, etc. to appropriate sensory stimulation. Spinal cord (spinal)

Hindbrain (low decerebrate)

Midbrain (high decerebrate)

Hypothalamus, thalamus (diencephalic)

Postural support: Performs units of movement (hissing, biting, growling, chewing, lapping, licking, etc.) when stimulated; shows exaggerated standing, postural reflexes, and elements of sleepwalking behavior. Spontaneous movement: Responds to simple features of visual and auditory stimulation; performs automatic behaviors such as grooming; performs sunsets of voluntary movements (standing, walking, turning, jumping, climbing, etc.) when stimulated. Affect and motivation: Voluntary movements occur spontaneously and excessively but are aimless; shows well-integrated but poorly directed affective behavior; thermoregulates effectively.

Self-maintenance: Links voluntary movements and automatic movements sufficiently well for self-maintenance (eating, drinking) in a simple environment. Basal ganglia (decorticate)

Cortex (normal)

Control and intention: Performs sequences of voluntary movements in organized patterns; responds to patterns of sensory stimulation. Contains circuits for forming cognitive maps and for responding to the relationships between objects, events, and things. Adds emotional value.

consciously perceive objects but could reach for them, thus reflecting a parallel conscious and unconscious visual system. But even within these two systems, there is parallel processing. Our color vision is well suited to distinguishing form and texture, and so is useful for perceiving grandmother. Our black-and-white vision is better suited to detecting the movement of objects. Color vision and blackand-white vision are dependent on different receptors in the eye (cones versus rods), different pathways to the cortex, and different functional areas in the cortex, and eventually different cognitive and behavioral functions. Similar arrangements occur for other sensory systems; for example, the body senses of fine touch and pressure and pain and temperature are mediated by different parallel systems. The parallel/hierarchical organization of the brain is further reflected in the organization of complex mental

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Figure 8.9 The anatomical and behavioral levels of the nervous system. Note. Shading indicates the highest remaining functional area, in a hierarchy from spinal cord to cortex. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

processes, such as language and memory. Consider, for example, that the meaning of words can be influenced by tone of voice. The actual word is stored in the left hemisphere language zones but the tone of voice is a function of the right hemisphere, again reflecting parallel processing of auditory information. The various levels of neural organization for mental processes thus may be fairly widely distributed in the brain, leading to the concept that such functions are really a result of the activity of a complex network of connected regions rather than a simple serial network. Functions in the Brain Are Both Localized and Distributed The identification of specific language regions (i.e., Broca’s and Wernicke’s areas) led to the idea that functions

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Sensory Input to the Brain Is Divided for Object Recognition and Motor Control

(A) Secondary

Primary

Tertiary

(B) Level 4 Level 3 Level 4

Level 2 Primary

Level 3 Level 4

Level 2 Level 3

Level 4

Figure 8.10 Models of cortical processing. Note. A: Simple serial hierarchical model of cortical processing. B: Distributed hierarchical model. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

were localized—at least in the forebrain. The fundamental problem, however, is in defining a function. Consider language as an example. Language includes the processes of producing words orally, in writing, and by sign language, as well as constructing complex compositions such as poems, stories, songs, and so on. Language also includes the comprehension of written, oral, and sign language, and even touched letters (Braille). Language also may include the capacity to use multiple languages. It also includes the ability to sing and play musical instruments. Language is clearly not a single function and must require many different types of neural processing that are widely distributed in the brain. People with selective brain injuries may lose specific language abilities to produce words, read words, understand words, and so forth. They may lose the ability to name living things but not inanimate things and vice versa. Only if damage is extensive is language extensively compromised. Thus, we can see that language is distributed in the brain, with specific language-related skills found in relatively discrete locations. Other psychological functions such as memory, social/ emotional behavior, spatial behavior, and so on also show the same pattern of localization and distribution of function. It therefore would take massive disease or injuries to completely eliminate any complex function. Indeed, one of the characteristics of dementia diseases such as Alzheimer ’s is that people can withstand widespread deterioration of the cortex and yet maintain remarkable functions until the disease is well progressed.

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Sensory systems evolved first for controlling motion, not for the recognition of specific information. Simple organisms can detect information and move to or from it. It is not necessary to “perceive” an object to direct movements toward or away from it. As animals, and their behaviors, became more complex they began to evolve ways of representing their environment. In animals with complex brains, such as ourselves, there are distinct systems for producing movement toward objects and for recognizing objects. The visual system serves as an example as we discovered in the case of D.F. (Figure 8.11). Visual information goes from the eyes to the brain stem to visual regions of the occipital lobe where it becomes divided: one route, known as the ventral stream, is to the temporal lobe for object recognition whereas a second route, known as the dorsal stream, is to the parietal lobe for the guidance of movement relative to objects (Milner & Goodale, 2006). Evidence that these systems are independent can be seen in people with injuries to the ventral or dorsal stream, respectively (for a review see Milner & Goodale, 2006). People such as D.F. with ventral stream injuries are “blind” for the recognition of objects, yet they nevertheless shape their hand appropriately when asked to reach for the objects that they cannot identify. Consider reaching for a cup, for example. When normal subjects reach for a cup, their hand forms a shape that is different than when they reach for a spoon. People with ventral stream injuries can make appropriate hand shapes yet they do not consciously recognize the object. In contrast, people with dorsal stream injuries can recognize objects Parietal lobe

Occipital lobe

Temporal lobe

Figure 8.11 Two streams of visual processing. Note. The dorsal stream is an unconscious online control of movement. The ventral stream is a conscious system for object recognition. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

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but make clumsy reaching movements because they do not form appropriate hand postures until they contact objects and then shape their hand based on tactile information. The recognition that the perception for movement and perception for thought are independent processes has important implications for understanding brain organization. First, these two systems provide an excellent example of parallel processing. Second, although our impression may be that we are aware of our sensory world, it is clear that the sensory analysis required for some movements is not conscious. Third, the presence of nonconscious and conscious brain processing underlies an important difference in our cognitive functions. The unconscious movement system is always acting in the present and in response to online sensory input. In contrast, the recognition system allows us to escape the present and to bring to bear information from the past. Thus, the recognition systems form the neural basis of enduring memory.

The emergence of a large prefrontal region is correlated with the emergence of sophisticated behavioral abilities required to organize behavior in place and time. The temporal aspect includes the development of a concept of the role of the self over time—in the past, present, and the future. Such abilities require a system that can constrain the search for sensory information, a process often referred to as “attention.” Attentional systems require a mechanism for continuous monitoring of both external and internal events, which essentially is a system designed for shortterm, or working, memory. A loss of prefrontal function, such as in diseases like schizophrenia or drug addiction, result in a loss of executive control of behavior, leading to disorganized and maladaptive responses to sensory information. The complexity of prefrontal functioning requires that this cerebral region develop slowly and likely does not fully mature until about 20 years of age in humans.

Prefrontal Cortex Combines Object and Motor Control Systems

Individual Differences in Brain Organization

The division of sensory systems into an object-related and a motor-control system does not shed light on how animals can decide to do something before goal objects are present. This form of long-term planning and behavioral organization evolved in parallel to the sensory systems and resides in a region known as the prefrontal cortex (Figure 8.12). The prefrontal cortex is the cerebral region lying at the frontmost region of the frontal lobe and is found in all mammals (Kolb, 2006). In primitive mammals, the prefrontal cortex is modest in size, but as more sensory maps are added in evolution, there is a corresponding increase in volume of the prefrontal cortex such that in humans this cortex represents about 15% of the cerebral cortex.

Premotor cortex

Motor cortex

It is remarkable how different we can be from each other. In part that is because no two brains are identical. There are, however, several factors that increase the interindividual variation in the brain, two prominent ones being sex and handedness (for a review, see Kolb & Whishaw, 2008). Just as gonadal hormones produce differences in genitalia, these hormones also produce differences in brain structure and thus brain function. Sex-related differences can be seen both in the gross anatomy of brain regions such as in the hypothalamus, as well as in the details of cell structure in the forebrain. These anatomical differences lead to a wide range of behavioral differences, including the superior verbal ability of women and the superior spatial ability of men. The differences are not large, on the order of less

Central sulcus

Dorsolateral prefrontal cortex Prefrontal Cortex

Inferior prefrontal cortex

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Orbital cortex

Figure 8.12 The organization of the frontal lobe. Note. From Introduction to Brain and Behavior, second edition, by B. Kolb and I. Q. Whishaw, 2005, New York: Worth. Reprinted with permission.

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Neuroanatomy/Neuropsychology

than a standard deviation, but they are consistent and are found across a wide range of populations and cultures. Similarly, there are differences in gross anatomy, cell structure, and connectivity in the right- and left-handed brain. Language provides a good example. At least 99% of right-handers have language in the left hemisphere but only about 67% of left-handers do. Although it is not known what anatomical differences predict which left-handers have left versus right hemisphere language, there is little doubt that there is some difference in neuronal organization that leads to the lateralization of language in the left versus the right.

Details of Brain Functioning Constantly Change Variability in neuronal organization is not only related to factors such as sex and handedness but is also related to experience. Experience-dependent variability reflects the brain’s capacity to alter its structure and function in reaction to environmental diversity, thus reflecting a capacity that is often referred to as brain plasticity. Although this term is now commonly used in psychology and neuroscience, it is not easily defined and is used to refer to changes at many levels in the nervous system ranging from molecular events, such as changes in gene expression, to behavior (e.g., Kolb & Gibb, 2008; Shaw & McEachern, 2001). Brain plasticity is required for learning and memory functions. In fact, information is stored in the nervous system only if there are changes in neuronal connectivity. Forgetting presumably reflects a loss of the connections that represented the memory. Brain plasticity is not just a characteristic of the mammalian brain but is found in the nervous system of all animals including even the simplest animals, such as the nematode C. elegans that is only a millimeter or so long (e.g., Rankin, 2005). Nonetheless, larger brains have more capacity for change and thus are likely to show more variability in neuronal organization. Brain plasticity is not always a good thing. Analysis of the brains of animals given addicting doses of drugs such as cocaine or morphine have shown large changes in neuronal connectivity that are suspected of underlying some of the maladaptive behaviors related to addiction (for a review, see Robinson & Kolb, 2004). There are many other examples of pathological plasticity including pathological pain (Baranauskas, 2001), pathological response to sickness (Raison, Capuron, & Miller, 2006), epilepsy (Teskey, 2001), and dementia (Mattson, Duan, Chan, & Guo, 2001).

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Psychological Functions Emerge from Extended Cerebral Networks Psychological functions such as memory, attention, emotion, and language can be described by words but they remain hypothetical constructs. A construct like memory is not a single thing but rather a reflection of many subprocesses, which we collectively refer to as memory. For example, we have memory for places, objects, faces, music, words, motor skills, and so on, and each of these requires a distinctive type of sensory processing. Furthermore, we have short-term memories of ongoing events and longterm memories of long past events. We also have memories of specific events as well as memories for which we can ascribe no single experience (e.g., knowing your own name). Nonetheless, although it should be no surprise that the memory of an old song or the rules of tennis are housed independently in the brain, there is a natural temptation to think that memory is found in a place in the brain. It is not. Thus, psychological constructs such as memory are widely distributed in both cortical and subcortical regions. The same is true of other psychological functions. The brain is not built on the concept of psychological functions but rather is built to support the processes that underlie different aspects of the functions. One example is language. We noted that for most people language is processed in the left hemisphere; however this is not because the brain evolved a place for language functions but rather that language requires certain types of auditory and motor processing that are housed in the left hemisphere.

SUMMARY The brain has a long evolutionary history and from a beginning of a few scattered neurons it has evolved in some species into a large, centrally located organ that represents the past, the present, and the future. Nevertheless, despite differences in size and complexity, the brains of different animal species are built on the same plan such that different regions are readily recognizable in different brains. This no doubt accounts for the many similarities displayed by diverse animal species and also allows us to generalize from the behavior of animals with simpler brains to ourselves. The brain has grown larger by the growth of existing structures and the addition of new structures, and, accordingly, behavior has become more complex by the expansion of some abilities and the addition of new behavioral strategies. The human brain has retained more primitive neural systems via which it makes online unconscious responses to the world but added systems via which it can represent that world consciously as a past, present,

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References 151

or future. In addition, despite having a basic anatomical plan, the brains of individuals vary enormously depending on their sex, handedness, and personal experience. Despite this plasticity, injury or disease that damages the brain reduces behavioral complexity by producing specific deficits if damage is limited, or generalized deficits if damage is extensive. Despite what neuroscience has learned about the brain, neuroscientists continue to study the factors that produce our conscious awareness of ourselves as individuals.

Kolb, B., & Whishaw, I. Q. (2008). Fundamentals of human neuropsychology (6th ed.). New York: Worth. Lashley, K. S. (1960). Functional determinants of cerebral localization. In F. A. Beach, D. O. Hebb, C. T. Morgan, & H. W. Nissen (Eds.), The neuropsychology of lashley (pp. 328–344). New York: McGraw-Hill. Mattson, M. P., Duan, W., Chan, S. L., & Guo, Z. (2001). Apoptotic and antiapoptotic signaling at the synapse: From adaptive plasticity to neurodegenerative disorders. In C. A. Shaw & J. McEachern (Eds.), Toward a theory of neuroplasticity (pp. 402–426). Philadelphia: Psychology Press. Milner, D., & Goodale, M. A. (2006). The visual brain in action (2nd ed.). New York: Oxford. Penfield, W., & Roberts, L. (1956). Speech and brain mechanisms. Princeton, NJ: Princeton University Press.

REFERENCES Baranauskas, G. (2001). Pain-induced plasticity in the spinal cord. In C. A. Shaw & J. McEachern (Eds.), Toward a theory of neuroplasticity (pp. 373–386). Philadelphia: Psychology Press. Fristch, G., & Hitzig, E. (1956). On the electrical excitability of the cerebrum. In G. von Bonin (Ed.), The cerebral cortex (pp. 73–96). Springfield, IL: Charles C Thomas. Hasson, U. Y., Nir, I. I., Levy, G., Fuhrmann, G. & Malach, R. (2004). Intersubject synchronization of cortical activity during natural vision. Science, 303, 1634–1640. Hebb, D. O. (1949). Organization of behavior. New York: McGraw-Hill. Hodgkin, A. L., & Huxley, A. F. (1952). A quantitative description of membrane current and its application to conduction and excitation in nerve. Journal of Physiology, 116, 497–506. Holmes, G. (1918). Disturbances of vision by cerebral lesions. British Journal of Ophthamology, 2, 353–384. Jerison, H. (1991). Brain size and the evolution of mind. New York: American Museum of Natural History.

Raison, C. L., Capuron, L., & Miller, A. H. (2006). Cytokines sing the blues: Inflammation and the pathogenesis of depression. Trends in Immunology, 27, 24–31. Rankin, C. H. (2005). Nematode memory: Now, where was I? Current Biology, 15, R374–R375. Robinson, T. E., & Kolb, B. (2004). Structural plasticity associated with drugs of abuse. Neuropharmacology, 47(Suppl 1), 33–46. Scoville, W. B., & Milner, B. (1957). Loss of recent memory after bilateral hippocampal lesions. Journal of Neurology, Neurosurgery, and Psychiatry, 20, 11–21. Shaw, C. A., & McEachern, J. C. (Eds.). (2001). Toward a theory of neuroplasticity (pp. 176–192). Philadelphia: Psychology Press. Sherrington, C. S. (1948). The integrative action of the nervous system. New Haven, CT: Yale University Press. Sperry, R.W. (1974). Lateral specialization in the surgically separated hemispheres. In F.O. Schmitt and JF.G. Worden (Eds.). Neurosciences: Third study program (pp. 5–20). Cambridge, MA: MIT Press.

Kolb, B. (2006). Do all mammals have a prefrontal cortex. In J. Kaas (Ed.), Evolution of nervous systems: A comprehenive review (Vol. 3, pp. 443–450). New York: Elsevier.

Teskey, G. C. (2001). Using kindling to model the nreuoplastic changes associated with learning and memory, neuropsychiatric disorders, and epilepsy. In C. A. Shaw & J. C. McEachern (Eds.), Toward a theory of neuroplasticity (pp. 347–358). Philadelphia: Psychology Press.

Kolb, B., & Gibb, R. (2008). Principles of neuroplasticity and behavior. In D. Stuss, I. Robertson, & G. Winocur (Eds.), Brain plasticity and rehabilitation, pp. 6–21). New York: Oxford University Press.

Weaver, I. C., Cervoni, N., Champagne, F. A., D’Alessio, A. C., Sharma, S., Seckl, J. R., et al. (2004). Epigenetic programming by maternal behavior. Nature Neuroscience, 7, 847–854.

Kolb, B., & Tees, R. C. (1990). The cerebral cortex of the rat. Cambridge, MA: MIT Press.

Whishaw, I. Q., & Kolb, B. (2006). The behavior of the laboratory rat. New York: Oxford.

Kolb, B., & Whishaw, I. Q. (2003). Fundamentals of human neuropsychology (5th ed.). New York: Worth.

Woolsey, C.N. (1958). Organization of somatic sensory and motor areas of cerebral cortex. In H. F. Harlow & C. N. Woolsey (Eds.), Biological and biochemical basis of behavior (pp. 63–81). Madison: University of Wisconsin Press.

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Petrides, M. (2005). Lateral prefrontal cortex: Architectonic and functional organization. Philosophical Transactions of the Royal Society of London. Series B, 360, 781–795.

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Chapter 9

Essentials of Functional Neuroimaging TOR D. WAGER, LUIS HERNANDEZ, AND MARTIN A. LINDQUIST

There has been explosive interest in the use of brain imaging to study cognitive and affective processes in recent years (Wager, Hernandez, Jonides, & Lindquist, 2007). The neuroimaging data from functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies are central to the emerging fields of cognitive neuroscience, affective neuroscience, social cognitive neuroscience, neuroeconomics, and related neurobehavioral disciplines. fMRI and PET data are being combined with data on human performance and psychophysiology in increasingly sophisticated ways to yield models of human thought, emotion, and behavior. The best such models are informed by the rich histories of cognitive psychology and psychophysiology and—due largely to the integration of neuroimaging data—are grounded in brain physiology. This grounding permits stronger and more specific connections with the neurosciences and biomedical sciences, allowing behavioral scientists to leverage a vast and growing literature on brain systems developed in these fields. All methods used in the human neurobehavioral sciences have limitations, and neuroimaging is no exception. The current trend is toward increasingly interdisciplinary approaches that use multiple methodologies to overcome some of the limitations of each method used in isolation. Recent advances in engineering and signal processing allow electroencephalography (EEG) and fMRI data to be collected simultaneously (Goldman, Stern, Engel, & Cohen, 2000), which provides improved temporal precision, among other benefits. Combined fMRI and EEG/ magnetoencephalography (MEG) analyses are being developed that can provide better spatiotemporal resolution than either method alone (Dale et al., 2000; V. Menon, Ford, Lim, Glover, & Pfefferbaum, 1997). Neuroimaging data are also being combined with transcranial magnetic

stimulation to integrate the ability of neuroimaging to observe brain activity with the ability of transcranial magnetic stimulation (TMS) to manipulate brain function and examine causal effects (Bohning et al., 1997). The rapid pace of development and interdisciplinary nature of the neurobehavioral sciences presents an enormous challenge to researchers. Moving this kind of science forward requires a collaborative team with expertise in psychology, neuroanatomy, neurophysiology, physics, biomedical engineering, statistics, signal processing, and other disciplines depending on the research questions. True interdisciplinary collaboration is exceedingly challenging, because team members must know enough about the other disciplines to talk intelligently with experts in each field. Lead researchers on neuroimaging projects must know when to ask for help with various aspects of the project and what kind of expertise is needed. Supporting researchers must understand enough about the research questions and possibilities to bring their knowledge to bear in an optimal way. The goal in this chapter is to review the basic techniques involved in the acquisition and analysis of neuroimaging data—and some recent developments—in enough detail to highlight the most important issues and concerns. We also provide an overall road map of what kinds of study design and analysis options are available and some of their important limitations. The aspects of PET and fMRI methodology are organized here into four sections. The first section deals with what neuroimaging techniques measure, including the essentials of PET and fMRI data acquisition and the relationship between brain activity and observed signals in each modality. The second section describes the hierarchical structure of neuroimaging data and how these data are used to make psychological inferences. We emphasize two kinds of inferences: forward inferences about brain

Parts of this chapter are adapted from Wager, Hernandez, Jonides, and Lindquist (2007). Elements of functional neuroimaging. In Cacioppo, Tassinary, and Berntson (Eds.), Handbook of psychophysiology, fourth edition (pp. 19–55). Cambridge: Cambridge University Press. We would like to thank Dr. Doug Noll for providing Figure 9.3, and Matthew Davidson, Damon Abraham, Katherine Dahl, and Bryan Denny for helpful comments on the manuscript. 152

Handbook of Neuroscience for the Behavioral Science, edited by Gary G. Berntson and John T. Cacioppo. Copyright # 2009 John Wiley & Sons, Inc. c09.indd 152

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What Neuroimaging Techniques Measure

activity given a psychological experimental manipulation, and reverse inferences about psychology given patterns of brain activation. This section also deals with statistical inferences about populations and the localization of results from functional neuroimaging studies. The third section discusses experimental designs for neuroimaging experiments, including some considerations that are particular to neuroimaging data. The fourth section deals with neuroimaging data analysis, including sections on artifacts and signal processing before analysis (preprocessing), the general linear model (GLM), brain-behavior and brain-physiology relationships, and methods for investigating brain connectivity.

WHAT NEUROIMAGING TECHNIQUES MEASURE There are many ways to measure brain function, including fMRI, PET, single positron emission computerized tomography (SPECT), electroencephalography (EEG) with analysis of event-related potentials (ERP; Fabiani, Gratton, & Federmeier, 2007; Pizzagalli, 2007), magnetoencephalography (MEG; Hämäläinen, Hari, Ilmoniemi, Knuutila, & Lounasmaa, 1993), and near-infrared spectroscopy (Villringer & Chance, 1997). Each of these techniques provides a unique window into the functions of mind and brain. In this chapter, we mainly focus on PET and fMRI, because they are the most widely used and provide the

Table 9.1

153

most anatomically specific information across the entire brain. The relatively good spatial resolution of PET and fMRI complement the precise timing information provided by EEG and MEG. In addition, the ability of fMRI to measure activity over the entire brain every 2 s or so offers great potential for synergy with animal research. Whereas animal electrophysiology and lesion experiments are often focused on a single region, neuroimaging can assess global function and interactions across large-scale brain systems. PET and fMRI can be used in different ways, depending on the software and type of imaging chosen, to measure biological processes related to brain activity. Measures are generally obtained for each of a large number of local regions of brain tissue called “voxels” (three-dimensional pixels; imagine little cubes stacked together), providing 3-D brain maps. The partial list of popular measures and techniques shown in Table 9.1 includes measures of both brain structure and function. Structural measures may be divided into measures related to gray- and white-matter volume and density, and measures related to neurochemical receptors and other biomarkers. The most frequently used functional measures are those that measure processes related to overall neuronal/ glial activity, referred to here as “activation.” These measures include measures of glucose metabolism, blood flow or perfusion in PET and arterial spin labeling (ASL), and the Blood Oxygen Level Dependent (BOLD) signal in fMRI. Activation and deactivation in both PET and fMRI reflect changes in neural activity only indirectly, and they measure

Summary of PET and fMRI methods. Techniques for Studying Brain Structure

What Is Imaged?

Technique

Analysis

Gray/white matter/CSF distinctions

T1-weighted imaging (MRI)

Voxel-based morphometry (VBM), volume-based measures, surface-based measures (e.g., cortical thickness)

Gray/white matter/CSF distinctions

T2-weighted imaging (MRI)

Same as preceding

White-matter structure

Diffusion tensor imaging (DTI); Radioligand binding

Diffusion tractography

Neurochemical receptor occupancy

(PET); GABA-A: [C-11] flumazenil; dopamine D2:

Kinetic modeling, Logan-plot analysis

[C-11 raclopride]; Mu-opioids, [C-11] carfentanil; acetylcholine: [F-18] epibatidine, [C-11] scopolamine, serotonin: [C- 11] benzylamine; others

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Gene expression

PET radiolabeling; MR spectroscopy with kinetic modeling

Metabolites and various biomarkers

MR spectroscopy

Regional blood flow (perfusion)

[O-15] PET

Voxel-wise linear modeling; multivariate connectivity techniques

Relative Hb deoxygenation

Blood Oxygen Level Dependent (BOLD) signal, T2*-weighted image

Same as preceding

Glucose metabolism

[F-18]-fluorodeoxyglucose (FDG) PET

Same as preceding

Regional blood flow (perfusion)

Arterial spin labeling (ASL) fMRI

Same as preceding

Task-related neurochemistry

Radioligand binding (PET); see above

Kinetic modeling, Logan-plot analysis followed by linear modeling

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Table 9.2

Relative advantages of fMRI and PET.

Advantages of fMRI Cost and availability

fMRI has lower cost, more facilities available

Spatial resolution

fMRI has higher resolution, but new PET scanners can have same functional resolution for group studies

Temporal resolution

fMRI is superior, permitting event-related designs

Brain connectivity analyses

fMRI permits time-series connectivity analysis; PET and fMRI both permit individual differences analysis

Combination with other measures

Simultaneous time-series acquisition of fMRI and EEG provides most detailed mapping of relationships

Single-subject studies

fMRI permits detailed high-resolution studies of individuals

Repeatability

fMRI does not use radioactive substances, so frequent scans are considered safe

Measuring neurochemistry

PET is superior; can be used to directly investigate neurochemistry

Transparency of activation measures

PET provides more direct measures of blood flow or metabolism

Artifacts

PET does not suffer from magnetic susceptibility artifacts and gradient- or RF-related artifacts

Combination with other measures

PET is not magnetic and can be combined with simultaneous EEG, MEG, and TMS

Studying baseline activity

PET provides quantitative measure of baseline state; ASL fMRI also can, but is less commonly available

Naturalness of environment

PET is quieter and has more open physical environment; advantage for auditory and emotion tasks

different biological processes related to brain activity, which may be broadly defined as the energy-consuming activity of neurons and glia, and the electrical and chemical signals they produce. Thus, both PET and fMRI can be used to measure brain activity, though each has unique advantages. These are summarized in Table 9.2. Measures of Brain Structure Structural Scans MRI can provide detailed anatomical scans of gray and white matter with a spatial resolution well below 1 mm3. These images are used to localize functional results in individual or group-averaged brains. A growing set of measures related to brain structure allows for the analysis of changes with practice or development, effects of aging, and differences between healthy individuals and those with psychological disorders. A popular way of analyzing gray-matter density is the voxel-based morphometry (VBM) method (Ashburner & Friston, 2000; Good et al., 2001), which uses structural image intensity to measure gray- and white-matter density. Other methods use measures of cortical thickness derived from surface reconstruction and unfolding (Fischl, Sereno, & Dale, 1999; Van Essen & Dierker, 2007), or the volume of anatomically defined structures. A recent study reported that London taxi drivers, who had developed extensive expertise in spatial navigation, had larger posterior hippocampi (Maguire et al., 2000).

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Both structural and functional MRI images are obtained using the same scanner; the only difference is in how the scanner is programmed. A brief overview of the image acquisition process is as follows. A sample (e.g., a brain) is placed in a strong magnetic field and exposed to a radiofrequency (RF) electromagnetic field pulse. The nuclei absorb the energy only at a particular frequency band, which is strongly dependent on their electromagnetic environment, and become “excited” (they change their quantum energy state). The nuclei then emit the energy at the same frequency as they “relax.” The same antenna that produced the RF field detects the returned energy. Pulse sequences, or software programs that implement particular patterns of RF and gradient magnetic field manipulations (manipulations of the magnetic field’s shape), are used to acquire data that can be reconstructed into a map of the MR signal sources, that is, an image of the brain. Pulse sequence programming is the province of physicists and bioengineers; such divisions of labor among physicists, psychologists, neuroscientists, and statisticians are a hallmark of neuroimaging, which is highly interdisciplinary in nature. For more in-depth information, we recommend two approachable texts (Elster, 1994; Huettel, Song, & McCarthy, 2004), and more detailed texts for the advanced reader (Bernstein, King, & Zhou, 2004; Haacke, 1999). The relaxation process can be described by three values: T1, T2, and T2*. T1 and T2 are constants determined by the spin frequency, field strength, and tissue type (largely based on the hydrogen content, which depends in turn on

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What Neuroimaging Techniques Measure

A

155

B

C

Figure 9.1 The same slice of brain tissue can appear very different, depending on which relaxation mechanism is emphasized as the source of contrast in the pulse sequence. Note: Using long echo times emphasizes T2 differences among tissue types, and shortening the repetition time emphasizes T1 differences among tissue types. The same slice of the brain acquired as A: a T1weighted image and B: a T2-weighted image. C: Diffusion tensor imaging

how much water is in the tissue). T1 refers to the rate at which spins relax back to alignment with the main magnetic field, and T2 refers to the rate of attenuation of the magnetic field applied by the RF pulse. T2* is like T2, but depends additionally on local inhomogeneities in magnetic susceptibility that are caused by changes in blood flow and oxygenation, among other factors. T1 and T2 are constants determined by the spin frequency, field strength, and tissue type (largely based on the hydrogen content, which depends in turn on how much water is in the tissue). Different pulse sequences—patterns of RF excitations and data collection periods—produce images that are sensitive primarily to T1, T2, or T2*. Because T1 and T2 vary with tissue type but are otherwise constant, T1- and T2-weighted images can produce detailed representations of the boundaries between gray matter (mostly cell bodies), white matter (mostly axons), and cerebrospinal fluid (CSF). Because T2* is sensitive to flow and oxygenation, T2*-weighting is used to create images of brain function. An example of the same slice of tissue imaged with T1 and T2 weighting can be seen in Figure 9.1 A and B. The images look strikingly different. Changing the contrast mechanism can be very useful in differentiating brain structures or lesions, since some structures will be apparent in some kinds of images

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allows researchers to measure directional diffusion and reconstruct the fiber tracts of the brain. This provides a way to study how different brain areas are connected. Diffusion image is adapted from “Probabilistic Diffusion Tractography with Multiple Fibre Orientations: What Can We Gain?” by T. E. J. Behrens, H. J. Berg, S. Jbabdi, M. F. S., Rushworth, & M. W. Woolrich, 2007, NeuroImage, 34, pp. 144–155. Adapted with permission.

but not in others. Multiple sclerosis lesions are virtually invisible in T1-weighted images, but appear brightly in T2weighted images. Anatomical Connectivity MRI pulse sequences may also be tuned to be sensitive to directional (anisotropic) patterns of water diffusion, which may be used to track the course of axon (fiber) tracts. Water diffuses more readily along the axons that make up the brain’s white matter than across them. Diffusion tensor imaging (DTI) is an increasingly popular technique for measuring directional diffusion and reconstructing the fiber tracts of the brain (Figure 9.1 C; Denis Le Bihan et al., 2001). New tractography analyses for quantifying the thickness and connectivity of these tracts are being rapidly developed (Behrens et al., 2007). Such tools will increasingly allow researchers to analyze the relationships between structural connectivity and neuropsychological processes such as development, training, aging, cognitive and emotional function, and psychopathology (JohansenBerg & Behrens, 2006). DTI can be combined with other techniques, such as fMRI or other anatomical and neurochemical measures. One study used DTI to define adjacent subregions of the medial prefrontal cortex, and then used

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fMRI to show that the subregions responded differentially to different tasks (Johansen-Berg et al., 2004). Other Anatomical Measures PET imaging is complementary to MRI in an important way: It permits estimation of the density of a variety of neurochemical receptors across the brain. A radioactive label is chemically attached to a pharmacological agent and injected into the bloodstream. The agent is transported into the brain, where it binds to a specific class of receptors, depending on its biochemical nature. The PET camera detects the radiation emitted when the radioactive label decays, and so provides a 3-D map of the distribution of labeled substance across the brain. Kinetic models, which use systems of differential equations in conjunction with known kinetic properties of the pharmacological agent, can be used to quantify the label in extravascular space (tissue) and that bound to receptors. Related neurochemical measures, such as the rate of dopamine synthesis, can be obtained as well. This method is often used to study changes in endogenous neurochemical release, and we describe it more fully later in this chapter. In addition, MR spectroscopy provides a way of testing for the presence of biochemicals and some kinds of gene expression in a brain volume of interest, though this has not been widely applied yet in the cognitive neurosciences. Certain compounds produce well-defined peaks in the measured frequency spectrum, and can be readily detected, but many compounds of interest in neuroscience cannot.

quite fast, and their half-lives vary from a couple of minutes to a few hours, which means that a cyclotron must be available nearby to synthesize the radioactive tracer minutes before each PET scan. The tracer is injected into the subject’s bloodstream in either a bolus or a constant infusion that produces a steady-state concentration of tracer in the brain. As the tracer decays within the blood vessels and tissue of the brain, positrons are emitted. The positrons collide with nearby electrons (being oppositely charged, they attract), annihilating both particles and emitting two photons that shoot off in opposite directions. Photoreceptive cells positioned in an array around the participant’s head detect the photons. The fact that matched pairs of photons travel in exactly opposite directions and reach the detectors simultaneously is important for the tomographic reconstruction of the three-dimensional locations where the particles were annihilated. Note that the scanner does not directly detect the positrons themselves; it detects the energy that results from their annihilation. Depending on the design, most PET scanners are made up of an array of detectors that are arranged in a circle around the patient’s head, or in two separate flat arrays that are rotated around the patient’s head by a gantry. To detect simultaneously occurring pairs of photons, each pair of detectors on opposite sides of the participant’s head must be wired to a “coincidence detector” circuit, as illustrated in Figure 9.2. Small tubes (called septa or collimators) are placed around the detectors to shield them from radiation

Measures of Brain Activity Using Positron Emission Tomography Perhaps the most frequent use of both PET and fMRI is the study of metabolic and vascular changes that accompany changes in neural activity. With PET, we can separately measure glucose metabolism, oxygen consumption, and regional cerebral blood flow (rCBF). Each of these techniques allows us to make inferences about the localization of neural activity based on the assumption that neural activity is accompanied by a change in metabolism, in oxygen consumption, or in blood flow. The PET camera provides images by detecting positrons emitted by a radioactive tracer, the frequencies of which are reconstructed into three-dimensional volumes. Positrons are subatomic particles having the same mass but opposite charge as an electron—they are “antimatter electrons.” The most common radioactive tracers are 15O, “oxygen-15,” commonly used in blood-flow studies, 18F (fluorine), used in deoxyglucose mapping, and 13C (carbon) or 123I (iodine), used to label raclopride and other receptor agonists and antagonists. The decay rate of such isotopes is

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Positron emitted by isotope decay Neighboring electron

Scintillation counter

Scintillation counter Annihilation emits two photons in opposite directions

180 deg.

Coincidence detector

Computer

Display

Figure 9.2 A schematic diagram of the main components of a PET scanner.

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What Neuroimaging Techniques Measure

from the sides and help prevent coincidences due to background radiation. The injected tracer will be distributed throughout the blood vessels and tissue of the brain (indeed, throughout the rest of the body as well). Each pair of detectors counts photons emitted within the column of tissue between them. The density of photons that were emitted at each location can be calculated mathematically from the number of counts at each position or “projection.” PET images are simply maps of how many positron annihilation events occurred in the slice of interest. A more complete explanation of PET image formation, including a discussion of filtered back projection and other methods, can be found in several good texts (Bendriem & Townsend, 1998; Sandler, 2003). What do PET counts reflect? The answer depends on what molecule the label is attached to and where that molecule goes in the brain. Ideally, for 15O PET, counts reflect the rate of water uptake into tissue. 18-fluorodeoxyglucose (FDG) PET measures glucose uptake, whereas 13C Raclopride PET measures dopamine binding. In practice, the observed level of signal depends on several factors, including the concentration of the radiolabeled substance in the blood, the blood flow and volume, the presence of other endogenous chemicals that compete with the labeled substance, and kinetic properties such as the binding affinity of the substance to receptors, the rate of dissociation of the substance from receptors, and the rate at which the substance is broken down by endogenous chemicals. Accurate quantification of binding requires study of the kinetic properties of the substance in animals and the use of this information in kinetic models, which use differential equations to estimate the biological parameters of interest (e.g., ligand bound specifically to the receptor type of interest). Kinetic models have been developed to estimate how much tracer is contained in different categories, or compartments, of blood and tissue. Different forms of kinetic modeling have different numbers of compartments; a twocompartment model estimates how much of the radiolabeled compound is in the vasculature as opposed to in the brain. A three-compartment model used in receptor binding studies estimates tracer quantities in blood, free tracer in tissue, and label bound to receptors. Often a reference region with few or no receptors (the cerebellum for dopamine) is used to model the separation of free from bound tracer; this requires the assumption that none of the signal in the reference region comes from bound tracer. A fourcompartment model additionally separates tracer bound to receptors of a specific type (called specific binding) from those bound to other receptors (called nonspecific binding). For more details, we refer you to Frey (1999).

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Measures of Brain Activity Using Functional Magnetic Resonance Imaging Unlike PET, which can provide measures of overall activity or specific neurochemical systems, fMRI is principally used to obtain measures of regional brain activity (see Table 9.1). The most popular method is currently the Blood Oxygenation Level Dependent (BOLD) signal (Kwong et al., 1992; Ogawa et al., 1992), which is obtained using T2*-weighted images. Other methods are available but less widely used, including several varieties of Arterial Spin Labeling (ASL; Williams, Detre, Leigh, & Koretsky, 1992), which use pulse sequences sensitive to blood volume or cerebral perfusion. We focus here on BOLD physiology because it is overwhelmingly the most common method in current use. BOLD imaging takes advantage of the difference in T2* between oxygenated and deoxygenated hemoglobin. As neural activity increases, so does metabolic demand for oxygen and nutrients. Capillaries in the brain containing oxygen and nutrient-rich blood are separated from brain tissue by a lining of endothelial cells, which are connected to astroglia, a major type of glial cell that provides metabolic and neurochemical-recycling support for neurons. Neural firing signals the extraction of oxygen from hemoglobin in the blood, likely through glial processing pathways (Shulman, Rothman, Behar, & Hyder, 2004; Sibson et al., 1997). As oxygen is extracted from the blood, the hemoglobin becomes paramagnetic—iron atoms are more exposed to the surrounding water—which creates small distortions in the B0 field that cause a T2* decrease (a faster decay of the signal). Increases in deoxyhemoglobin can lead to a decrease in the BOLD signal, often referred to as the “initial dip.” The initial decrease in signal (whose existence is controversial) is followed by an increase, due to an over-compensation in blood flow that tips the balance toward oxygenated hemoglobin (and less signal loss due to dephasing), which leads to a higher BOLD signal. Initially, fMRI was performed by injection of contrast agents (such as iron) with paramagnetic properties, but the discovery that the T2* relaxation rate of oxygenated hemoglobin was longer than that of deoxygenated hemoglobin led to BOLD imaging as it is currently used with humans, without contrast agents (Kwong et al., 1992; Ogawa, Lee, Kay, & Tank, 1990). How well does the BOLD signal reflect increases in neural firing? The answer to this important question is complex, and understanding the physiological basis of the BOLD response is currently a topic of intense research (Buxton & Frank, 1997; Buxton, Uludag, Dubowitz, & Liu, 2004; Heeger & Ress, 2002; Vazquez & Noll, 1998). Some relationships among factors that contribute to BOLD signal are summarized in Figure 9.3.

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T2*-weighted Image Intensity ⫹

MR Properties

Decay Time (T2*) ⫹

Physical Effects

Other Factors Vessel Diameter Magnetic Field Vessel Orientation Uniformity (microscopic) Hematocrit Blood Volume Fraction ⫹ ⫺ Cerebral Blood Volume (CBV)

Blood Oxygenation

Vascular Physiological Effects

⫹ ⫹

⫹ ⫺

Cebral Blood Flow (CBF)

⫹ Metabolic Rates

Glucose and Oxygen Metabolism ⫹

Brain Function

Neuronal Activity

Figure 9.3 Influences on T2*-weighted signal in BOLD fMRI imaging. Note: Courtesy of Dr. Doug Noll.

The BOLD signal corresponds relatively closely to the local electrical field potential surrounding a group of cells—which is itself likely to reflect changes in postsynaptic activity—under many conditions. Demonstrations by Logothetis and colleagues have shown that high-field BOLD activity closely tracks the position of neural firing and local field potentials in cat visual cortex, even to the locations of specific columns of cells responding to particular line orientations (Logothetis, Pauls, Augath, Trinath, & Oeltermann, 2001). Under other conditions, however, neural activity and the BOLD signal may become decoupled (Disbrow, Slutsky, Roberts, & Krubitzer, 2000). For these reasons and others, the BOLD signal is only likely to reflect a portion of the changes in neural activity in response to a task or psychological state. Many regions may show changes in neural activity that is missed because they do not change the net metabolic demand of the region. Another important question is whether BOLD signal increases reflect neural excitation or inhibition. Some

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research supports the idea that much of the glucose and oxygen extraction from the blood is driven by glutamate metabolism, a major (usually) excitatory transmitter in the brain. Shulman and Rothman (1998) suggest that increased glucose uptake is controlled by astrocytes, whose end-feet contact the endothelial cells lining the walls of blood vessels. Glutamate, the primary excitatory neurotransmitter in the brain, is released by 60% to 90% of the brain’s neurons. When glutamate is released into synapses, it is taken up by astrocytes and transformed into glutamine. When glutamate activates the uptake transporters in an astrocyte, it may signal the astrocyte to increase glucose uptake from the blood vessels. Although it remains plausible that some metabolic (and BOLD) increases could be caused by increased inhibition of a region, in many tasks where both BOLD studies and neuronal recordings have been made, BOLD increases are found in regions in which many cells increase their activity. This is true in studies of visual processing, eye movements, task switching, working memory, food reward, pain, and other domains.

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What Neuroimaging Techniques Measure

Measures of Functional Neurochemistry Using Positron Emission Tomography The affinity of particular pharmacological agents for certain types of neurotransmitter receptors, such as raclopride for dopamine D2 receptors, provides a way to investigate the functional neurochemistry of the human brain. Radioactive labels such as C-11, a radioactive isotope of carbon, are synthesized in a cyclotron and attached to the pharmacological agent. Labeled compounds are injected into the arteries by either a bolus (a single injection) or continuous infusion, typically until the brain concentrations reach steady state. This method can be used to image task-dependent neurotransmitter release. As radioactively labeled neurotransmitters binds to receptors, the label degrades and gamma rays are emitted that are detected by the PET camera. When endogenous neurotransmitters are released in the brain, there is greater competition at receptors, and less binding of the labeled substance (referred to as specific binding). Thus, neurotransmitter release generally results in a reduction in radioactivity detected by the PET camera. The most common radioligands and transmitter systems studied are dopamine (particularly D2 receptors) using [11C]raclopride or [123I]iodobenzamide, muscarinic cholinergic receptors using [11C]scopolamine, opioids using [11C]carfentanil, and benzodiazepines using [11C]flumazenil. In addition, radioactive compounds that bind to serotonin, opioid, and several other receptors have been developed. As described earlier, because the dynamics of radioligands are complex, pharmacological agents must be carefully selected and tested in animals. Parameters from these studies are used in kinetic models to aid in quantifying how much labeled substance is bound to the receptor type of interest (Frey, 1999).

Limitations of Positron Emission Tomography and Functional Magnetic Resonance Imaging As you might expect, both PET and MRI have their share of pitfalls. You should consider the limitations of each technique not only when designing experiments, but also when examining the neuroimaging literature. Always ask the following question: “Are the activations caused by the experimental paradigm or by other unwanted sources?” Conversely, you should also ask: “Were there other active regions that were missed by the experimental paradigm?” Some of these errors may have occurred because of the spatial or temporal limitations of the technique, or they may be due to image artifacts or mischaracterized noise. Spatial Limitations Neither PET nor fMRI is well-suited for imaging small subcortical nuclei or cortical microcircuitry, though advances

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in high-field imaging and parallel acquisition methods are helping. The spatial resolution of PET is on the order of 1 to 1.5 cm3. fMRI resolution can be less than 1 mm3 in high-field imaging in animals, but is typically on the order of 27 to 36 mm3 or more for human studies. Thus, features such as cortical columns and even major subnuclei (e.g., there are 30 or so in each of the amygdala and thalamus) cannot typically be identified. The limiting factors in fMRI include signal strength and the point-spread function of BOLD imaging, which tends to extend beyond neural activation sites into draining veins (Duong et al., 2002). Careful work in individual participants has demonstrated the imaging of ocular dominance columns in humans (Cheng, Waggoner, & Tanaka, 2001). While this resolution does not sound all that bad, another factor seriously limits the spatial resolution in most studies. That is, making inferences about populations of subjects requires analyzing groups of individuals, each with a different brain. Usually, individual brains are aligned to one another through a registration or warping process (see Data Analysis: Implementation, later in this chapter), which introduces substantial blurring and noise in the group average. Thus, the effective resolution for group fMRI and PET studies is about the same. One estimate based on meta-analysis is that the spatial variation in the location of an activation peak among comparable group studies is 2 to 3 cm (Wager, Reading, & Jonides, 2004). Overcoming these limitations with high-resolution fMRI imaging is a challenging and developing research area. By focusing on particular regions and omitting data collection in much of the brain, voxels on the order of 1.5 mm per side can be acquired, yielding fMRI maps with resolution closer to the physical size of functional subregions (e.g., cortical fields within the hippocampus, or nuclei in the brain stem). This technique provides several advantages over standard mapping techniques. First, resolution can potentially be considerably enhanced, particularly when using high-field imaging and analysis techniques that remove some spread in fMRI signal due to draining veins (R. S. Menon, 2002). Second, collecting thinner slices can reduce susceptibility artifacts and improve imaging around the base of the brain (Morawetz et al., 2008). Finally, limitations in group studies related to interindividual variability can be partially overcome using identification of regions of interest on individual participants’ anatomical images or by advanced cortical unfolding and intersubject warping techniques (Zeineh, Engel, Thompson, & Bookheimer, 2003). However, there are costs as well. There is a substantial loss in signal due to the smaller volume of each voxel. In addition, coregistration techniques that ensure structureto-function correspondence and normalization techniques typically used to provide intersubject registration in group

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studies do not work very well when only a portion of the brain is imaged, because there are fewer functional landmarks for registration. High-resolution studies are promising when a small set of subcortical nuclei or nearby cortical regions are of primary interest. Acquisition Artifacts Artifactual activations (patterns of apparent activation arising from nonneural sources) and image distortions may arise from several sources, some unexpected. An early study found a prominent PET activation related to anticipation of a painful electric shock in the temporal pole (Reiman, Fusselman, Fox, & Raichle, 1989). However, it was discovered some time later that this temporal activation was actually located in the jaw—the subjects were clenching their teeth in anticipation of the shock. Important types of artifact include those related to magnetic susceptibility, reconstruction, head movement, heartbeat and breathing, instability in magnetic gradients used to acquire images, and radio-frequency interference from outside sources. Many of these artifacts apply only to or are more pronounced with fMRI; we provide more details on dealing with artifacts in analysis in the section Data Analysis: Implementation. Susceptibility artifacts in fMRI occur because magnetic gradients near air and fluid sinuses and at the edges of the brain cause local inhomogeneities in the magnetic field that affects the signal, causing distortion in echo-planar imaging (EPI) sequences and blurring and dropout in spiral sequences. These problems increase at higher field strengths and provide a significant barrier in performing effective high-field fMRI studies. Not all scanner/sequence combinations can reliably detect BOLD activity near these sinuses, which affects regions including the orbitofrontal cortex, inferior temporal cortex, hypothalamus, and amygdala. Signal may be recovered by using optimized sequences such as “z-shimming” (Constable & Spencer, 1999) or spiral in/out sequences (Glover & Law, 2001) or using a physical magnetic shim held in the mouth of the participant (Wilson & Jezzard, 2003). Signal loss and distortion may be further minimized by using improved reconstruction algorithms (Noll, Fessler, & Sutton, 2005) and “unwarping” algorithms that measure and attempt to correct EPI distortion (Andersson, Hutton, Ashburner, Turner, & Friston, 2001). Functional MRI also contains more sources of signal variation due to noise than does PET, including a substantial slow drift of the signal across time and higher frequency changes in the signal due to physiological processes accompanying heart rate and respiration. The lowfrequency noise component in fMRI can obscure results related to a psychological process of interest and it can

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produce false positive results, so it is usually removed statistically prior to analysis. A consequence of slow drift is that it is often impractical to use fMRI for designs in which a process of interest only happens once or unfolds slowly over time, such as drug highs or the experience of strong emotions, though some experimental/analysis approaches have been developed to facilitate such studies (Lindquist & Wager, 2007Lindquist, Waugh, & Wager, 2007). The vast majority of fMRI designs use discrete events that can be repeated many times over the course of the experiment (e.g., the most common method for studying emotion in fMRI is to repeatedly present pictures with emotional content). Temporal Resolution and Trial Structure Another important limitation of scanning with PET and fMRI is the temporal resolution of data acquisition. The details of this limitation are discussed in subsequent sections, but it is important to note here that PET and fMRI measure very different things, over different time scales. Because PET computes the amount of radioactivity emitted from a brain region, at least 30 seconds of scanning must pass before a sufficient sample of radioactive counts is collected. This limits the temporal resolution to blocks of time of at least 30 seconds, well longer than the temporal resolution of most cognitive processes. For glucose imaging (FDG) and receptor mapping using radiolabeled ligands, the period of data collection for a single condition is much longer, on the order of 30 to 40 minutes. Functional MRI has its own temporal limitation due largely to the latency and duration of the hemodynamic response to a neural event. Typically, changes in blood flow do not reach their peak until several seconds after local neuronal and metabolic activity has occurred. Thus, the locking of neural events to the vascular response is not very tight. Because of this limitation, a promising current direction is the estimation of the onset and peak latency of fMRI responses, and other parameters, averaged over many trials (Lindquist & Wager, 2007; R. S. Menon, Luknowsky, & Gati, 1998; Miezin, Maccotta, Ollinger, Petersen, & Buckner, 2000). We provide a more thorough discussion of this and related issues later in this chapter.

FROM DATA TO PSYCHOLOGICAL INFERENCE Goals of Data Analysis: Prediction and Inference A fundamental question in neuroimaging research is determining what the researcher hopes to achieve with the chosen method. Successful research requires a solid grasp of

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From Data to Psychological Inference

what kinds of imaging results constitute evidence for a psychological or physiological theory, and a grounded understanding of what kinds of results are likely to be obtainable. There are several potential inferential goals in neuroimaging studies. One goal is prediction of a psychological or disease state using neuroimaging data, which can be accomplished using regression or classification techniques (Norman, Polyn, Detre, & Haxby, 2006). More often, the psychologist would like to infer something about the structure of mental processes from imaging data. Making inferences about psychological states has been termed reverse inference, because it involves estimating the relative probabilities of different psychological hypotheses given the data, whereas what is observed in imaging studies is the probability of the data given a psychological state. Chapter 1 of this Handbook (Cacioppo & Berntson, in press) deals extensively with psychological inference from physiological data. In addition, several excellent papers review this issue in brain imaging (Poldrack, 2006; Sarter, Berntson, & Cacioppo, 1996) and physiological data generally (Cacioppo & Tassinary, 1990). Though we do not recapitulate this discussion here, we note that making psychological inferences based on activation in single brain regions is particularly problematic. Researchers have inferred that romantic love and retribution involve “reward system” activation because these conditions activate the caudate nucleus (Aron et al., 2005; de Quervain et al., 2004), that social rejection is like physical pain because it activates the anterior cingulate (Eisenberger, Lieberman, & Williams, 2003), among countless similar conclusions in the literature. These inferences are problematic because both these regions are involved in a wide range of tasks, including shifting of attention, working memory, and inhibition of simple motor responses, so their activation is not indicative of any particular psychological state (Bush, Luu, & Posner, 2000; Kastner & Ungerleider, 2000; Paus, 2001;

z ⫽ 16

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Note: All voxels identified show significant switch costs in at least two switch-no switch contrasts (p < .05 Family-wise error rate corrected in each). Thus, many regions not shown here may also show brain switch costs at less stringent thresholds. Regions colored in red are common activations that show no significant differences among costs for different types

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Van Snellenberg & Wager, in press; T. D. Wager, Jonides, & Reading, 2004; T. D. Wager, Jonides, Smith, & Nichols, 2005). There are other types of reverse inference that are less specific about the localization of psychological functions in the brain but are more defensible. These inferences fall into two major categories: those based on dissociations in activation among tasks, and those based on activation overlap across tasks. Both types involve studies that test two or more tasks in the same experiment. Dissociation occurs when a brain region is more active in Task A than in Task B. A double dissociation occurs when each task activates one region more than the other task. Double dissociations are a powerful tool because they imply that the two tasks utilize different processes, and that one task is not a subset of the other. A recent study in our laboratory illustrates this approach. We found that different types of task switching, or switching attention from one feature or object to another, differentially activate a set of regions thought to be involved in the control of attention (T. D. Wager, Jonides, Smith, & Nichols, 2005). Four types of switches were dissociable— each produced higher brain activity in some regions than the others—paralleling behavioral findings that performance switch costs are more highly correlated for similar types of switches (see Figure 9.4). The implication from this converging evidence is that different types of attention switching involve unique processes. Though double dissociations are potentially powerful, they have been criticized on several counts. For one thing, nonlinear relationships between task demands and activation can produce a double dissociation even if there are no processes unique to each task. Sternberg (2001) has proposed a stronger criterion for task separability called “separate modifiability,” which entails finding outcomes that are affected by each task but not the other task.

z ⫽ 10

Figure 9.4 (Figure C.1 in color section) Axial slices showing brain regions responsive to different types of switching and their overlap.

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Common activation Internal ⬎ External Enternal ⬎ Internal Object ⬎ Attribute Attribute ⬎ Object I ⬎ E and O ⬎ A

of switch (at p < .05 uncorrected). Other regions show evidence for greater activation in some switch types than others, as indicated in the legend. A ⫽ Attribute switch types; E ⫽ External; I ⫽ Internal; O ⫽ Object. From “Accounting for Nonlinear BOLD Effects in fMRI: Parameter Estimates and a Model for Prediction in Rapid Event-Related Studies,” by T. D. Wager, A. Vazquez, L. Hernandez, and D. C. Noll, 2005, NeuroImage, 25, pp. 206–218.

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A second type of psychological inference is based on the overlap in activation among tasks, which is often taken as evidence that the tasks share common processes (Sylvester et al., 2003). In the task switching study shown in Figure 9.4, even though there were quantitative dissociations in activation magnitude, all regions responded to at least two types of task switch, and some responded to all four. This implies at least some common processes across switch types, paralleled by significant performance correlations across types (T.D. Wager, Jonides, & Smith, 2006). Though the logic that activation overlap equals process overlap is commonly used, it provides weak support for shared neuronal processes: A single voxel in a neuroimaging study typically contains on the order of one million neurons, and it is entirely possible that different subsets of neurons in the same voxel are activated by different tasks. Paton, Belova, Morrison, and Salzman (2006) found different cells in the monkey amygdala that respond to either positive or negative predictions about upcoming rewards within the volume of a single neuroimaging voxel. Wang, Tanaka, and Tanifuji (1996), using optical imaging, found topographical maps of perceived head orientation in areas of temporal cortex that spanned only about 1 mm of cortex. fMRI-Adaptation Designs These issues have led to another method for assessing the use of common neural substrates across tasks. This method relies on repetition-suppression effects, or adaptation of fMRI responses to repeated events. It is possible to take advantage of this effect to tell whether two stimulus types (A and B) activate the same or different populations of neurons within a voxel (Grill-Spector & Malach, 2001). If a stimulus of type A is presented, then subsequent presentations of A will result in reduced signal (adaptation). The logic is that other stimuli, say of type B, that engage the same set of neurons will also evoke a reduced signal ([Balone– BafterA], cross-adaptation), whereas those that engage different neurons (even within the same voxel) will evoke a larger signal. Thus, small cross-adaptation effects may provide evidence that B engages different populations of neurons, whereas large cross-adaptation effects may be evidence that the circuitry for B and A overlap. However, caution in interpretation is in order, because habituation ([Balone– BafterA]) can be caused by the mechanical properties of the vascular bed (Vazquez et al., 2006; T. D. Wager, Vazquez, et al., 2005), and not to a neuronal habituation process. In fact, the response to A immediately after B is always likely to produce a reduced response compared with A alone because of the time it takes the vessels to regain their original shape after a BOLD response. This complicates the inference that similar adaptation and cross-adaptation implies overlapping neuronal populations. Another issue is that a recent

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electrophysiological study designed to test the validity of this paradigm reported differential habituation in single-cell recording to two stimuli, even though they both activated the same neuron (Sawamura, Orban, & Vogels, 2006). This finding challenges the inference that different adaptation and cross-adaptation effects imply different populations of neurons. Finally, though interpretations of fMRI-adaptation effects are often cast in terms of neuronal firing, more global processes related to memory may play an important role as well (Henson, 2003). The Hierarchical Structure of Neuroimaging Data Whichever type of inference is desired, inference is based on data, usually from multiple individuals. This section describes the structure of neuroimaging data, and the following sections describe some conceptual essentials of the steps that lead to psychological inference: valid group analysis, thresholding techniques, and localization of activated regions. Proper analysis of multisubject data in each voxel yields a statistical parametric map (SPM) of the reliability of contrast values—images that contain test statistic values (e.g., t-values) and p-values for the group analysis at each voxel. These statistic images are thresholded, with some provision for correcting for multiple comparisons across the many brain voxels tested, to obtain maps of suprathreshold or activated regions. Activated regions are localized relative to standard brain landmarks, often with the aid of brain atlases and norms, and interpreted in the context of other human and animal literature. Imaging data typically involves repeated observations over time—in fMRI as many as two thousand brain images can be collected in the course of a single imaging session for each participant. These images are nested within task conditions (e.g., tasks A and B, or “switch attention” [for a particular switch type] and “do not switch,” in our example study). Task conditions, in turn, are crossed with participant, meaning that they are assessed for each participant. Participants may be additionally nested within groups (e.g., patients versus controls, young versus elderly). Most often, a statistical model is specified for each participant that estimates the average response to each task condition of interest. Responses to different task conditions are compared by calculating contrasts across two or more conditions. Those measures are called contrast values, and they usually reflect a comparison of the activity levels between task conditions of interest (e.g., A minus B, “switch attention” minus “no switch”) that yields a single number for each participant. Contrast values for each voxel yield contrast images, three-dimensional maps of activation difference values for each participant. T-tests or comparable analyses can be performed for each voxel to discover where in the

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From Data to Psychological Inference

brain the difference is reliable. More detail on contrasts is provided in the section Data Analysis: Implementation. Analyzing contrast values has been referred to as the “subtraction method,” the logic of which is this: If you test two experimental conditions that differ by only one process, then a subtraction of the activations of one condition from those of the other should reveal the brain regions associated with the target process. Subtraction logic rests on a critical assumption that has been called the assumption of “pure insertion” (Sternberg, 1969). According to this assumption, changing one process does not change the way other processes are performed. Thus, by this assumption, the process of interest may be purely inserted into the sequence of operations without altering any other processes. Violations of subtraction logic have been demonstrated (Zarahn, Aguirre, & D’Esposito, 1997), and evoked activation depends on baseline cerebral blood flow in an area and other factors (Vazquez et al., 2006). However, subtractions remain widely used because comparisons among relative activity levels are central to the inferencemaking process. The assumption of pure insertion underlies the inference that more observed activity implies more intense neural and metabolic processes. In defense of the subtraction method, pure insertion need not be quantitatively or strictly true in all cases to yield useful comparisons across conditions. The contrast method applies to many comparisons other than the simple Task A – Task B subtraction, including incremental variations in task difficulty and factorial designs. It also applies to brain-performance correlation designs, in which activation contrast values are correlated with performance contrast values. These designs may employ multiple control or comparison conditions to strengthen the case for a relationship between activity in a particular brain region and a psychological process. They also extend beyond imaging of “activation” to studies that image neurochemical activity and other signals. Principles of Population Inference It is usually advantageous to design studies and statistical analyses in a way that permits inferences about a population of participants. Population inference is typical in all kinds of studies (e.g., when testing a new drug, researchers perform statistical tests that allow them to infer that the drug is likely to produce a benefit on average for individuals in a certain population). Even most studies of psychophysics and electrophysiology in monkeys, which often rely on only one or two participants for the entire study, need to be able to claim that their results apply beyond the particular individuals studied. They do so by invoking the additional assumption that all participants will behave the same way

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as the few observed in the study. In almost all domains of human neuropsychology, this is not a safe assumption, and statistics should be performed that permit population inference in a standard way. This can be achieved by considering the multilevel nature of neuroimaging data. A key to population inference is to treat the variation across participants as an error term in a group statistical analysis, which leads to generalizability of the results to new participants drawn from the same population. The most popular group analysis is the one-sample t-test on contrast estimates (e.g., Task A – Task B) at each voxel. This analysis tests whether the contrast of interest is nonzero on average for the population from which the sample was drawn, and it provides a starting point for our discussion on population inference. The principle, however, applies to any kind of statistical model, including more complex ANOVA and regression models and multivariate analyses such as group independent components analysis (ICA). Mixed versus Fixed Effects The one-sample t-test across contrast values treats the value of that contrast as a random variable with a normal distribution over subjects, and hence the error term in the statistical test is based on the variance across participants. Such an analysis has come to be known as a “random effects” analysis in the neuroimaging literature. Many early studies performed incorrect statistical analyses by lumping data from different participants together into one “supersubject” and analyzing the data using a single statistical model. This is called a “fixed effects” analysis because it treats the participant as a fixed effect, and assumes the only noise is due to measurement error within subjects. It is not appropriate for population inference because it does not account for individual differences. Collecting five hundred images each (250 of Task A and 250 of Task B) on two participants would be treated as the equivalent of collecting two images each (Task A and B) on 500 participants. Some researchers have argued that the fixed analysis allows researchers to make inferences about the brains of participants in the study, but not to a broader population. While this is technically true, inferences about particular individuals are seldom useful; such a lack of generalizability would be unacceptable in virtually any field, and we do not consider it appropriate for neuroimaging studies either. A more complete analysis is the mixed effects analysis, so termed because it estimates multiple sources of error, including measurement error within subjects and inter-individual differences between subjects. The one-sample t-test on contrast estimates described above is actually a simplified mixed-effects analysis that is valid if the standard errors of contrast estimates are the same for all participants.

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Full mixed-effects analyses use iterative techniques (such as the Expectation-Maximization [EM] algorithm) to obtain separate estimates of measurement noise and individual differences. They are implemented in popular packages such as Hierarchical Linear Modeling (HLM; Raudenbush & Bryk, 2002), R, and MLwiN (Rasbash, 2002). Neuroimaging data-friendly mixed-effects models are implemented in FSL (Beckmann, Jenkinson, & Smith, 2003; Woolrich, Behrens, Beckmann, Jenkinson, & Smith, 2004) and FMRISTAT (Worsley, Taylor, Tomaiuolo, & Lerch, 2004) software and are potentially implementable in SPM5. Thresholding and Multiple Comparisons The results of neuroimaging studies are often summarized as a set of “activated regions,” such as those shown in Figure 9.4. Such summaries describe brain activation by color-coding voxels whose t-values or comparable statistics (z or F) exceed a certain statistical threshold for significance. The implication is that these voxels are activated by the experimental task. A crucial decision is the choice of threshold to use in deciding whether voxels are active. In many fields, test statistics whose p-values are below .05 are considered sufficient evidence to reject the null hypothesis, with an acceptable false positive rate (alpha) of .05. In brain imaging, we often test on the order of 100,000 hypothesis tests (one for each voxel) at a single time. Hence, using a voxel-wise alpha of .05 means that 5% of the voxels on average will show false positive results. This implies that we actually expect on the order of 5,000 false positive results. Thus, even if an experiment produces no true activation, there is a good chance that without a more conservative correction for multiple comparisons, the activation map will show numerous activated regions, which would lead to erroneous conclusions. The traditional way to deal with this problem of multiple comparisons is to adjust the threshold so that the probability of obtaining a false positive is simultaneously controlled for every voxel (statistical test) in the brain. In neuroimaging, a variety of approaches toward controlling the false positive rate are commonly used—we discuss them in detail later. The fundamental difference between any method that is used is whether they control for the family wise error rate (FWER) or the false discovery rate (FDR). The FWER is the probability of obtaining any false positives in the brain, whereas the FDR is the proportion of false positives among all rejected tests. To illustrate the difference between FWER and FDR, imagine that we conduct a study on 100,000 brain voxels at alpha ⫽.001 uncorrected, and we find 300 significant voxels. According to theory, we would expect that

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100 (or 33%) of our significant discoveries, to be false positives, but which ones we cannot tell. Since 33% is a significant proportion of all active voxels, we may have low confidence that the activated regions are true results. Thus, it may be advantageous to set a threshold that limits the expected number of false positives to 5%. This is referred to as FDR control at the .05 level. In this case, we might argue that most of the results are likely to be true activations; however, we still cannot tell which voxels are truly activated and which are false positives. FWER, by contrast, is a stronger method for controlling false positives. Controlling the FWER at 5% implies that we set a threshold so that, if we were to repeat the previously mentioned experiment 100 times, only 5 out of the 100 experiments would result in one or more false positive voxels. Therefore, when controlling the FWER at 5%, we can be fairly certain that all voxels that are deemed active are truly active. The thresholds will typically be quite conservative, leading to problems with false negatives, or truly active voxels that are now deemed inactive. In our example, perhaps only 50 out of the 200 truly active voxels will give significant results. While we can be fairly confident that all 50 are true activations, we have still lost 150 active voxels, most of the true activity, which may distort our inferences and the usefulness of the experiment (see Figure 9.5). Most published PET and fMRI studies do not use either of these corrections; instead, they use arbitrary uncorrected thresholds, as shown in Figure 9.6, with a modal threshold of p < .001. A likely reason is that with the sample sizes typically available, corrected thresholds are so high that power is extremely low. This is extremely problematic when interpreting conclusions from individual studies, as many of the activated regions may simply be false positives. Imposing an arbitrary extent threshold for reporting based on the number of contiguous activated voxels does not necessarily correct the problem because imaging data are spatially smooth, and thus corrected thresholds should be reported whenever possible. Figure 9.6 shows the same activation map with spatially correlated noise thresholded at three different P-value levels. Due to the smoothness, the false-positive activation blobs (outside the squares) are contiguous regions of multiple voxels. Because achieving sufficient power is often not possible, it makes sense to report results at an uncorrected threshold and use meta-analysis or a comparable replication strategy to identify consistent results (T. D. Wager, Lindquist, & Kaplan, 2007), with the caveat that uncorrected results from individual studies cannot be strongly interpreted. Ideally, a study would report both corrected results and results at a reasonable uncorrected threshold (e.g., p < .001 and 10 contiguous voxels) for archival purposes.

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From Data to Psychological Inference

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(A) ␣ⴝ0.10, No correction

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Proportion of false positives FWER control at 10%

FWER Occurrence of false positive FDR control at 10%

0.0871

0.0952

0.0790

0.0908

0.0761

0.1090

0.0851

Proportion of active voxels that are false positives Figure 9.5 An overview of the effects of various approaches toward dealing with multiple comparisons. Note: (Top) Ten simulated t-maps were analyzed using an uncorrected threshold p < .10. True positives are indicated by white regions inside the gray squares. False positives are white pixels outside of the gray square. The proportion of false positives is listed under each image. They average 10%, as expected. (Middle) The same images with the threshold designed

␣ⴝ0.10

␣ⴝ0.01

␣ⴝ0.001

Figure 9.6. Multiple comparisons in the presence of spatially correlated noise. This figure shows simulated t-maps as in Figure 9.5, but with spatially “smooth” noise as is typical in actual fMRI experiments. In this case, imposing an arbitrary ‘extent threshold’ based on the number of contiguous activated voxels does not necessarily solve the problem of false positives. The same activation map, with spatially correlated noise, is thresholded at three different P-value levels. Due to the smoothness, the false-positive activation blobs (outside of the squares) are contiguous regions of multiple voxels, which can easily be misinterpreted as regions of activity.

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to control the Familywise error rate (FWER) at 10% using Bonferroni correction. There is only one false positive in the 10 images, at the cost of a significant increase in the number of false negatives. (Bottom) Similar results obtained using an FDR controlling procedure at the 10% level. The proportion of active voxels that are false positives is listed under each image. They average 10% as expected.

Family Wise Error Rate Correction The simplest way of controlling the FWER is to use Bonferroni correction. Here the alpha value is divided by the total number of statistical tests performed (voxels). If there is spatial dependence in the data—which is almost always the case, because the natural resolution and applied smoothing both lead to spatial smoothness in imaging data—this is an unnecessarily conservative correction that leads to a decrease in power to detect truly active voxels. Gaussian Random Field Theory (RFT; Worsley et al., 2004), used in SPM, FMRISTAT, and BRAINSTAT software (Taylor & Worsley, 2006), is another (more theoretically complicated) approach toward controlling the FWER. If the image is smooth and the number of subjects is relatively high (around 20), RFT is less conservative and provides control closer to the true false positive rate

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Essentials of Functional Neuroimaging Long-term Memory P-value thresholds used

60 # of Maps

50 0 ⫺50

40 20 0

50

0

⫺50

⫺100

0.0001 0.0005 0.001 0.005 Uncorrected

Figure 9.7 Common thresholds used in neuroimaging experiments. Note: A midline sagittal slice (left) shows the peak activations reported in 195 separate studies of long-term memory. The frequencies of P-value thresholds used for all statistical parametric maps in these studies are shown to the right. The most common threshold is P < .001, uncorrected for

than the Bonferroni method. With small samples, RFT is often more conservative than the Bonferroni method. It is acceptable to use the more lenient of the two, as they both control the FWER, which is what SPM currently does. In addition, RFT is used to assess the probability that k contiguous voxels are exceeding the threshold under the null hypothesis, leading to a “cluster-level” correction. Nichols and Hayasaka (2003) provide an excellent review of FWER correction methods, and they find that while RFT is overly conservative at the voxel level, it is somewhat liberal at the cluster level with small sample sizes. Both methods previously described for controlling the FWER assume that the error values are normally distributed, and that the variance of the errors is equal across all values of the predictors. As an alternative, nonparametric methods instead use the data to find the appropriate distribution. Using such methods can provide substantial improvements in power and validity, particularly with small sample sizes, and we regard them as the gold standard for use in imaging analyses. Thus, these tests can be used to verify the validity of the less computationally expensive parametric approaches. A popular package for doing nonparametric tests in group analyses, “Statistical Non-Parametric Mapping” (SnPM; Nichols & Holmes, 2002), is based on permutation tests. False Discovery Rate Control The false discovery rate (FDR) is a development in multiple comparison problems developed by Benjamini (1995) and Hochberg. While the FWER controls the probability of any false positives, the FDR controls the proportion of false positives among all rejected tests. The FDR controlling procedure is adaptive in the sense that the larger the signal, the lower the threshold. If all of the null hypotheses are true, the FDR will be equivalent to the FWER. Any procedure that controls the FWER will also control the FDR. Hence, any procedure that controls the FDR can only be

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0.01

0.05

0.05 Corr.

multiple comparisons. Corr: Corrected threshold. From “Meta-Analysis of Functional Neuroimaging Data: Current and Future Directions,” by T. D. Wager, M. Lindquist, and L. Kaplan, 2007, Social, Cognitive, and Affective Neuroscience, 2, pp. 150–158.

less stringent and lead to increased power. A major advantage is that since FDR controlling procedures work only on the p-values and not on the actual test statistics, it can be applied to any valid statistical test. Regions of Interest Analysis Because of the difficulty in preserving both false positive control and power in experiments with few subjects, researchers often specify regions of interest (ROIs) in which activation is expected before the study is conducted. ROI analyses are conducted variously over the average signal within a region, the peak activation voxel within a region, or preferably on individually defined anatomical or functional ROIs. Another technique involves testing every voxel within an ROI (e.g., the amygdala) and correcting for the number of voxels in the search volume. This is often referred to as a “small volume correction.” Two important cautions must be mentioned. First, conducting multiple ROI analyses increases the false positive rate. While it may be philosophically sound to independently test a small number of areas in which activation is expected, testing many such regions violates the spirit of a priori ROI specification and leads to an increased false positive rate. Small volume corrections in multiple ROIs also do not preserve the false positive rate across ROIs. Second, although activated regions can be used as ROIs for subsequent tests, the test used to define the region must be independent of the test conducted in that region. Acceptable examples include defining a region based on a main effect and then testing to see whether activity in that region is correlated with performance, or using the main effect of (A ⫹ B) to define a region and then testing for a difference (A – B). Problematic examples are defining a region activating in older subjects and then testing to see if its activity is reduced in younger subjects or defining a region based on activity in the first run of an experiment

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From Data to Psychological Inference

and then testing whether it shows less activity in subsequent runs. Both of these are not valid tests because they do not control for regression to the mean. Functional Localization and Atlases Accurately identifying the anatomical locations of activated regions is critical to making inferences about the meaning of brain imaging data. Knowing where activated areas lie permits comparisons with animal and human lesion and electrophysiology studies. It is also critical for accumulating knowledge across many neuroimaging studies. Localization is challenging for several reasons: First among them is the problem of variety; each brain is different, and it is not always possible to identify the same piece of brain tissue across different individuals (Thompson, Schwartz, Lin, Khan, & Toga, 1996; Vogt, Nimchinsky, Vogt, & Hof, 1995). Likewise, names for the same structures vary: The same section of the inferior frontal gyrus (IFG) can be referred to as IFG, inferior frontal convexity, Brodmann’s Area 47, ventrolateral prefrontal cortex, the pars orbitalis, or simply the lateral frontal cortex. Standard anatomical atlas brains differ as well, as do the algorithms used to match brains to these atlases. There is currently a wide and expanding array of available tools for localization and analysis. A database of tools is available from the Neuroimaging Informatics Tools and Resources Clearinghouse (NITRC; Table 9.3), and another useful list can be found at www.cma.mgh.harvard.edu/iatr/. The most accurate way to localize brain activity is to overlay functional activations on a co-registered, highresolution individual anatomical image. Many groups avoid issues of variability by defining anatomical regions of interest (ROIs) within individual participants and testing averaged activity in each ROI. Functional localizers— separate tasks or contrasts designed to locate functional regions in individuals—are also widely used, and functional and structural localizers can be combined to yield individualized ROIs. Structural ROIs are often used in detailed analysis of medial temporal regions in memory research; and retinotopic mapping, a functional localization procedure, to define individual visual-processing regions (V1, V2, V4, etc.) is now standard in research on the visual system (Tootell, Dale, Sereno, & Malach, 1996). However, the vast majority of studies are analyzed using voxel-wise analysis over much of the brain. In most applications, precise locations are difficult to define a priori within individuals, and often many regions as well as their connectivity are of interest. In such cases, atlas-based localization is used. Such localization can be performed using paper-based atlases (Duvernoy, 1995; Haines, 2000; Mai, Assheuer, & Paxinos, 2004), and there

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is no substitute for a deep knowledge of neuroanatomy. Automated atlases and digital tools are becoming increasingly integrated with analysis software. Some of the major ones are described next. Early approaches to atlas-based localization were based on the Talairach atlas (Talairach & Tournoux, 1988), a hand-drawn illustration of major structures and Brodmann’s Areas (BAs)—cortical regions demarcated according to their cytoarchitecture by Brodmann in 1909—from the left hemisphere of an elderly French woman. The brain is superimposed on a 3-D Cartesian reference grid whose origin is located at the anterior commissure. This allows brain structures to be identified by their coordinate locations. This stereotactic convention remains a standard today. Peak or center-of-mass coordinates from neuroimaging activations are reported in left to right (x), posterior to anterior (y), and inferior to superior (z) dimensions. Negative values on each dimension indicate locations at left, posterior, and inferior positions, respectively. The Talairach region labels were digitized, and a popular software program, the Talairach Daemon (Lancaster et al., 2000), allows researchers to map neuroimaging results onto Talairach’s labels. In addition, at least two popular software packages, AFNI (Cox, 1996) and BrainVoyager (Brain Innovation, Maastricht, Netherlands), allow researchers to align brains from neuroimaging studies to “Talairach space” using a few key landmarks identified on the brain and on the atlas. The alignment is performed by estimating 12 linear transformation parameters that include translation, rotation, zooms, and shears. Because the Talairach brain is not representative of any population and is not complete—only the left hemisphere was studied, and no histology was performed to accurately map BAs—Talairach coordinates and their corresponding BA labels should not be used (see Brett, Johnsrude, & Owen, 2002; Devlin & Poldrack, 2007, for discussion), as better alternatives are now available. Modern digital atlases based on group-averaged anatomy have largely replaced the Talairach brain. A current standard in the field is the Montreal Neurologic Institute’s (MNI’s) 305-brain average1 (Collins, Neelin, Peters, & Evans, 1994), shown in Figure 9.8 A, which is the standard reference brain for two of the most popular software packages, SPM and FSL (Smith et al., 2004) and the International Consortium for Brain Mapping project.

1

Called avg305T1 in SPM software. A higher-resolution template in the same space, called the ICBM-152 and named avg152T1 in SPM, is also available. It was created from the average of the 152 most prototypical images in the 305-brain set.

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Table 9.3

Current web sites for key resources.

Software Registries Neuroimaging Informatics Tools and Resources

www.nitrc.org

Internet Analysis Tools Registry

www.cma.mgh.harvard.edu/iatr/

Software Packages SPM

www.fil.ion.ucl.ac.uk/spm/software/

FSL

www.fmrib.ox.ac.uk/fsl/

AFNI

http://afni.nimh.nih.gov/

BrainVoyager

www.brainvoyager.com

FMRISTAT

www.math.mcgill.ca/keith/fmristat/

VoxBo

www.voxbo.org

FIASCO

www.stat.cmu.edu/˜fiasco/index.php?ref=FIASCO_home.shtml

Analysis Toolboxes SnPM, nonparametric analysis

www.sph.umich.edu/ni-stat/SnPM/

SPMd, image diagnostics

www.sph.umich.edu/ni-stat/SPMd/

Robust regression toolbox

www.columbia.edu/cu/psychology/tor/software.htm

Mediation analysis toolbox

www.columbia.edu/cu/psychology/tor/software.htm

GIFT (Group ICA)

http://icatb.sourceforge.net

MVPA toolbox: Classification

www.csbmb.princeton.edu/mvpa/

Netlab: Pattern classification

www.ncrg.aston.ac.uk/netlab/

Inverse logit HRF model

www.columbia.edu/cu/psychology/tor/software.htm

Atlases and Databases BrainMap

http://brainmap.org

ICBM

http://www.loni.ucla.edu/ICBM/

SUMS DB

http://sumsdb.wustl.edu:8081/sums/index.jsp

SPM Anatomy Toolbox

www.fz-juelich.de/inb/inb-3//spm_anatomy_toolbox

Wager lab meta-analyses

www.columbia.edu/cu/psychology/tor/MetaAnalysis.htm

Surface-Based Normalization/Warping FreeSurfer

http://surfer.nmr.mgh.harvard.edu

Caret/SureFit

http://brainmap.wustl.edu/caret/

Design Optimization Genetic Algorithm for fMRI

www.columbia.edu/cu/psychology/tor/software.htm

M-sequence toolbox

http://www.mathworks.com/matlabcentral/fileexchange/loadFile.do?objectId=3083

Digital atlases, including the MNI-305 template (not the Talairach template), permit fine-grained nonlinear warping of brain images to the template and can (if data quality is adequate) match the locations of gyri, sulci, and other local features across brains. A popular approach implemented in SPM software is intensity-based normalization. In this process, intensity values in a brain image are matched to a reference atlas image (template) by deforming the brain image in linear or nonlinear ways and using search algorithms to find the deformations that yield the best match. A preferred intensity-based method is the “unified segmentation

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and normalization” algorithm in SPM5 (Ashburner & Friston, 2005). A recent and promising alternative to intensity-based approaches is surface-based normalization, in which brain surfaces are reconstructed from segmented graymatter maps and inflated to a spherical shape or flattened (reviewed in Van Essen & Dierker, 2007). Features (e.g., gyri and sulci) are identified on structurally simpler 2-D or spherical brains, and the inflated brain is warped to an average spherical atlas brain. This approach has yielded better matches across individuals in comparison studies

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From Data to Psychological Inference (A) MNI 305 Average

(E)

(B) ICBM LBPA40 (SPM5)

(C) SPM Anatomy Toolbox

169

(D) Wager lab N18 (SPM5)

(F) ICBM MNI452 (Poly)

Anterior

Posterior

Anterior

Posterior

Medial

Lateral

Insula

Figure 9.8 (Figure C.2 in color section) Examples of atlas template images and a group-averaged, normalized structural image from a study. Note: A: The Montreal Neurologic Institute (MNI) 305-brain average as included with Statistical Parametric Mapping (SPM99, SPM2, or SPM5) software. The atlas brain is the same across software versions, though the algorithms for normalizing brains to the template have changed. B: The International Consortium for Brain Mapping (ICBM) LBPA atlas, based on manually labeled region from the Center for Morphometric Analysis at Harvard University. Each color represents a gross brain structure based on a consensus among 40 individually labeled brains. C: The singlesubject T1 brain co-registered with MNI space—the “colin brain” based on an average of 27 images of one individual—with overlaid consensus regions based on probabilistic cytoarchitecture. The probabilistic maps represented here are available in the SPM Anatomy toolbox, V1.5, and represent data from a series of studies on cytoarchitectural mapping of postmortem brains registered to the single-subject MNI template (Amunts et al., 2005; Eickhoff, Amunts, Mohlberg, & Zilles, 2006); see Table 9.3. Because the underlay brain is only a single brain, it may not be representative

(Fischl, Sereno, Tootell, & Dale, 1999; Van Essen & Dierker, 2007). Several free packages implement surfacebased normalization to templates registered to MNI space, including FreeSurfer (Table 9.3), Caret/SureFit software (Van Essen et al., 2001), and BrainVoyager. AFNI, using SUMA software (Saad, Reynolds, Argall, Japee, & Cox, 2004), and FSL have facilities for viewing and analyzing surface-based data with FreeSurfer and SureFit. Surfacebased add-ons in these packages permit surface-based registration to be performed after gross registration to the Talairach landmarks. Because the original BAs were not precisely or rigorously defined in a group, reporting of BAs using the Talairach atlas is not recommended (Devlin & Poldrack, 2007). Modern probabilistic cytoarchitectural atlases are being developed (Amunts, Schleicher, & Zilles, 2007), and some of these are

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Insula of anatomical locations in a study sample (compare the midbrain with that in D) and thus is not an ideal underlay image for localization of new study data. D: A trimmed average of 18 subjects’ T1 images initially warped to the MNI template using SPM and refined using a genetic algorithm based on custom code. This average brain shows good structural definition, indicating good intersubject registration, and is a suitable underlay image for functional activations. E: The ICBM 452-brain MNI average, with 5th-order polynomial warping to the standard space. The structural definition is excellent for the average of many brains, but the space is different from the MNI 305 space (e.g., the brain stem is much more anterior in the 452 brain), illustrating the need to report the specific atlas and procedures used in neuroimaging studies. F: Activations from a taskswitching paradigm (yellow; Wager, Jonides, Smith & Nichols, 2005a, 2005) superimposed on results from a meta-analysis of executive working memory (blue; Wager & Smith, 2003). Surface reconstruction was done with Caret software (Table 9.3) and shows a partially inflated left hemisphere (left) and a flattened cortical map of that hemisphere (right). Red and green arrows show the medial frontal gyrus and inferior frontal junction on each rendering.

available digitally either from the researchers or within FSL and SPM (as part of the SPM Anatomy Toolbox; Eickhoff et al., 2005; Figure 9.8B and C). In addition, software packages increasingly provide tools for visualizing activations relative to known functional and structural landmarks. Caret software allows study results to be mapped to a variety of atlases, including atlas brains included with SPM2, SPM99, and the Van Essen lab’s surface-based PALS atlas (see Figure 9.8F). Brain sections, surfaces, and flattened maps can be visualized, and digital overlays include probabilistic maps of visuotopic regions, orbitofrontal regions from a recent anatomical study (Ongur, Ferry, & Price, 2003), structural and functional landmarks, and a database of previous studies and reported peaks. The associated SumsDB database is a repository for study maps and peak coordinates (Table 9.3).

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Another way to localize functional activations is to compare them with the results of meta-analyses of other neuroimaging studies. Comparison with meta-analytic results can help identify functional landmarks and provide information on the kinds of tasks that have produced similar activation patterns. Whereas it was typical in early neuroimaging studies to claim consistency with previous studies based on activation in the same gross anatomical regions (e.g., activation of the anterior cingulate cortex), it is now recognized that many such regions are very large, and more precise correspondence is required to establish consistency across studies. Quantitative meta-analyses identify the precise locations that are most consistently activated across studies, and they thus provide excellent functional landmarks. Some meta-analysis maps are available on the SumsDB and BrainMap databases (Table 9.3), and a number are available on the web from individual researchers. Our lab currently has images from metaanalyses available on the web (Table 9.3), and these can be loaded into SPM, FSL, BrainVoyager, Caret, or other packages for visualization. The variety and heterogeneity of tools that are currently available is both a strength and an obstacle to effective localization. A few guidelines may aid in the process. First, it is preferable to overlay functional activations on an average of the actual anatomical brains from the study sample, after normalization (registration and/or warping) to a chosen template, instead of relying solely on an atlas brain (see Figure 9.8D). Normalization cannot be achieved perfectly in every region, and showing results on the subject’s actual anatomy is more accurate than assuming the template is a perfect representation. In addition, viewing the average warped brain can be informative about whether the normalization process yielded high co-registration of anatomical landmarks across participants, and can help identify problem areas. Single-subject atlases should not be taken as precise indicators of activation location in a study sample, and while they make attractive underlay images for activations, they should not be used for this purpose. Second, it is important to remember that atlas brains are different, and different algorithms used with the same atlas produce different results. Therefore, it is important to report which algorithm and which atlas were used. Also, it would be highly misleading to use a probabilistic atlas such as those in the SPM anatomy toolbox if the study brains were normalized to a different template (or with different procedures) than the one used to create the atlas (e.g., the SPM anatomy toolbox should not be used when normalizing to the ICBM-452 atlas; see Figure 9.8E). Regardless of the tools used, identifying functional activations on individual and group-averaged anatomy, collaborating with neuroanatomists when possible, and using print atlases to identify activations relative

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to structural landmarks are all essential components of the localization and interpretation process.

EXPERIMENTAL DESIGN FOR NEUROIMAGING EXPERIMENTS Types of Experimental Designs Designing a neuroimaging study involves a trade-off between experimental power and the ability to make strong inferences from the results. Some designs, such as the blocked design, typically yield high experimental power, but provide imprecise information about the particular psychological processes that activate a brain region. Event-related designs allow brain activation to be related more precisely to the particular cognitive processes engaged in certain types of trials, but suffer from decreased power. Researchers may also choose to focus intensively on testing one comparison of interest, and maximizing the power to detect this particular effect, or they may test multiple conditions to draw inferences about the generality of a brain region’s involvement in a class of similar psychological processes. In the following subsections, we describe several experimental designs and provide some discussion of the applications for which they are best suited. Blocked Designs Because long intervals of time (30 seconds or more) are required to obtain good PET images, the standard experimental design used in PET studies is the blocked design. In this design, different conditions in the experiment are presented as separate blocks of trials. To image a briefly occurring psychological process (e.g., the activation due to attention switching) using a blocked design, you might repeat the process of interest during an experimental block (A) and have the subject rest during a control block (B). The A – B (A minus B) comparison is the most basic type of contrast for this design. The blocked structure of PET designs (and blocked fMRI designs) imposes limitations on the interpretability of results. While activations related to slowly changing factors such as task-set or general motivation are well captured by blocked designs, they are not well suited if you want to image the neural responses to individual stimuli. In addition, the A – B contrast does not allow researchers to determine whether a region is activated solely in A, deactivated solely in B, or some combination of both effects. Multiple controls and comparison conditions can ameliorate this problem to some degree. The main advantage to using a blocked design is that it typically offers increased statistical power to detect a change. Under ideal conditions, blocked designs can be over 6 times

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Experimental Design for Neuroimaging Experiments

estimates typically come from studies of brief visual and motor events. In practice, the timing and shape of the HRF are known to vary across the brain, within an individual and across individuals (Aguirre et al., 1998; Schacter, Buckner, Koutstaal, Dale, & Rosen, 1997; Summerfield et al., 2006). Part of the variability is due to the underlying configuration of the vascular bed, which may cause differences in the HRF across brain regions in the same task for purely physiological reasons (Vazquez et al., 2006). Another source of variability is differences in the pattern of evoked neural activity in regions performing different functions related to the same task. Blocked designs are less sensitive to the variability of the HRF because they depend on the total activation caused by a train of stimulus events that makes the overall predicted response less sensitive to variations in the shape of responses to individual events. Predicted responses in block designs may still be inaccurate if the HRF model is inaccurate or if the density and time course of neural activity is not appropriately modeled (Price, Veltman, Ashburner, Josephs, & Friston, 1999). In a single-trial event-related design, events are spaced at least 20 to 30 seconds apart in time. fMRI signal can be observed on single trials if the eliciting stimulus is very strong (Duann et al., 2002), permitting the possibility of fitting models at the level of an individual trial (Rissman, Gazzaley, & D’Esposito, 2004). This promising technique enables the testing of relationships between brain activity and trial-level performance measures such as reaction time and emotion ratings for particular stimuli (Phan et al., 2004). Early studies frequently employed selective averaging of activity following onsets of a particular type (Aguirre, Singh, & D’Esposito, 1999; Buckner et al., 1998; Menon et al., 1998). However, even brief events (e.g., a 125 ms visual checkerboard display) have been shown to affect fMRI signal more than 30 s later (T. D. Wager, Vazquez,

as efficient as randomized event-related designs (T. D. Wager & Nichols, 2003). Generally, theory and simulations designed to assess experimental power in fMRI designs point to a 16 s to 18 s task / 16 to 18 s control alternating-block design as being optimal with respect to statistical power (Liu, 2004; Skudlarski, Constable, & Gore, 1999; T. D. Wager & Nichols, 2003). However, this is not always true as the relative power of a blocked design depends on whether the target mental process is engaged continuously in A and not at all in B, and whether imposing a block structure changes the task. Event-Related Functional Magnetic Resonance Imaging Event-related fMRI designs take advantage of the rapid dataacquisition capabilities of fMRI. They provide the ability to estimate the fMRI response evoked by specific stimuli or cognitive events within a trial (Rosen, Buckner, & Dale, 1998). In fMRI, the whole brain can be measured every 2 to 3 seconds (the “TR,” or repetition time of image acquisition), depending on the type of data acquisition and the spatial resolution of the images. The limiting factor in the temporal resolution of fMRI is generally not the speed of data acquisition, but rather the speed of the underlying evoked hemodynamic response to a neural event, referred to as the hemodynamic response function (HRF). A typical HRF begins within a second after neural activity occurs and peaks 5 to 8 seconds after that neural activity has peaked (Aguirre, Zarahn, & D’Esposito, 1998; Friston, Frith, Turner, & Frackowiak, 1995). Figure 9.9 shows the canonical HRF used in SPM software. While event-related designs are attractive because of their flexibility and the information they provide about individual responses, they rely more strongly on assumptions about the time course of both evoked neural activity and the HRF. It is common to assume a near-instantaneous neural response for brief events and a canonical HRF shape to generate linear models for statistical analyses (Figure 9.9; see also Data Analysis: Implementation). The canonical

Indicator functions A

Assumed HRF (Basis function)

Design Matrix (X)

Image plot of X A B C D

A Time

B

B

C

C 0

5 10 15 20

D

D 0

50 100 150 200 Time (s)

Figure 9.9 Construction of an event-related fMRI design matrix with four different event types (A-D), using the canonical SPM HRF.

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171

0

50 100 150 200 Time (s)

Note: Indicator functions corresponding to the four event types are convolved with the canonical HRF to create the regressors that make up the design matrix. An image of the design matrix is shown to the far right.

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et al., 2005). Because the selective averaging procedure does not take the stimulus history into account, it must be used with caution when responses to different events overlap in time. Because of this, the majority of analyses, including those that estimate the shapes of HRFs, are currently done within the GLM framework (see Data Analysis: Implementation). Reports that the fMRI BOLD response is linear with respect to stimulus history (Boynton, Engel, Glover, & Heeger, 1996) encouraged the use of more rapidly paced trials (Zarahn et al., 1997), spaced less than 1 s apart in the most extreme cases (Burock, Buckner, Woldorff, Rosen, & Dale, 1998; Dale & Buckner, 1997). Here linearity implies that the magnitude and shape of the HRF does not change depending on the preceding stimuli. Studies have found that nonlinear effects in rapid sequences (1 or 2 s) can be quite large (Birn, Saad, & Bandettini, 2001; Friston, Mechelli, Turner, & Price, 2000; Vazquez & Noll, 1998; T. D. Wager, Vazquez, et al., 2005), but that responses are roughly linear if events are spaced at least 4 s to 5 s apart (Miezin et al., 2000). If rapid designs are properly designed, they still allow us to discriminate the effects of different conditions. Key is incorporating “jitter,” or a variable interstimulus interval (ISI) between events, which is critical for comparing event-related responses with an implicit resting baseline to determine whether the events are “activations” or “deactivations” relative to rest. With a randomized and jittered design, sometimes several trials of a single type will occur in a row, and because the hemodynamic response to closely spaced events sums in a roughly linear fashion, the expected response to that trial-type will build to a high peak. Introducing jitter allows peaks and valleys in activation to develop that are specific to particular experimental conditions. If we care only about comparing event types (e.g., A – B), randomizing the order of events creates optimal rise and fall without additionally jittering the ISI. However, jittered ISIs are critical for comparing events to baseline activity and thus determining whether events activate or deactivate a voxel relative to that baseline (Josephs & Henson, 1999; Wager & Nichols, 2003). Suppose you have a rapid sequence with two types of trial—say, attention switch trials (S) and no-switch trials (N) as in the task-switching experiment described earlier (Figure 9.4). Randomly intermixing the trials with an ISI of 2 s will allow you to estimate the difference S – N. However, you cannot tell whether S and N activate or deactivate relative to some other baseline. If you vary the interstimulus intervals randomly between 2 and 16 s, you can compare S – N (with less power because there are fewer trials), but you also can test whether S and N show positive or negative activation responses. This ability comes from the inclusion of

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intertrial rest intervals against which to compare S and N, and the relatively unique signature of predicted responses to both S and N afforded by the random variation in ISIs. The advantages of rapid pacing—including faster trials and sometimes increased statistical efficiency—must be weighed against potential problems with nonlinearity, multicolinearity, and model misfitting. A current popular choice is to use jittered designs with interstimulus intervals of at least 4 s, with exponentially decreasing frequencies of delays up to 16 s. Optimized Experimental Designs What constitutes an optimal experimental design depends on the psychological nature of the task as well as on the ability of the fMRI signal to track changes introduced by the task manipulations over time. It also depends on the specific comparisons (contrasts) of interest in the study. To make matters worse, the delay and shape of the BOLD response (and ASL signals, and other blood flow-based methods), scanner drift, and nuisance factors such as physiological noise conspire to make experimental design for fMRI more complicated than for experiments that measure behavior alone. Not all designs with the same number of trials of a given set of conditions are equal, and the spacing and ordering of events is critical. Some intuitions and tests of design optimality follow from a deeper understanding of the statistical analysis of fMRI data and are elaborated on later. For a full treatment, there are several excellent papers (Josephs & Henson, 1999; Liu, 2004; Smith, Jenkinson, Beckmann, Miller, & Woolrich, 2007; T. D. Wager & Nichols, 2003). Several computer algorithms are available for constructing statistically optimized designs, including an approach based on m-sequences—mathematical sequences that are near-optimal for certain types of designs (Buracas & Boynton, 2002), and one based on a genetic algorithm (Wager & Nichols, 2003) that incorporates m-sequence designs as a starting point and considers the relative importance of various contrasts to the study goals in calculating optimality. Design Strategies for Enhanced Psychological Inference Thus far, we have alluded to a simple contrast between two conditions, the subtraction of a control condition (B) from an experimental one (A), or [A – B]. Such contrasts are critical because any task, performed alone, produces activation in huge portions of the brain. Though contrasts in event-related designs can usually be more readily interpreted as being evoked by specific psychological or physical events than those in blocked designs, a single contrast

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Experimental Design for Neuroimaging Experiments

leaves much room for incorrect inference. This is because there may be multiple psychological and physical differences between task conditions A and B. Imagine a study that compares a difficult version of a working memory task (A) with an easy one (B). Not only does the more difficult task require greater use of working memory, it may also elicit increases in heart rate, more frustration, more error-detection and correction processes, and more monitoring and adjustment of performance. The result is that the [A – B] contrast does not reveal activations associated only with working memory demand. Parametric Modulation Designs One way to constrain interpretation and strengthen the credibility of subtraction logic is to incrementally vary a parameter of interest across several levels (e.g., working memory demand), and perform multiple subtractions or linear contrasts across levels. An example is a study of the Tower of London task (Dagher, Owen, Boecker, & Brooks, 1999), which requires subjects to make a sequence of moves to transfer a stack of colored balls from one post to another in the correct order. The experimenters varied the number of moves incrementally from 1 to 6. Their results showed linear increases in activity in dorsolateral prefrontal cortex across all 6 conditions, suggesting that this area subserved the planning operations critical for good performance. Multiple Control Conditions and Conjunctions Another fruitful approach is to include multiple control conditions matched for various aspects of a target task of interest. In our working memory example, this might amount to including a control condition that produces comparable increase in heart rate without involving working memory, and another that is frustrating without involving working memory, and so on. If a brain region is more activated in the working memory task than each of the control tasks, then it strengthens the case that the region subserves working memory. A productive line of research using this approach is that of Kanwisher and colleagues in the study of face recognition (Kanwisher, McDermott, & Chun, 1997). In a long series of studies, they identified an area in the fusiform gyrus that responded to pictures of faces and drawings of faces, but not to houses, scrambled faces, partial faces, facial features, animal faces, and other control stimuli. By presenting a large number of control stimuli of various types, Kanwisher et al. ruled out many confounding variables and infer that the brain area they studied, which they called the fusiform face area (FFA), was specifically activated by the perception of faces. Though the interpretation of these results as evidencing a face-selective module

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in the cortex is still being debated, this line of research is an excellent example of using multiple control conditions to rule out alternative hypotheses for the cause of activation of a region. The fact that the ultimate implications for neuroscience are debated is a testament to the difficulty of conceptualizing and ruling out all the plausible confounds, and of making reverse inferences in general. A natural way of making comparisons using multiple control conditions is to use conjunction analysis, which is a logical “and” operator across multiple contrasts. You might want to identify voxels active in a [task A – task B] contrast and in a [task A – task C] contrast. In general, this question is approached by first calculating a statistical map for each contrast of interest, and then selecting those voxels that meet a chosen statistical threshold in both (or all) maps. In effect, the minimum statistic is compared with the conjunction null hypothesis, which specifies that all the contrasts must have significant effects for the conjunction to hold (T. Nichols, Brett, Andersson, Wager, & Poline, 2005). This logic holds generally for all kinds of conjunctions, for example, [A-B] and [C-D] and [E-F], whether or not they are independent. Care must be taken when considering the selection of a significance threshold for a conjunction of contrasts [A-B] and [A-C]: Earlier versions of conjunction analysis in SPM99 and SPM2 software (Price & Friston, 1997) tested the global null hypothesis that none of the effects are truly present. Rejecting this hypothesis implies a true effect in at least one contrast, which is actually an “or” rule: a significant conjunction result in this case implies true activation for contrast [A-B] or contrast [A-C] (T. Nichols et al., 2005). The current version of SPM offers the user a choice of which null hypothesis to test and also offers a range of intermediate alternatives, for example, the hypothesis that 2 or fewer of a series of contrasts have true effects (Friston, Penny, & Glaser, 2005). Unlike the other tests described, this hypothesis requires the assumption of independence among the contrasts, which is clearly violated in our example conjunction with two control conditions [A-B] and [A-C] because they share a baseline. Overall, if you want to test for the intersection (logical and) of multiple effects, then the conjunction null is the proper null hypothesis. In reporting results, the precise procedures and null hypothesis should always be stated; as with other aspects of data analysis, it is not sufficient to merely state that you performed a conjunction analysis with a particular software package. A Note on Baselines Whether a task produces activation or deactivation depends on the baseline condition with which it is compared. Over the past decade or so, Raichle and colleagues have argued for the idea that a quiet resting

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state provides a natural baseline condition against which to evaluate task-related activation (Gusnard, Raichle, & Raichle, 2001; Raichle et al., 2001). A source of support is that the oxygen extraction fraction, the ratio of oxygen use to oxygen supplied by blood, is relatively constant across the resting brain. The argument is that this ratio is one that we are equipped to maintain over long time periods, so it provides a natural physiological baseline. Due in large part to the evidence that Raichle has garnered, many researchers compare tasks with an open-eyed fixation or closedeye resting baseline condition. The intertrial intervals in an event-related design, if enough rest and temporal jitter are provided, can provide an estimate of task-evoked activation relative to baseline activity (though the baseline level itself cannot be quantified with BOLD fMRI); however, tasks may also elicit sustained activity during the intertrial intervals (Visscher et al., 2003). Others argue that the baseline state is just another type of cognitive state, albeit one that is poorly experimentally controlled or characterized. Stark and Squire (2001) found that activity in the medial temporal lobes was substantially higher during rest than during some low-level cognitive tasks. Whether a task of interest activated or deactivated the medial temporal lobes depended on the choice of baseline, begging the question of exactly what kind of mnemonic or other cognitive activity is happening during “rest.” Thus, some researchers choose to compare tasks of interest with low-level baseline tasks during which mental activity can be more precisely experimentally controlled (Johnson et al., 2005). Ultimately, the comparison between task states, including rest, is a comparison of activity evoked by different kinds of mental representations. These comparisons can only be psychologically meaningful if the mental processes involved in each task can be specified. This does not preclude the resting state as a baseline condition of interest. Proponents of the baseline state recognize it as an active state, and theories of mental activity during rest include simulation of situations, contingencies, and associated thoughts and feelings generally focused on the self (and likely involving memory retrieval and medial temporal lobe activation; Gusnard et al., 2001). Each investigator must consider these issues in relation to the particular goals of the study when designing the tasks and comparisons.

switches among objects. This design is a simple 2 ⫻ 2 factorial, with two types of trials (switch versus nonswitch) crossed with two types of judgments (object/attribute). This design permits the testing of three contrasts: (1) a main effect of switch versus nonswitch; (2) a main effect of task type; and (3) the interaction between the two, which tests whether the switch versus nonswitch difference is larger for one task-type than the other. Factors whose measurements and statistical comparisons are made within subjects, such as those described previously, are withinsubjects factors; and those whose levels contain data from different individuals (e.g., depressed patients versus controls) are between-subjects factors. Within-subjects factors generally offer substantially more power and have fewer confounding issues (e.g., differences in brain structure and HRF shapes) than between-subjects factors. Factorial designs allow us to investigate the effects of several variables on brain activations. They also permit a more detailed characterization of the range of processes that activate a particular brain region (e.g., attention switching in general, or switching more for one tasktype than the other. Factorial designs also permit us to discover double dissociations of functions within a single experiment. In our example (Figure 9.4), a factorial design was required to infer that a manipulation (e.g., object-switching) affected dorsolateral prefrontal cortex, but a second manipulation (e.g., attribute switching) did not. Factorial designs can also be used to test for violations of the critical assumption of pure insertion, and for a number of other processes. If the baseline process (e.g., task difficulty) can be manipulated independently of the target process (task-switching requirement), then researchers can test for interactions between task difficulty and switching, and test the notion that the switch process produces an additive increase in activation beyond the processes involved in the basic task.

Factorial Designs

PET and fMRI studies yield data in a format that requires substantial preprocessing before statistical analysis and inference can be performed in a valid and optimal way. The goals of preprocessing are (a) to minimize the influence of data acquisition and physiological artifacts; (b) to check statistical assumptions and transform data to meet the assumptions; (c) to standardize the locations of brain

Another extension of subtraction logic is the factorial design. The study of task switching presented in the introduction to this chapter serves as an example (T. D. Wager, Jonides, et al., 2005). A subset of conditions in the study compared switch versus nonswitch trials for each of two different types: switches among object attributes and

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DATA ANALYSIS: IMPLEMENTATION Data Preprocessing Artifacts, Assumptions, and the Need for Preprocessing

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Data Analysis: Implementation

regions across subjects to achieve validity and sensitivity in group analysis. Most analyses are based, first, on the assumption that all the voxels in any given image were acquired at the same time. Second, it is assumed that each data point in the time series from a given voxel was collected from that voxel only (the participant did not move in between measurements). Third, it is assumed that the residual variance will be constant over time and have a white noise distribution. Additionally, when performing group analysis and making population inference, all individual brains are assumed to be in register, so that each voxel is located in the same anatomical region for all subjects. Without any preprocessing, none of these assumptions hold and statistical analysis would not yield valid or interpretable results. In addition, as noted earlier (see Limitations of PET and fMRI: Acquisition Artifacts in this chapter), neuroimaging data contain artifacts that arise from many sources, including head movement, brain movement, and vascular effects related to periodic physiological fluctuations, and reconstruction and interpolation processes. fMRI data in particular often contain transient spike artifacts and slow drift over time related to a variety of sources, including

magnetic gradient instability, RF interference, and movement-induced inhomogeneities in the magnetic field. An example of transient artifacts as visualized in AFNI is shown in Figure 9.10. Spikes in the data during isolated volume acquisitions are apparent in some entire slices but not others, as shown by the bright bands in the sagittal slices at the bottom of Figure 9.10. This pattern suggests that gradient performance was affected during acquisition of some echo-planar images, which were acquired slice by slice in interleaved order in this experiment. These artifacts likely constitute a violation of the assumptions of normally and identically distributed errors; unless they are dealt with, the consequences include reduced power in group analysis and potentially increased false positives in single-subject inference. A first line of defense is, as with any kind of data analysis, to examine the data—in as raw a form as possible—and diagnose problems. This can be challenging given the massive proportions of neuroimaging data, and different packages provide different ways of looking at the data. As shown in Figure 9.10, AFNI provides an excellent facility for viewing time courses and images from one or more voxels (see Table 9.3 for a list of packages and web sites). Spike artifacts are often identified and problematic images

Figure 9.10 Transient spike artifacts as visualized in the software package AFNI.

This suggests that gradient performance was affected during acquisition of some echo-planar images, which were acquired slice by slice in interleaved order in this experiment.

Note: Spikes in the data during isolated volume acquisitions are apparent in certain slices, as shown by the bright bands in the sagittal slices (bottom).

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removed prior to or in the course of analysis, or minimized using trimming procedures, as in FIASCO software. VoxBo software also has good data-surfing capabilities. A popular approach implemented in FSL, FMRISTAT, and specialized packages such as GIFT (see later in this chapter) is to extract principal components or independent components from the whole-brain time series and visualize them. These components are increasingly used for artifact removal (Nakamura et al., 2006; Tohka et al., 2007), though if single-subject inference is desired, care must be taken not to bias the results by removing variance from the data without accounting for it in the statistical analysis. Apart from using the procedures described here, the effects of slow drift, the problem of intersubject registration, and some other artifacts can be minimized using preprocessing and analysis techniques to be described. In the rest of this chapter, we focus on fMRI analysis and briefly describe common preprocessing steps. Other neuroimaging methods, including PET, require different steps than those described here. Preprocessing Steps for Functional Magnetic Resonance Imaging The major steps in fMRI preprocessing are reconstruction, slice acquisition timing correction, realignment, coregistration of structural and functional images, registration or nonlinear warping to a template (also called normalization), and smoothing. Single-subject analyses do not require the warping step, which introduce spatial uncertainty in terms of anatomical locations and thus can provide much higher anatomical resolution. Group studies largely preclude false positives due to fMRI time series artifacts and permit population inference. Some group studies do not employ smoothing to increase spatial resolution. Reconstruction Images must be first reconstructed from the raw MR signal. Raw and reconstructed data are stored in a variety of formats, but reconstructed images are generally composed of a 3-D matrix of data, containing the signal intensity at each voxel or cube of brain tissue sampled in an evenly spaced grid, and a header that contains information about the dimensionality, voxel size, and other image parameters. A popular format is Analyze, also known as AVW, which uses a separate header file and image file for each brain volume acquired. Other formats, such as NIFTI, are also gaining popularity. A series of images describes the pattern of activity over the course of the experiment. It is also common to store images in a 4-D matrix, where the fourth dimension is time. Slice Timing Statistical analysis using a single hemodynamic reference function assumes that all the voxels in

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an image are acquired simultaneously. In reality, the data from different slices are shifted in time relative to each other because most BOLD pulse sequences collect data slice by slice—some slices are collected later during the volume acquisition than others. Thus, we need to estimate the signal intensity in all voxels at the same moment in the acquisition period. This can be done by interpolating the signal intensity at the chosen time point from the same voxel in previous and subsequent acquisitions. A number of interpolation techniques exist, from bilinear to sinc interpolations, with varying degrees of accuracy and speed. Sinc interpolation is the slowest, but generally the most accurate. Some researchers do not use slice timing, as it adds interpolation error to the data, and instead use more flexible hemodynamic models to account for variations in acquisition time. Realignment A major problem in most time-series experiments is movement of the subject’s head during acquisition of the time series. When this happens, the image voxels’ signal intensity gets contaminated by the signal from its neighbors. Thus, we must rotate and translate each individual image to compensate for the subject’s movements. Realignment is typically performed by choosing a reference image (popular choices are the first image or the mean image) and using a rigid body transformation of all the other images in the time series to match it, which allows the image to be translated (shifted in the x, y, and z directions) and rotated (altered roll, pitch, and yaw) to match the reference. The transformation can be expressed as a premultiplication of the “target” image spatial coordinates to be altered by a 3 ⫻ 3 affine matrix. The elements of this matrix are parameters to be estimated, and an iterative algorithm is used to search for the parameter estimates that provide the best match between a target image and the reference image. Usually, the matching process is done by minimizing sums of squared differences between the two images. Realignment corrects adequately for small movements of the head, but it does not correct for the more complex spinhistory artifacts created by the motion. The parameters at each time point are saved for later inspection and are often included in the analysis as covariates of no interest; however, even this additional step does not completely remove the artifacts created by head motion. Residual artifacts remain in the data and contribute to noise. Sometimes this noise is correlated with task contrasts of interest, which poses a problem, and can create false results in single-subject analyses. However, because these artifacts are expected to (and typically do) differ in sign and magnitude across subjects, group analysis is valid. Group analyses are usually robust to such artifacts in terms of false positives, but power can be severely compromised if large movement artifacts are present.

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Data Analysis: Implementation

Because of these issues, it is typical to exclude subjects that move their heads substantially during the scan. Subject motion in each of the 6 directions can be estimated using the magnitudes of the transformation required for each image during the realignment process, and time series of displacements are standard output for realignment algorithms. There are no hard-and-fast rules for how much movement is too much, but more than 1.5 mm displacement within a scanning session (while the scanner is running continuously) is typically considered problematic, and can usually be avoided with proper instructions to subjects and head restraints. Warping to Atlas (Normalization) For group analysis, each voxel must lie within the same brain structure in each individual subject. Individual brains have different shapes and features, but there are regularities shared by every nonpathological brain, and normalization attempts to register each subject’s anatomy with a standardized atlas space defined by a template brain (see Figure 9.11). Normalization can be linear, involving simple registration of the gross shape of the brain, or nonlinear, involving warping to match local features. In intensity-based normalization, matching is done using image intensities corresponding to gray/white matter/fluid tissue classes. Surface-based normalization uses extracted features such as gyral and sulcal boundaries explicitly (see Thresholding and Multiple Comparisons, earlier in this chapter). Here, we describe nonlinear intensity-based normalization as implemented in SPM software. Whereas the realignment and co-registration procedures perform a rigid body rotation, normalization can stretch and shrink different regions of the image to achieve the closest match. This warping consists of shifting the locations of pixels by different amounts depending on their original location. The function that describes how much to shift the voxels is unknown, but can be described by a set of cosine basis functions. The task is then to search for a set of coefficients (weights of each basis function) that minimize the least squares difference between the transformed image and the template. How closely the algorithm attempts to match the local features of the template depends on the number and spatial frequency of basis functions used. Often, warping that is too flexible (using many basis functions) can produce gross distortions in the brain, as local features are matched at the expense of getting the right overall shape, as shown in Figure 9.11B. This happens essentially because the problem space is too complex, and the algorithm can settle into a “local minimum” solution that is not close to the global optimal solution. Surface-based warping uses similar principles, but matches features on extracted cortical surface representations instead of image intensities.

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Intersubject registration is one of the largest sources of error in group analysis. Thus, it is important to inspect each normalized brain and, if necessary, take remedial measures. These include manually improving the initial alignment, using a mask to exclude problematic regions of atrophy or abnormality (e.g., a lesion), altering the number of basis functions and other fitting parameters, and in some cases developing specialized template brains (e.g., for children). Figure 9.11C shows a process of checking normalization for one subject. We have identified control points on the MNI ICBM152 template brain (left) that correspond to easily identifiable features. Then, we have taken those points and overlaid them on the subject’s normalized T1 image. For this subject, unlike the pathological case in Figure 9.11B, each of the control points matches the corresponding anatomical feature on the subject’s brain quite well. Such checking can be done in several ways, and though there are no hardand-fast rules for how to check and how much error is too much, each lab should develop a set of standardized procedures. Smoothing Many investigators apply a spatial smoothing kernel to the functional data, blurring the image intensities in space. This is ironic, given the push for higher spatial resolutions and smaller voxels—so why does anyone do it? One reason is to improve intersubject registration. A second reason is that Gaussian random field theory, a popular multiple-comparisons correction procedure, assumes that the variations across space are continuous and normally distributed. However, images are sampled on a grid of voxels, and neither assumption is likely to hold; smoothing can help meet these assumptions. Smoothing typically involves convolution with a Gaussian kernel, which is a 3-D normal probability density function often described by the full width of the kernel at half its maximum height (FWHM) in mm. One estimate of the smoothing required to meet the assumption is a FWHM of 3 times the voxel size (e.g., 9 mm for 3 mm voxels). An important consideration is that acquiring an image with large voxels and acquiring it with small voxels and smoothing an image are not the same thing. The signal-tonoise ratio during acquisition increases as the square of the voxel volume, so acquiring small voxels means that much signal is lost that can never be recovered. It is optimal in terms of sensitivity to acquire images at the desired resolution and not employ smoothing. Some recent acquisition schemes acquire images at the final functional resolution desired, which also permits much more rapid image acquisition as time is not spent acquiring information that would be discarded in analysis (M. Lindquist, Glover, & Shepp, in press).

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Figure 9.11 Normalization attempts to register each subject’s anatomy with a standardized template brain using an intensitybased warping procedure. Note: A: A schematic overview of the warping process. High resolution T1 images are warped onto a T1 template to give normalized images in a standard space. B: Incorrect warping can produce gross distortions in

Previously, many investigators applied temporal smoothing to the data as well as spatial smoothing. This procedure is another form of filtering like the high-pass filtering done in the course of model estimation; it removes high-frequency signals from the data, whereas high-pass filtering removes low-frequency signals. This procedure was implemented in SPM99 software (Table 9.3) primarily to facilitate accurate estimation of the degrees of freedom, which was assumed after smoothing to equal that implied by the kernel. This approach has largely been replaced by more standard time series models (e.g., autoregressive modeling). There is no expected benefit to temporal smoothing on sensitivity, as it further decreases the temporal resolution of the data, and it is not recommended. Co-registration Often, high-resolution structural images (T1 and/or T2) are used for warping and localization.

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the brain, as local features are matched at the expense of getting the correct overall shape. C: The normalization procedure can be checked by identifying control points on the MNI ICBM152 template brain (left) that correspond to easily identifiable features and overlaying them on the subject’s normalized T1 image. For this subject, each of the control points matches well with the corresponding anatomical feature.

The same transformations (warps) are applied to the functional images, which produce the activation statistics, so accurate registration of structural and functional images is critical. Co-registration aligns structural and functional images, or in general, different types of images of the same brain. Because functional and structural images are collected with different sequences and different tissue classes have different average intensities, using a least squares difference method to match images is often not appropriate. The signal intensity in gray matter (G), white matter (W), and ventricles are ordered W > G > V in functional T2* images, and V > G > W in structural T2 images (Figure 9.1). In such cases, an affine transformation matrix can be estimated by maximizing the mutual information among the two images, or the degree that knowing the intensity of one can be used to predict the intensity of the other (Cover & Thomas, 1991). Typically, a single structural image is co-registered to the first or mean functional image.

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Data Analysis: Implementation

Localizing Task-Related Activations with the GLM The GLM is the most common statistical method for assessing task–brain activity relationships in neuroimaging (Worsley & Friston, 1995). GLM is a linear analysis method that subsumes many basic analysis techniques, including t-tests, ANOVA, and multiple regression. The GLM can be used to estimate whether the brain responds to a single type of event, to compare different types of events, to assess correlations between brain activity and behavioral performance or other psychological variables, and for other tests. The GLM is appropriate when multiple predictor variables—which together constitute a simplified model of the sources of variability in a set of data—are used to explain variability in a single, continuously distributed outcome variable. In a typical neuroimaging experiment, the predictors are related to psychological events, and the outcome variable is signal in a brain voxel or region of interest. Analysis is typically “massively univariate,” meaning that the analyst performs a separate GLM analysis at every voxel in the brain, and summary statistics are saved in maps of statistic values across the brain. Because of the hierarchical structure of the data, an appropriate analysis for multisubject PET and fMRI studies is the mixed-effects GLM model. This is often approximated by performing a GLM model for each subject and using the resulting activation parameter estimates in a second-level group analysis. We refer to this as the unweighted summary statistic approach. FSL software currently performs a mixed-effects analysis, whereas the most typical analysis in SPM, AFNI, BrainVoyager, VoxBo, and other packages is the unweighted summary statistic approach. We describe the mechanics of a single-subject analysis and then the mixed-effects approach in the following subsections. Single-Subject GLM Model Basics For a single subject, the fMRI time course or series of PET values from one voxel is the outcome variable (y). Activity is modeled as the sum of a series of independent predictors (x variables, that is, x1, x2) related to task conditions and other nuisance covariates of no interest (e.g., head movement estimates). In fMRI analysis, for each task condition or event type of interest, a time series of the predicted shape of the signal response is constructed, usually using prior information about the shape of the vascular response to a brief impulse of neural activity. The vectors of predicted time series values for each task condition are collated into the columns of the design matrix, X, which contains a row for each of n observations collected (observations over time) and a column for each of k predictors. The GLM

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fitting procedure estimates the best-fitting amplitude (scaling factor) for each column of X, so that the sums of fitted values across predictors best fit the data. These amplitudes are regression slopes, and are denoted with the variable βˆ (the “hat” denotes an estimate of a theoretical constant value). It also estimates a time series of error values, εˆ, that cannot be explained by the model. The model is thus described by the equation: y ⫽ X ␤ ⫹ε

(9.1)

where β is a k⫻ 1 vector of regression slopes, X is an n⫻k model matrix, y is an n⫻ 1 vector containing the observed data, and ε is an n⫻ 1 vector of unexplained error values. The equation is in matrix notation, so that Xβ indicates the rise and fall in the data explained by the model, or the sum of each column of X multiplied by each element of β. Error values are assumed to be independent and to follow a normal distribution with mean 0 and standard deviation σ. The estimated βˆ s correspond to the estimated magnitude of activation for each psychological condition described in the columns of X. One advantage of the GLM is that there exists an algebraic solution for βˆ that minimizes the squared error: T -1 T βˆ ⫽ (X X) X y

(9.2)

where T indicates the transpose operator. Inference is generally conducted by calculating a t-statistic, which equals the βˆ s divided by their standard errors, and obtaining p-values using classical inference. The standard errors of the estimates are the diagonal elements of the matrix: se(βˆ ) ⫽ (X T X)-1σˆ

(9.3)

Notably, the error term comprises two separate terms from different sources. σ is the residual error variance, which depends on many factors, including scanner noise. (XTX)–1 depends on the design matrix itself, and reflects both the variability in the predicted signal and covariance among preditors (multicolinearity). Design optimization algorithms, described earlier, work on minimizing the design-related component of the standard error: (XTX)–1. An important additional feature of the data requires a further extension of the model. fMRI data are autocorrelated—signals are correlated with versions of themselves shifted in time and are not independent—and the autocorrelation must be removed for valid single-subject inference. This is typically done by estimating the autocorrelation in the residuals, after model fitting, and then removing the autocorrelation by “prewhitening.” Prewhitening works

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by premultiplying both sides of the general linear model equation (Equation 9.1) by the square root of a filtering matrix W, that will counteract the autocorrelation structure and create a new design matrix W1/2X and whitened data W1/2y. This process is incorporated into what is known as the generalized least-squares solution, so that: βˆ ⫽ (X T WX)-1X T Wy

(9.4)

Note that the standard errors and degrees of freedom change as well due to the whitening process. Because the estimation of W depends on βˆ , and vice versa, a one-step algebraic solution is not available, and the parameters are estimated using an iterative algorithm. There are many ways of designing W, ranging from estimates that make strong simplifying assumptions about the form of the data, such as the one-parameter autoregressive AR(1) model, to empirical estimates that use many parameters. As with any model-fitting procedure, a trade-off exists between using few and many parameters. Many-parameter models generally produce close fits to the observed data. However, models with few parameters—if they are chosen carefully—can produce more accurate estimates of the underlying true function because they are less susceptible to fitting random noise patterns in the data. Contrasts Contrasts across conditions can be easily handled within the GLM framework. Mathematically, a contrast is a linear combination of predictors. The contrast (e.g., A – B in a simple comparison, or A ⫹ B – C – D for a main effect in a 2 ⫻ 2 factorial design) is coded as a k⫻ 1 vector of contrast weights, which we denote with the letter c. For example, the contrast weights for a simple subtraction is c⫽ [1 – 1]T, while a single contrast for a linear effect across four conditions might be c⫽ [–3 – 1 1 3]T. Concatenating multiple contrasts into a matrix can simultaneously test a whole set. Thus, the main effects and interaction contrasts in a 2 ⫻ 2 factorial design can be specified with the following matrix: C⫽

[1 1 ⫺1 ⫺1

1 ⫺1 1 ⫺1

1 ⫺1 ⫺1 1]

Columns 1 and 2 test main effects, and the third tests their interaction. To test contrast values against a null hypothesis of zero—the most typical inferential procedure—contrast weights must sum to zero. If the weights do not sum to zero, then the contrast values partially reflect overall scanner signal intensity, and the resulting t-statistics are invalid. The analyst must take care to specify

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contrasts correctly, as contrast weights in neuroimaging analysis packages are often specified by the analyst, rather than being created automatically as in SPSS, SAS, and other popular statistical packages. The true contrast values Cβ can be estimated using CTβˆ , where βˆ is obtained using Equation 9.2. The standard errors of each contrast are the diagonals of: se (CT βˆ ) ⫽ CT (XT X)-1 XT Cσˆ

(9.5)

The whitening process is omitted here for simplicity, but can readily be incorporated. Most imaging statistics packages write a series of images to disk containing the betas for each condition throughout the brain, and another set of contrast images containing the values of CTβˆ throughout the brain. Contrast images are typically used in a group analysis. A third set of images contains t-statistics, or the ratio of contrast estimates to their standard errors. Assumptions The model-fitting procedure assumes that the effects due to each of the predictors add linearly and do not change over time (the system is linear and timeinvariant). The inferential process assumes that the observations are independent, that they all come from the same distribution, and that the residuals are distributed normally and with equal variance across the range of predicted values. All these assumptions are violated to a degree in at least some brain regions in a typical imaging experiment, which has prompted the development of important extensions, including diagnostic tools and robust model-fitting procedures (Loh, in press; Luo & Nichols, 2003; T. D. Wager, Keller, et al., 2005). Violations of the assumptions are not merely a theoretical nuisance. They can make the difference between a valid finding and a false positive result, or between finding meaningful activations in the brain and wasting substantial time and money. Diagnostic tools have been developed for exploring the data, looking for artifacts, and checking a number of assumptions about the data and model (Loh, 2008; Luo & Nichols, 2003), and like many tools developed by members of the neuroimaging community, they are freely available on the Internet. The quantity of data(e.g.,100,000 separate regressions on 1,000 data points per subject ⫻ 20 subjects) and the software and data structures that support its analysis make it difficult to examine assumptions and check the data, which makes such diagnostic tools all the more important. Another active area of research concerns strategies for dealing with some known violations of assumptions, described later. Violations of independence can be handled in a limited way using generalized least squares. Violations of equality and normality can be dealt with by using

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Data Analysis: Implementation

nonparametric permutation tests to make statistical inferences (Nichols & Holmes, 2002), or, if they result from the presence of outliers, by robust regression techniques (T. D. Wager, Keller, et al., 2005). Free implementations of each of these extensions are available (Table 9.3). GLM Model-Building in fMRI Perhaps the most challenging task in linear regression analysis is the creation of realistic predictions of taskrelated signals for the columns of X. PET images integrate across many psychological events, obviating the need for accurate models but also limiting the specificity with which activation can be linked to specific events or time periods. As discussed earlier, a popular method of forming predicted BOLD time series is to use a canonical HRF. The process is shown in Figure 9.9. To build the model, researchers start with an indicator vector representing the neuronal activity for each condition sampled at the resolution of the fMRI experiment, shown at the left of Figure 9.9 for four hypothetical event types (A – D). This vector has zero value except during hypothesized neural activation periods, when the signal is assigned a value of 1. Each indicator vector is convolved with the HRF to yield a predicted time course related to that event, which forms a column of the X. The rightmost panel shows X in image form, a common format for presentation in papers. If the canonical HRF fits the shape of the BOLD response to psychological events, then using the canonical HRF simplifies the analysis and has great sensitivity to detect differences. Consider two psychological events A and B that both activate a voxel, but with different amplitudes, as shown in the top left panel of Figure 9.12. Empirical time courses are shown in light lines, and the fitted responses (model fits) with the canonical HRF are shown in dark lines. The [A – B] contrast will appropriately reflect the different response amplitudes. The canonical HRF is a double-edged sword. If the canonical HRF does not fit, there is at best a drop in power, and at worst false positives and misinterpretation of results (Lindquist & Wager, 2007). Consider an example in which two conditions A and B produce responses of equivalent amplitude, but at different delays. This is shown in the top center panel of Figure 9.12, where the response to B is delayed by 3 s. Since the HRF shape is fixed, any difference in model fits will produce a difference in the only free parameter, amplitude. In this example, the estimated amplitude for A will be greater than for B. Without additional diagnostic tests, one might falsely infer that A activates the brain region more than B. This example illustrates the importance of visualizing the data and fits, rather than simply interpreting a statistically significant result at face value.

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Comparing groups of individuals (e.g., older versus younger adults, or patients and normal controls) can be especially problematic. If you find [A – B] amplitude differences, are those differences caused by differences in neural activity amplitude or the timing and shape of the vascular component of the BOLD response? Elderly subjects have reduced and more variable shapes of their HRFs compared with younger subjects (D’Esposito, Zarahn, Aguirre, & Rypma, 1999), making direct comparisons with a canonical HRF problematic. Alternate approaches include (a) measuring HRFs in visual and motor cortex for each individual subject using a separate task (Aguirre et al., 1998) or (b) using a more flexible model of the HRF by using a basis set. Basis Sets In the previous discussion, conditions are modeled by a single linear regressor that allows us to estimate only the amplitude of the predicted response (βˆ ) or contrast (CTβˆ ). Alternatively, the same neural indicator vector can be convolved with multiple canonical waveforms and entered into multiple columns of X for a single event type. These reference waveforms are basis functions, and the predictors for an event type constructed using different basis functions can combine linearly to better fit the evoked BOLD responses. An example is shown in the second row of Figure 9.12, in which a linear combination of the canonical HRF and its temporal derivative provide better fits to responses that look similar to the HRF (left panel), are shifted in time (center panel), or have extended activation durations (right panel). This basis set is the most popular current alternative to the canonical HRF alone among users of SPM software (Friston, Glaser, et al., 2002; Friston, Josephs, Rees, & Turner, 1998). Notice that the fits are better, but changes in delay and duration are far from perfectly modeled. The ability of a basis set to capture variations in hemodynamic responses such as those depicted in Figure 9.12 depends on both the number and shape of the reference waveforms. There is a fundamental trade-off between flexibility to model variations and power. This is because each parameter is estimated with error, and flexible models can tend to model noise and thus produce noisier parameter estimates. One of the most flexible models, a finite impulse response (FIR) basis set, contains one free parameter for every time point following stimulation in every cognitive event type that is modeled (Glover, 1999; Goutte, Nielsen, & Hansen, 2000; Ollinger, Shulman, & Corbetta, 2001). Using such a model makes minimal assumptions about the shape of the HRF because the βˆ s estimate the average response at each time point following the onset of an event. The FIR model is a preferred way to estimate and visualize

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Essentials of Functional Neuroimaging Basis functions True amplitude diff. [A - B] A

True delay diff. [A - B] A

True duration diff. [A - B]

B

Canonical HRF B

Canonical HRF ⫹ temporal derivative

Smooth FIR

Inverse Logit Model

Peri-stimulus Time

Figure 9.12 Basis sets differ in the flexibility they provide in modeling different HRF shapes. Note: Each column in the figure shows HRF estimates for an experiment where two conditions A and B produce responses of: (left) different amplitudes; (center) equivalent amplitude, but at different delays; and (right) equivalent amplitude, but different durations. The ability of four different basis sets to estimate the hemodynamic response function (HRF)

the shape of BOLD responses, and it is implemented in major software packages including AFNI, SPM, and FSL. An example of model fits using a smooth FIR model, which is constrained to produce smooth response functions, is shown in the third row of Figure 9.12. The model fits (dark black lines) fit the data reasonably accurately in all conditions, including those shifted in time (center) and extended in duration (right). Other choices of basis sets include those composed of principal components (Aguirre et al., 1998; Woolrich, Behrens, & Smith, 2004), cosine functions (Zarahn, 2002), radial basis functions (Riera et al., 2004), spectral basis sets (Liao et al., 2002), and other functions. The bottom row in Figure 9.12 shows fitted responses from a basis set recently developed in our lab that uses three superimposed inverse logit functions to model the rise, fall, and undershoot of the BOLD response (Lindquist & Wager, 2007). The model can handle both delays and variations in duration, making a single model appropriate for both brief events and prolonged epochs of stimulation. In addition, fits are as accurate as the FIR model fits for these data, and simulations showed that the model compares favorably with a range of other models in terms of statistical power. The model is freely available (see Table 9.3). Basis sets offer a major advantage—more accurate modeling of the HRF across subjects and across the brain—but they pose additional technical difficulties that make their use less common than perhaps it should be. First, it is not

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corresponding to each condition is shown in the four rows. The basis sets that were used are SPM software’s canonical HRF, the canonical HRF ⫹ its temporal derivative, a smooth finite impulse response (FIR) model and the inverse logit model. From “Validity and Power in Hemodynamic Response Modeling: A Comparison Study and a New Approach,” by M. A. Lindquist and T. D. Wager, 2007, Human Brain Mapping, 28, p. 776. Adapted with permission.

straightforward to calculate contrasts across conditions when there are multiple parameter estimates per condition. Leaving out some basis functions when calculating contrasts, though it is often done, is not generally advised. An alternative is to calculate one contrast per basis function for each contrast of interest. Group analysis can then be done using repeated measures analyses at the second level (in group analysis) rather than the usual one-sample t-test. However, there is a cost in power when basis functions are added, and in general whenever more parameter estimates are compared. Physiological Noise and Covariates of No Interest In both PET and fMRI designs, additional predictors are typically added to account for known sources of noise in the data. These nuisance covariates are included to reduce noise and to prevent signal changes related to head movement and physiological (e.g., respiration) artifacts from influencing the contrast estimates. In addition, covariates that implement high-pass filtering, or removal of signal frequencies below a specified cutoff, can also be added at this stage; this is the standard approach in SPM software. In PET, a common covariate is the global (whole-brain) mean signal value for each subject, included to control for differences in amount of radioactive tracer in circulation. In fMRI, the signal typically drifts slowly over time, so that the most power is in the lowest temporal frequencies. This characteristic has prompted the widespread use of

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Data Analysis: Implementation

high-pass filters that remove fluctuations below a specified frequency cutoff from the data. High-pass filtering is often performed in the GLM analysis by adding covariates of no interest (e.g., low-frequency cosines). Of course, care must be taken to ensure that the fluctuations induced by the task design are not in the range of frequencies removed by the filter. Design optimization algorithms can take this into account when constructing trial sequences (T. D. Wager & Nichols, 2003). Much of the autocorrelated noise and other noise variance in fMRI may come from aliased physiological artifacts (Lund, Madsen, Sidaros, Luo, & Nichols, 2006). Thus, it is increasingly popular to measure heartbeat and respiration during scanning and to use preprocessing algorithms for removing signals related to measured physiological fluctuations from the data prior to analysis (Glover, Li, & Ress, 2000). Programs for doing this are typically available from authors of research articles, but have not yet been incorporated as standard tools in neuroimaging analysis packages. Group Analysis The analysis described so far has been, for fMRI datasets, an analysis of data from a single subject. However, researchers are often interested in making inferences about a population, not just about a single subject or even a set of individual subjects, which requires a group analysis. Both PET and fMRI studies nearly always involve collecting more than one image per subject, and testing for the significance of effects in a group of subjects. In fMRI, typically, separate GLM analyses are conducted on the time series data for each subject at each voxel in the brain to estimate the magnitude of activation evoked by the task. This is called a “first level” analysis. These estimates are carried forward and tested for reliability across subjects in a “second level” group analysis. In PET, the first level analysis often consists of simple image subtractions, followed by the same type of second level analysis as for fMRI. The unweighted summary statistics approach referred to earlier consists of a simple one-sample t-test across contrast estimates for each subject. This analysis, like others discussed so far, is repeated at each voxel. It can be specified in the GLM framework, so Equations. 9.1 to 9.3 hold, and independence is typically assumed across subjects so no prewhitening is needed. The one-sample t-test for overall activation corresponds to a test of the model intercept in a GLM model. Additional covariates across subjects (e.g., average performance scores) can be specified and tested in simple or multiple regression. Two-sample and ANOVA designs to compare groups and related GLM variants can also be specified. Including covariates can improve statistical power for the test of overall activation, though

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care must be taken: The significance of the intercept can only be assessed if all other covariates are transformed to have a mean of zero. The unweighted summary statistic approach is valid if the contrast standard error is the same across all subjects, which implies identical design matrices and residual variances. This is rarely if ever true in practice, though the cost is mostly in the statistical power of the analysis and it is still widely used. Full mixed-effects models relax those stringent assumptions by considering the standard errors within each subject as well as contrast estimates. Mixedeffects analyses are standard in FSL and FMRISTAT software (see Mixed versus Fixed Effects, earlier in this chapter, and Table 9.3). Mixed-effects analyses essentially weight subjects when calculating group statistics. The larger a subject’s standard error, the less reliable their estimate, and the less that subject should contribute to the group results. This requires estimating variance components: One component is variance related to within-subject measurement error and model misfitting (σ2W), and another component is variance related to true interindividual differences among subjects (σ2B). Accurate estimation of the relative contribution of error within- and between-subjects allows for appropriate weighting. Restricted maximum likelihood (ReML) is a popular estimate of variance components based on the residuals. Since variance estimates and model fits (βˆ s) are interdependent, iterative algorithms such as EM are used to estimate ReML variance components. Statistical Power and Sample Size Statistical power depends on having either a large effect size (high contrast values) or a small standard error. The standard error in a group analysis is determined by both σ2W and σ2B. At the group level, σ2B can be reduced and power increased by increasing the sample size, more accurate normalization or more informed ROI selection, and increased control of strategies used and individual psychological responses to the task. σ2W can be reduced by improving modeling procedures and reducing acquisitionrelated scanner noise and physiological noise. A key question when designing a group study is determining an adequate sample size. The answer to this question depends on the effect size in the group, the amount of scanner noise and signal optimization, and it will be different for each task and each brain voxel (Desmond & Glover, 2002; Zarahn & Slifstein, 2001). Power analysis is difficult in fMRI because power depends on so many factors relating to psychology, task design and analysis, and hardware; however, by referring to standard effect sizes, you can obtain estimates of what sample sizes are needed in a group analysis.

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Figure 9.13 shows plots of power (y-axes) as a function of sample size (x-axes) for three effect sizes in two kinds of analysis. The effect sizes are Cohen’s d values, which is defined as mean activation magnitude divided by its standard deviation, for a simple one-sample t-test in group analysis. In behavioral sciences, d⫽ 0.3, 0.5, and 1 are considered small-, medium-, and large-effect sizes, respectively. Most activations reported in neuroimaging have effect sizes that are substantially larger—d⫽ 2 or more. However, this is partly because voxel-wise mapping capitalizes on chance due to selection bias: Voxels in which chance favors the evidence for activation have large effect sizes and tend to be reported. Whereas observed effect sizes in published reports are usually overestimated due to selection bias, the problem is exacerbated when many tests are performed. We show power curves here for effect sizes of 0.5, 1, and 2. Figure 9.13A shows results for a whole-brain search with 200,000 voxels, a typical number depending on acquisition and analysis choices, and FWE correction at p < .05 using the Bonferroni method. To achieve 80% power with a reasonable sample size, the effect size must be larger than 0.5, and around 40 subjects are required for d⫽ 1 and 18 subjects for d⫽ 2. Figure 9.13B shows the same results using nonparametric permutation testing, which takes into account the spatial smoothness in the data. We used nonparametric thresholds from 10 analyses from various studies reported in Nichols & Hayasaka, (2003) to estimate the effective number of independent

(A)

comparisons and thus power. With nonparametric analysis, around 25 subjects for d⫽ 1 and 11 subjects for d⫽ 2 provides 80% power. Design optimization procedures can be employed before data is ever collected to increase the effect size. For a fixed effect size and sample size, power depends on the within-subject standard error (se(CTβ)), which depends on both the design matrix, X, and the residual standard deviation, σ (Equation 9.5). The latter can be reduced by optimizing data collection (e.g., pulse sequences and hardware) and in the study design by ensuring the engagement of subjects in the tasks. Error related to X can be minimized during experimental design by carefully choosing the number, sequence, and spacing of events to minimize the design-related component of the standard error, CT (XTX)–1C. Effective minimization increases predictor variance and reduces predictor covariance (multicolinearity), and is particularly critical in event-related fMRI. It is possible to build an event-related fMRI design in which even large neuronal effects cannot be detected. For this reason, computer-aided design optimization can be very useful (Buracas & Boynton, 2002; T. D. Wager & Nichols, 2003). Finally, both theory and simulations show that there is a substantial trade-off in power between detecting activation differences between conditions using an assumed HRF shape and estimating the shape of evoked activations with a more flexible model (Liu, Frank, Wong, & Buxton,

(B) Power with nonparametric thresholding

Power with Bonferroni correction 1

1 d2 Power at p  05 corrected

Power at p  05 corrected

d2 0.8 d1 0.6

0.4 d  0.5

0.2

0

0

20

40 Sample Size

60

80

Figure 9.13 A: Power curves—calculated for effect sizes of 0.5, 1, and 2—for a whole-brain search with 200,000 voxels and FWE correction at p < .05 using the Bonferroni method. Note: The number of voxels would be typical of a whole-brain search through gray and white matter with a 2 ⫻ 2 ⫻ 2 mm sampling resolution.

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0.8 d1 0.6

0.4 d  0.5

0.2

0

0

20

40 Sample Size

60

80

B: The same power curves calculated based on the results of nonparametric permutation testing, which takes into account the spatial smoothness in the data. Based on the smoothness reported in Nichols and Hayasaka (2003) for 10 different statistic maps, we calculated an average of ˜750 effective independent comparisons. Correction across this number of comparisons was used in calculating power.

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Data Analysis: Implementation

2001). This trade-off is shown in Figure 9.14, in which shape-estimation power is shown on the x-axis and contrast-detection power is shown on the y-axis. The points in the model represent designs with different sequences and timing of events. Blocked designs have the highest [A – B] contrast detection power when the canonical HRF is used, but provide little information about the shape of the HRF. M-sequences, or sequences that are orthogonal to themselves shifted in time, provide optimal shape estimation power (the nonoptimality in the figure is due to truncation of the m-sequences—so they are not perfect), but low detection power (Buracas & Boynton, 2002). Random event-related designs fall somewhere in between. As the figure shows, designs optimized with a genetic algorithm (T. D. Wager & Nichols, 2003) can produce substantially better results than random designs on both measures. Bayesian Inference Bayesian methods have received a great deal of attention in fMRI literature. These inferential methods are now key 25 Block design 169 s on/off

Approximate theoretical limit

Contrast detection power Cor(1)(z)

20

Optimized designs (GA)

15

Event-related designs

185

components in several major fMRI analysis software packages (e.g., SPM and FSL). A full treatment of Bayesian methods is beyond the scope of this chapter, but an excellent overview can be found in Gelman et al. (2004). A key difference from the frequentist approach discussed previously (which subsumes classical inference in the GLM and its extensions) is that Bayesian analysis combines evidence from the data through priors—beliefs about the data specified as probabilities prior to data collection—to yield posterior probability values. This can be a big advantage in that estimates from data (e.g., of HRF shapes) can be easily regularized based on known information from other studies. Such prior constraints are also possible in frequentist analyses, though they require modifications and special procedures; lasso, ridge regression, and robust regression are examples. If you do not want to impose strong prior beliefs, then it is possible to use noninformative priors, which is implemented in the Bayesian approach in FSL software (Woolrich, Behrens, Beckmann, et al., 2004). For the single-level model, this leads to parameter estimates that are equivalent to those obtained using classical inference. Another way to choose prior beliefs is by estimating them from data. This is the “empirical Bayes” approach. It is a hybrid between classical and Bayesian inference that can provide some regularization without biasing the results of hypothesis tests, and is used in SPM software (Friston, Glaser, et al., 2002; Friston, Penny, et al., 2002).

10

Assessing Brain Connectivity 5 m-sequence designs 0 3.4

3.5

3.6 3.7 3.8 4 3.9 HRF shape estimation power

4.1

4.2

Figure 9.14 The trade-off between contrast detection (y-axis) and hemodynamic response function (HRF) shape estimation power (x-axis), and the performance of different types of designs on each. Note: Power on each axis is expressed here in terms of z-scores in a simulated group analysis (n⫽ 10, effect sizes estimated from visual cortex data in Wager et al., 2005). The double-circle shows a block design with roughly optimal task alternation frequency (16 s/task). The dark circles show power for a number of randomized event-related designs with roughly optimal parameters under linear modeling assumptions (randomized sequences with a stimulus every 2 s). The dark squares show truncated m-sequence designs with the same parameters as the randomized design. The open circles show results for genetic algorithm (GA) optimized designs with the same parameters. Each circle represents the results of one run of the optimization routine with different user-specified detection/shape estimation trade-off settings.

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Human brain mapping has been primarily used to provide maps that show which regions of the brain are activated by specific tasks. There has been an increased interest in augmenting this type of analysis with connectivity studies that describe how various brain regions interact and how these interactions depend on experimental conditions. It is common practice in the analysis of neuroimaging data to make the distinction between functional and effective connectivity (Friston, 1994). Functional connectivity is defined as the undirected association between two or more fMRI time series, while effective connectivity is the directed influence of one brain region on the physiological activity recorded in other brain regions; it implies both causality and directness. It implies causality because the models used to assess effective connectivity are usually directional, and directness in the sense that effective connectivity measures attempt to partial out indirect influences from other regions. Functional connectivity is a statement about observed associations among regions and other performance and physiological variables such as the correlation between time

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series in two regions (bivariate connectivity). Simple functional connectivity analyses usually compare correlations between ROIs, sometimes in a task-dependent fashion, or between a “seed” region of interest and voxels throughout the brain. Multivariate analysis methods are also used to reveal networks of multiple interconnected regions. Popular methods include Principal Components Analysis (PCA; Andersen & Avison, 1999), Partial Least Squares (PLS; McIntosh, Bookstein, Haxby, & Grady, 1996) and Independent Components Analysis (ICA; Calhoun, Adali, Pearlson, & Pekar, 2001a,b; McKeown & Makeig, 1998). Connectivity between two or more regions may result from direct influences (functional links between regions) or indirect effects due to common input from a third variable. None of these methods can address issues of causality or the common influences of other variables. Functional connectivity methods can be applied at different levels of analysis, with different interpretations at each level (see Figure 9.15). Connectivity across time series data can reveal networks that are dynamically coactivated over time (either intrinsically, regardless of task state, or in a task-dependent fashion), and is closest to the concept of communication among regions, though it does Time series/trial levels Region 1 Region 2

␤ˆ 1

Subject 1

not conclusively demonstrate that. Connectivity across single-trial response estimates (Rissman et al., 2004) can identify coherent networks of task-related activations. Whereas these levels are only accessible to fMRI and EEG/MEG, which provide relatively rich time series data, other levels of analysis may be examined in PET studies. Connectivity across subjects can reveal patterns of coherent individual differences, which may result from communication among regions but also from differences in strategy use or other genetically determined or learned differences among individuals. Finally, connectivity across studies can reveal tendencies for studies to coactivate within sets of regions, which may be influenced by any of the factors previously mentioned, and also differences among tasks or other study-level variables. An example is the finding that studies in which posttraumatic stress disorder (PTSD) patients showed increased amygdala activity tended to be the same studies in which patients showed decreased activation of the medial frontal cortex (Etkin & Wager, 2007). Regardless of the level of analysis, functional connectivity analyses can be useful for understanding that brain activations are part of coherent patterns that are separate, independent effects of task manipulations. Subject level Region 1 Region 2

Correlate magnitudes within condition or differences across conditions ␤ˆ ID

␤ˆ 2 Subject 2 ␤ˆ n

Subject n ⫹ ⫹ Artifacts Gradient drift Shot artifacts (spikes) Physiological effects Movement-related artifacts Arousal fluctuations

⫹ ⫹ Trial averaging/ model fitting Individual differences in . . . ⫺ ⫹ Neurovascular coupling differences Time series Trial resp. amplitudes

Figure 9.15 Functional connectivity methods can be applied at different levels of analysis, with different interpretations at each level. Note: (Left) Connectivity across time series data can reveal networks that are dynamically coactivated over time. The solid, dotted, and dashed lines indicate activation time series from three different subjects on the left, and average activation magnitudes for the same subjects (shown by hemodynamic response function [HRF] curves) at the right. Alternatively, measures of single-trial activation amplitude (black dots) can be extracted and used to estimate connectivity, which avoids some ambiguity with respect to the source of connectivity (task-related versus spontaneous).

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Artifacts Hematocrit, CO2 Vascular response (hemodyamic model fit) Gray-matter density Alertness

Brain system recruitment Strategy Performance Genetics

However, artifactual influences can make interpretation of both types of connectivity difficult (see list at the bottom). Distributed artifacts tend to create positive covariance, whereas neurovascular coupling differences —and resulting differences in HRF shapes between regions—tend to weaken covariance estimates. (Right) At the subject level, you can correlate magnitudes within condition or differences across conditions. This analysis is conducted on individual differences, rather than on time series data, and results may have different interpretations than time series connectivity data. Again, special care needs to be taken to limit the influence of artifacts, which are likely to be largely related to factors that create individual differences in model fits across the brain (see list).

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Generally, activation is only informative if it is restricted to specific brain regions (e.g., activation of the insula means little if every other brain region is activated to the same degree). Likewise, demonstrating that connectivity is greater within a set of regions than among other regions (e.g., for the cognitive control network of Cole & Schneider, 2007, or for demonstrating two or more separable sets of interconnected regions such as the multiple separate networks of coherent opioid release reported by Wager, Scott, & Zubieta, 2007) can provide valuable information about how brain regions function together. Demonstrating specificity of functional connectivity to a particular task state, as the psychophysiological interaction (PPI)/moderation analysis to be described later is designed to do, can be informative about how functional connectivity relates to psychological states. Reporting reciprocal activity (negative correlations) between ventromedial PFC and amygdala may be of limited usefulness if such correlations can be found in any task state; in that case, they may be a general feature of BOLD physiology or vasculature rather than an interesting instance of communication among brain regions. In contrast, effective connectivity analysis is modeldependent. Typically, a small set of regions and a proposed set of connections are specified a priori, and tests of fit are used to compare a small number of alternative models and assess the statistical significance of individual connections. Because connections may be specified directionally (with hypothesized causal influences of one area on another), the model implies causal relationships. Because there are many possible models, the choice of regions and connections must be anatomically motivated. Most effective connectivity depends on two models: a neuroanatomical model that describes which areas are connected, and a mathematical model that describes how areas are connected. Common methods include Structural Equation Modeling (SEM; McIntosh & Gonzalez-Lima, 1994) and Dynamic Causal Modeling (DCM; Friston, Harrison, & Penny, 2003). While effective connectivity methods have become increasingly popular, it is important to keep in mind that the conclusions about direct influences and causality obtained using these models are only as good as the specified models. Any misspecification of the underlying model will almost certainly lead to erroneous conclusions. In particular, the exclusion of important lurking variables (brain regions involved in the network but not included in the model) can completely change the fit of the model and thereby affect both the direction and strength of the connections. Great care always needs to be taken when interpreting the results of these methods. The distinction between functional and effective connectivity is not entirely clear (Horwitz, 2003). If the discriminating

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features are (a) a directional model in which causal influences are specified; and (b) the willingness to make claims about direct versus indirect connections, then many analyses, including multiple regression, might count as effective connectivity. Indeed, the PPI analysis referred to is typically described as an effective connectivity model, but it tests an interaction effect using linear regression (whether the slope of the linear association between two variables depends on the level of a third, moderating variable). The three-variable PPI model is actually a simple SEM though the criterion of assessing direct effects is not met, since no common indirect influences are accounted for. In the end, the difference between this model and more complicated SEMs is one of scale, and direct effects in any SEM can only be properly assessed if all relevant “3rd variables” have been included in the model and their connections modeled appropriately. While many researchers use both SEM and DCM with the goal of ascribing causality between different brain regions, the tests performed in both techniques are based on model fit rather than on the causality of the effect. Similarly, Granger causality (Roebroeck, Formisano, & Goebel, 2005) is another approach that is typically considered to test effective connectivity, though neither causal influences nor direct versus indirect effects are tested within the basic model framework. Causality is tested strictly in the sense of temporal relationships, rather than on whether activity in a brain region is necessary or sufficient for activity in another. In the end, it is not the label of “functional” or “effective” that is important, but the specific assumptions and robustness and validity of inference afforded by each method. When performing connectivity and correlation studies, it is tempting to make statements about causal links between different brain regions. The idea of causality is a very deep and important philosophical issue (Pearl, 2000; Rubin, 1974). A cavalier attitude frequently is taken in attributing causal effects, and the differentiation between explanation and causation is often blurred. Properly randomized experimental designs permit causal inferences of task manipulations on brain activity. In neuroimaging and EEG/MEG studies, all the brain variables are observed, and none are manipulated. We do not recommend making strong conclusions about causality and direct influences among brain regions using these methods because it is difficult to verify the validity of such conclusions. The combination of neuroimaging and TMS or related forms of brain stimulation (Bohning et al., 1997) may provide more reliable causal inferences about the effects of activating one brain region on another. By stimulating the brain, experimental manipulation of one brain area can be achieved and its causal effects on other brain regions thus examined. The problem

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remains of assessing which effects are direct as opposed to mediated by other intervening regions. Bivariate Connectivity Functional connectivity is a statement about the observed associations among regions and other performance and physiological variables. The simplest approach toward functional connectivity is to simply calculate the crosscorrelation between time series from two separate brain regions. The results can be used to determine whether the changes in activity in these regions are related to each other in a linear manner. This idea is expanded in seed analysis (Cordes et al., 2000; Della-Maggiore et al., 2000), where the cross-correlation between the time course from a predetermined region or cluster (the seed region) and all other regions of the brain is calculated. This allows researchers to search the brain for other regions that are positively (or negatively) correlated with the activity pattern found in the seed region. In addition to standard statistical assumptions, time series connectivity typically assumes that the connectivity is instantaneous, meaning that the time constants for neuronal and vascular effects are the same for each pair of regions, and the impulse response functions are thus the same. This assumption is often likely to be violated, and several approaches have been taken to account for variability in the neuronal activity—fMRI signal coupling, such as multivariate autoregressive modeling (Harrison, Penny, & Friston, 2003; Kim, Zhu, Chang, Bentler, & Ernst, 2007). Granger causality, a kind of autoregressive model discussed in more detail later, is a promising approach toward relaxing this assumption. Whatever method is used, functional connectivity is meaningful only to the degree that it is not driven by artifacts related to image acquisition and physiological noise; some artifactual influences are listed in Figure 9.15. Another approach that helps minimize issues of interregion neurovascular coupling differences and artifacts (but does not eliminate them) is the beta series approach (Rissman et al., 2004). In this technique, correlations are not estimated directly from the time series data. Instead, you obtain trial-by-trial estimates of event-related activity within the standard GLM framework. These trial-level activation parameter estimates (called beta values) are correlated across regions to obtain a measure of functional connectivity during each of the individual task components. Component Analysis: Principal Components Analysis, Independent Components Analysis, and Partial Least Squares Multivariate methods model brain imaging data by decomposing a large dataset (e.g., 1,000 time points ⫻ 100,000

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voxels ⫻ 20 subjects) into a smaller set of components and a series of weights. The components may be canonical patterns of activity across time and the weights their distribution across brain space, or vice versa. Principal components analysis (PCA), independent components analysis (ICA), and partial least squares (PLS) variations on this theme. These and related multivariate methods—canonical variates analysis (CVA), factor analysis, ordinal trends analysis (Habeck et al., 2005), and the multivariate linear model (MLM; Kherif et al., 2002)—are becoming an increasingly important part of the neuroimaging analyst’s toolbox. They all share the common core idea of decomposing the data into simpler components that maximize the variability explained by the model. The approaches differ in the criteria used to select components, and in whether the experimental design is included as part of the data to be modeled (inclusion is a defining feature of PLS). Each technique described in this section involves decomposing a data matrix, Y, into a set of spatial and temporal components. Let us define Y to be a t⫻v matrix, where t is the number of time points and v the number of voxels. Each column of Y is therefore a time series corresponding to one voxel in the brain, and each row is the collection of voxels that make up an image at a specific time point. Principal Components Analysis (PCA) decomposes the data matrix, Y, by finding linear combinations of time series, each of which make up a column in a matrix U (also of dimension t⫻v), such that each column of U is uncorrelated with every other column of U. The columns of U, called components, are arranged in order of variance explained: The first component explains the most variance possible in Y, the second component explains the maximal amount of remaining variance, and so forth. Together with their associated spatial maps and variances (to be described), these v components perfectly reproduce the data, but most of the total variance is usually captured in just the first few components of U. Thus, the first components can be considered a compressed representation of the data. Because each component is a weighted sum across time series of different voxels, another matrix V (of dimension voxel ⫻ component [v⫻v]) contains columns of voxel weights to create each component in U. The first column of V shows how to weight each of the v voxel time series to capture the most variance in Y and represents the spatial distribution of the first component. Thus, the columns of U are the temporal components (the “canonical” time series) and those of V are the spatial components (the maps across brain voxels) of these time series. In neuroimaging, the components are usually calculated through singular value decomposition (SVD) of the centered (mean-zero) data. SVD is a numerical technique that

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Data Analysis: Implementation

decomposes a data matrix, Y, into three simpler matrices (zeros make up at least half of the new matrices), while still representing the original data. In the case of neuroimaging data, these matrices can be interpreted as temporal components U and spatial components V such that: Y ⫽ USV ⌻

(9.6)

With centered (mean-zero) data, S is a diagonal matrix (only the diagonal elements are nonzero) whose entries are the “singular values,” the sums of squared deviations explained by each component. These are related to the eigenvalues such that λ ⫽ S2 /(t– 1). The columns of V are the eigenvectors, as in the eigendecomposition described earlier, and U*S are the component scores (components scaled by the amount of variability they explain), equal to Y*V in the eigendecomposition. The power of this technique lies in that the eigenvectors are orthogonal to each other. By decomposing the data into its eigenvectors and eigenvalues, we obtain a set of components (whether temporal or spatial) that are uncorrelated with each other. Furthermore, we also obtain coefficients of how heavily those components are represented in the original data. A thorough treatment of eigenvectors, eigenvalues, and SVD is provided by Strang (1980). Once you grasp the central idea of data decomposition into spatial and temporal components, you can understand many other techniques, such as ICA, as variations on this theme. Rather than maximizing the variance explained by each additional, orthogonal component, ICA components are chosen to maximize the statistical independence of the components in a more general sense. The components are not required to be orthogonal; rather, the constraint is that they be independent. The distribution of one component cannot be predicted from the values of the other, or more formally, the joint probability P(A,B) of components A and B is equal to P(A)P(B). In the Infomax variant of ICA, mutual information between components—a general measure of dependence that does not require the relationships between components to be linear or monotonic—is minimized (McKeown, 1998). ICA assumes that the data, Y, are a weighted sum of source signals (time series) contained in the source matrix X. The data Y is a linear mixture of these source components described by the weighting or mixing matrix of spatial weights M: Y ⫽ MX

(9.7)

Since both M and X are both unknown, there is no algebraic solution, so iterative search algorithms are used to estimate both M and X. An alternative decomposition is to transpose the data matrix and treat the spatial components as

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sources and the temporal components as mixing weights. (For more details, see McKeown & Sejnowski, 1998; Petersson, Nichols, Poline, & Holmes, 1999). At first glance, it appears close to impossible to solve Equation 9.7 for both M and X simultaneously. However, ICA makes crucial assumptions that allow you to obtain a solution. The main assumptions are that the data set consists of p statistically independent components, where at most one component is Gaussian. The independence assumption entails that the activations do not have a systematic overlap in time or space, while the non-Gaussiantity assumption is required for the problem to be well defined. In addition, it is assumed that the mixing matrix, M, is both square and invertible, which implies that the independent components can be expressed as a linear combination of the data matrix. Both PCA and ICA reduce the data to a simpler (lowerdimension than that of the v voxels) space by capturing the most prominent variations across the set of voxels. The components may reflect signals of interest or they may alternatively be dominated by artifacts, and it is up to the user to determine which are of interest (e.g., task-related). Both ICA and PCA assume all variability results from signal, as noise is not included in the model formulation. One issue involved with interpreting the results of an ICA analysis is that the sign of the independent components cannot be determined. In addition, the order of importance of the independent components cannot be determined. Therefore, it is necessary to sift through all the components to search for ones that are task-related or otherwise of interest. There is also no guarantee that a specific number of components can be used to explain most of the variation as is the case in PCA. A popular variant in the social sciences literature is factor analysis, which additionally fits a parameter for the noise variance at each voxel. A disadvantage of factor analysis is that the solution is rotationally indeterminate, and thus a number of combinations of spatial and temporal components can explain the same variability in the data. While both ICA and PCA are not rotationally indeterminate, there is some question as to what the “right” rotation is (in PCA it is determined by the amount of variance explained, which is not an index of meaningfulness since artifacts can create much variance). Interpreting thresholded component maps, as is commonly done, depends critically on establishing a rotation that is meaningful and reliable across studies. Multisubject Extensions As described so far, these techniques model only a single subject’s data. In a group study, there is the additional complexity of making population inference. It is not correct to treat all the data as coming from one supersubject and decomposing the group data matrix, for the same reasons that fixed effects

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analyses in the GLM are not appropriate. One approach is to decompose the group matrix, and subsequently “backreconstruct” or estimate spatial weights for each subject for a component of interest (Calhoun, Adali, Pearlson, & Pekar, 2001a). The spatial weights at each voxel across subjects are treated as random variables, and one-sample t-test is conducted to test whether that voxel loaded significantly on that component in the group. This approach is implemented in the Group Analysis of Functional Imaging toolbox (GIFT; Table 9.3). Another approach, called tensor ICA, is to use a three-way data decomposition with the group data to estimate temporal components and weights for each subject and each voxel (Beckmann & Smith, 2005). The subject weights at each voxel are then tested for significance. This approach is similar to related PCA-based techniques of PARAFAC (Bro, 1997) and INDSCAL/ALSCAL (Young, Takane, & Lewyckyj, 1978). It is implemented in the ICA tool (called MELODIC) in FSL software (Table 9.3). Structural Equation Modeling Structural equation modeling (SEM) has a rich history in the social sciences literature (Bollen, 1989). It was first applied to imaging data by McIntosh and Gonzalez-Lima (1994). In SEM, the emphasis lies on explaining the variance-covariance structure of the data. While SEM allows for the inclusion of latent variables (which is one of its major selling points in the social sciences), this option is not typically used by the neuroimaging community. An SEM without latent variables is typically called path analysis, but in this chapter we refer to methodology by the name structural equation modeling as this is the common practice in the neuroimaging literature. Structural equation models comprise a set of a priori determined regions and directed connections between these regions. A causal relationship is attributed a priori to the connections where an arrow from A to B implies that A causes B. Further path coefficients are defined corresponding to each link that represents the expected change in activity of one region given a unit change in the region influencing it. The path coefficient indicates the average influence across the time interval measured. Algebraically, we can express an SEM model as Y ⫽ MY ⫹εε

(9.8)

where Y is the data matrix, M is a matrix of coefficients that reflect the linear relationship between regions and e is independent and identically distributed normal noise. Typically, this model is rewritten: Y ⫽ (I ⫺ M)⫺1ε

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(9.9)

where I represents the identity matrix. The solution of the unknown coefficients in M is obtained by studying the empirical covariance matrix of Y. Like ICA, solving this model is not straightforward and typically users resort to iterative techniques. The covariance of the data represents how the activities in two or more regions are related. In SEM, we seek to minimize the difference between the observed covariance matrix and the one implied by the structure of the model. The parameters of the model are adjusted to minimize the difference between the observed and modeled covariance matrix. All inferences about the path coefficients rest on nested or stacked models. A hypothesis test on a single path coefficient may be performed by comparing the full model, with all path coefficients estimated, with a nested model in which the coefficient of interest is constrained to be zero.2 The two models are compared using a likelihood ratio test (LRT)—a statistical test of the goodnessof-fit between two models—to test whether a nonzero coefficient results in a significantly better model fit, and thus whether the coefficient is reliably different from zero. The LRT is only valid if it is used to compare nested models; that is, the more complex model must differ from the simple model only by the addition of one or more parameters. A similar approach can be taken when making inferences about changes in connectivity between different experimental conditions. This is done by first partitioning the data according to the different experimental conditions. Next, two models are specified. In the null model, path coefficients are constrained to be equal across conditions, and in the alternative model, coefficients of interest are allowed to vary. The LRT is used to test whether there is any significant difference between the models. If a significant difference exists, we reject the hypothesis that the path coefficients are equal in both conditions and a condition-dependent effect is declared. SEM makes some assumptions in setting up the model formulation. The data is assumed to be normally distributed and independent from sample to sample. An important consequence of the assumptions is that SEM discounts temporal information. Consequently, permuted data sets produce the same path coefficients as the original data, which is a weakness. The assumption of independence is violated in the analysis of a single subject. When looking at the individual differences level, this assumption is more reasonable.

2

Or another test value of interest.

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Dynamic Causal Modeling The measurements used in each of the connectivity approaches described so far are hemodynamic, and this limits the scope of the interpretation that can be made at the neuronal level. Dynamic causal modeling (Friston et al., 2003) is an attempt to move the connectivity analysis from the hemodynamic to the neuronal level. DCM uses standard linear systems analyses techniques, namely statespace design (Franklin, Workman, & Powell, 1997), and treats the brain as a deterministic nonlinear dynamic system that is subject to inputs and produces outputs. It makes inference about the coupling among brain areas and how the coupling is influenced by changes in experimental context. DCM models interactions at the neuronal rather than the hemodynamic level and is therefore more biologically accurate than many other models. However, the hemodynamic properties of the system must also be taken into account, as they can confound the measurements (e.g., a vascular delay could be interpreted as a neuronal delay). DCM is based on a neuronal model of interacting cortical regions, supplemented with a forward model describing how neuronal activity is transformed into the measured hemodynamic response. Effective connectivity is parameterized in terms of the coupling among unobserved neuronal activity in different regions. We can estimate these parameters by perturbing the system and measuring the response. Experimental inputs cause changes in effective connectivity at the neuronal level that in turn cause changes in the observed hemodynamics. DCM uses a bilinear model for the neuronal level and an extended balloon model (Buxton, Wong, & Frank, 1998) for the hemodynamic level. In a DCM model, the user specifies a set of experimental inputs (the stimuli) and a set of outputs (the activity in each region for each region). The task of the algorithm is then to estimate the parameters of the system, in this case, the “state variables.” Each region has five state variables; four correspond to the hemodynamic model and the fifth corresponds to neuronal activity. The estimation process is then carried out using Bayesian statistics: Normal priors are placed on the model parameters and an optimization scheme is used to estimate parameters that maximize the posterior probability. The posterior density is then used to make inferences about the significance of the connections between various brain regions. DCM is computationally demanding and is limited to eight regions in the current implementation of SPM. Granger Causality The main problem with methods such as SEM and DCM is that any misspecification of the underlying model will lead to erroneous conclusions. Granger causality takes a

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different approach to the problem. The technique was originally developed in economics (Granger, 1969) and has recently been applied to connectivity studies (Roebroeck et al., 2005). The benefit of Granger causality is that it does not rely on any a priori specification of a structural model, but rather is an approach for quantifying the usefulness of past values from various brain regions in predicting values in other regions. Granger causality provides information about the temporal precedence of relationships among two regions, but it is in some sense a misnomer because it does not actually provide information about causality. It is true that one variable (x) may precede a correlated variable (y) because x causes y. For example, hitting a baseball causes flight. However, there may be no causal relationship at all: A rooster may crow (x) every morning just before the sun rises (y), but it does not cause the sun to rise. For purposes of economic forecasting for which the technique was developed—or for making predictions based on fMRI data—the actual causal relationships may not matter, and Granger “causality” may be sufficient to be informative. However, it should not be taken as a measure of true causality. To illustrate the method, let x and y be two time courses of length N extracted from two brain regions or voxels. Each time course is modeled using a linear autoregressive model3 of the Mth order (where M≤N– 1), that is: M

x [ n ] ⫽ ∑ a [ i ]x [ n − m ] ⫹ εx [ n ] m⫽1

(9.10

M

y [ n ] ⫽ ∑ b [ i ] y [ n − m ] ⫹ εy [ n ] m⫽1

where both ex and ey are defined to be white noise. The vectors a and b are coefficients that describe how the current values of the time course depends on its past, and therefore it is clear from this formulation that both time courses depend immediately on their own past M values. As a second step of the analysis, we can expand each time course’s model using the autoregressive terms from the other signal. These additional autoregressive terms correspond to the directed influence (previous history) and not to the instantaneous signal; they can be written in the format: value_now ⫽ self_history ⫹ other_history ⫹ error More formally, the equations in our example can be expressed as: 3

Autoregressive models are used to represent processes whose “current” values can be written as a function of their own past values. The order of the model specifies how many steps back into the past the specified function goes.

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M

m⫽1

m⫽1

M

M

m⫽1

m⫽1

x [ n ] ⫽ ∑ a [ i ] x [ n − m ] ⫹ ∑ b [ i ] y [ n − m ] + εx [ n ] y [ n ] ⫽ ∑ b [ i ] y [ n − m ] ⫹ ∑ a [ i ] x [ n − m ] εy [ n ]

(9.11)

In this formulation, the current value of both time courses is assumed to depend both on the past M values of its own time course, but also the past M values of the other time course. By fitting each of these models (Equations 9.10 to 9.13), we can perform tests to determine whether the previous history of x has predictive value of the time course y (and vice versa). If the model fit is significantly improved by the inclusion of the cross-autoregressive terms, it provides evidence that the history of one of the time courses can be used to predict the current value of the other and a “Granger-causal” relationship is inferred. To test the influence between the two regions, we compare the fits to the model for each time course both with and without the additional “cross-autoregressive” terms (Roebroeck et al., 2005). The ratio of error sums of squares obtained from these fits are used to define a measure of the linear-directed influence from x to y, which is denoted Fx → y. If past values of x improve on the prediction of the current value of y, then Fx → y is large. A similar interpretation, but in the opposite direction, holds for Fy → x, which is defined in an analogous manner. The difference between these two terms can be used to infer which region’s history is more influential on the other. This difference is referred to as “Granger causality.” From this definition, it is clear that the idea of temporal precedence is used to identify the direction and strength of causality from information in the data. While it can reasonably be argued that temporal precedence is a necessary condition for causation, it is certainly not a sufficient condition. Therefore, to directly equate Granger causality and causality is a large leap of faith.

SUMMARY fMRI and PET are techniques for imaging the functioning human brain with increasingly precise temporal, spatial, and “neurochemical” resolution. Their major impact on psychology and neuroscience is to help establish a physical grounding in the brain for psychological concepts. However, inferences about psychological processes from brain activity are tricky to make. New kinds of analyses, such as pattern classification and meta-analysis, are helping researchers make inroads into this problem in limited ways. A second advantage of neuroimaging is that it allows researchers to study brain processes directly in humans,

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allowing links to be forged across animal and human neuroscientific research. Ultimately, this promotes the ability to integrate across fields and take advantage of a wealth of knowledge available from animal systems by establishing parallels with human brain processes. An obstacle in this process is the complexity of managing and processing neuroimaging data. To overcome this obstacle, researchers have developed a wide variety of tools, most of which are freely available to the research community, and centralized resources for distributing these tools over the internet. Finally, a third advantage of neuroimaging is that it allows full coverage of the brain, including—paticularly with fMRI—dynamic measures of activity every few seconds or less. This combination of spatial and temporal coverage is not available using other techniques, including those commonly used in animal models. This makes fMRI a particularly good tool for studying functional brain connectivity and large-scale distributed networks. A wide and growing array of connectivity analyses are being developed to exploit this feature, and the combination of advances in data collection, analysis methods, and methods for making psychological inferences are making fMRI a uniquely useful tool for neuroscientists.

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Chapter 10

Thalamocortical Relations S. MURRAY SHERMAN

The thalamus is a paired structure joined at the midline and located at the center of the brain (Figure 10.1). Each half is roughly the size of a walnut. The main part of the thalamus is divided into a number of discrete regions, known as relay nuclei. These contain the relay cells that project to the cerebral cortex. (In this chapter, cortex refers to neocortex, which does not include the hippocampal formation or olfactory cortex.) Lateral to this main body of the thalamus is the thalamic reticular nucleus (TRN in Figure 10.1), which fits like a shield alongside the body of the main relay nuclei of the thalamus. The thalamic reticular nucleus is comprised entirely of GABAergic neurons that do not innervate cortex but instead innervate thalamic relay cells. In Figure 10.1,

all but the front part of the thalamic reticular nucleus has been cut away to reveal the relay nuclei. Strictly speaking, the relay nuclei are the dorsal thalamus, while the thalamic reticular nucleus is part of the ventral thalamus; here, dorsal and ventral reflect embryonic origin rather than relative location in the adult, meaning that the relay nuclei and thalamic reticular nucleus have different developmental origins. Unless otherwise specified, thalamus refers to the relay nuclei of the dorsal thalamus. Virtually all information reaching the cortex must pass through and be relayed by the thalamus. Thus anything we are consciously aware of and all of our perceptions of the outside world depend on thalamic relays. This relay is dynamically controlled by behavioral states and processes, including attentional demands. Each of the main relay nuclei shown in Figure 10.1 innervate one or a small number of cortical areas and, as far as we know, every area of cortex receives a thalamic input. The thalamus is there not just to get peripheral information to the cortex, but it continues to play a vital role in the further processing of this information by the cortex. Where thalamocortical relationships are understood (e.g., the projection of the lateral geniculate nucleus to the primary visual cortex), the thalamic input plays a major role in determining the functional properties of the cortical target area. It has been shown that if retinal inputs in the ferret are diverted into the medial geniculate nucleus instead of the normal auditory inputs, the auditory cortex acquires visual responsiveness and organizes orientation-specific domains normally seen only in the visual cortex (Sharma, Angelucci, & Sur, 2000). Since all thalamic nuclei innervate cortex and all cortical areas are thus innervated, this might suggest that the functional properties of any cortical area follow its thalamic input rather than inputs from other cortical areas. This is a rather subversive idea that runs counter to the traditional dogma that cortical processing depends solely on direct cortico-cortical pathways. Cortico-thalamo-cortical pathways play a heretofore neglected and perhaps dominant role in cortical functioning.

Figure 10.1 Schematic view of the right thalamus of the human. Shown are the main relay nuclei plus the thalamic reticular nucleus (TRN), of which only the anterior portion is visible; the remainder has been removed to reveal the thalamic relay nuclei. Normally, the thalamic reticular nucleus extends the length of thalamus as a thin shield closely apposed to the lateral surface of the relay nuclei. Abbreviations: A, Anterior Nuclei; CM, Central Medial Nucleus; IL, Intralaminar Nuclei; LD, Lateral Dorsal Nucleus; LGN, Lateral geniculate Nucleus; LP or PO, Lateral Posterior or Posterior Nucleus; MD, Medial Dorsal Nucleus; MGN, Medial Geniculate Nucleus; MI, Midline Nuclei; P, pulvinar; TRN, thalamic reticular nucleus; VA, Ventral Anterior Nucleus; VL, Ventral Lateral Nucleus; VPL, Ventral Posterolateral Nucleus; VPM, Ventral Posteromedial Nucleus. 201

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To understand the functional relevance of the thalamus, it is necessary to understand some details about cell and circuit properties. Fortunately, these are mostly conserved throughout thalamus, so once we appreciate these for a model nucleus, we can extrapolate these properties for the entire thalamus. This is not to say that there are not important differences found among thalamic relay nuclei, but we concentrate in this chapter on those major properties that are common to the thalamus. The best-known and most thoroughly studied of the thalamic nuclei is the lateral geniculate nucleus, which relays retinal information to the visual cortex. We use this nucleus as our model template for cell and circuit properties. For details of the thalamus beyond the scope of this chapter, see recent books on this topic by Jones (2007) and Sherman and Guillery (2006).

BASIC CELL TYPES There are three major cell types involved in thalamic circuitry: relay cells, local interneurons, and thalamic reticular nucleus cells. The relay cells use glutamate as a neurotransmitter, whereas the other cell types are GABAergic. Relay Cells Although the evidence for different classes of relay cells is scattered and incomplete outside of the lateral geniculate nucleus (e.g., Li, Bickford, & Guido, 2003; Yen & Jones, 1983), the evidence is firm for several distinct types of geniculate relay cell. For example, the main geniculate relay cell classes in the cat are called X and Y (see Figure 10.2), and the equivalent types in the monkey are called parvocellular and magnocellular based on the geniculate laminae in which they are located (Casagrande & Xu, 2004; Hendry & Reid, 2000; Sherman, 1985). Another cell type, called W in the cat and K in the monkey, has also been described, but it is unclear if this is one or several distinct classes, and these cells are relatively poorly understood; further details can be found in Casagrande and Xu (2004; Hendry and Reid (2000); and Sherman (1985). These X and Y cell types in the cat’s lateral geniculate nucleus are innervated by equivalent, distinctive retinal cell types and thus represent an independent, parallel, retino-geniculo-cortical neuronal streams of information. X cells have smaller cell bodies and dendritic arbors that are largely bipolar and oriented perpendicular to the laminar borders of the lateral geniculate nucleus (Friedlander, Lin, Stanford, & Sherman, 1981; Stanford, Friedlander, & Sherman, 1983). Y cells have larger cell bodies and thicker dendrites arranged more or less in a spherical volume (Friedlander et al., 1981;

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Figure 10.2 Examples of thalamic cell types from lateral geniculate nucleus of the cat. These tracings were made from intracellular labeling during in vivo recording (Friedlander et al., 1981; Hamos et al., 1985). A: X relay cell. Note the grape-like appendages near primary branch points. Three examples are shown at higher magnification. B: Y relay cell. C: Interneuron. The dendrites have the appearance of an axonal terminal arbor, and the many boutons seen among the dendrites are indeed synaptic boutons known as F2. Examples are shown in the higher magnification. Scale: The scale bar is 50 µm for the cell drawings and 10 µm for the insets of A and C.

Stanford et al., 1983). For both cells, retinal inputs innervate relatively proximal dendrites, within about 100 µm from the cell body (Wilson, Friedlander, & Sherman, 1984). On Y cells, these retinal synapses are formed fairly simply onto dendritic shafts, but in X cells, these tend to contact curious grape-like appendages found near proximal dendritic branch points. Interneurons Interneurons are particularly interesting cells because, in addition to conventional axonal outputs, they also produce presynaptic terminals from their dendrites, and these dendritic outputs are more numerous than are the axonal (Friedlander et al., 1981; Hamos, Van Horn, Raczkowski, Uhlrich, & Sherman, 1985; Wilson et al., 1984). Figure 10.2C shows an example of an interneuron from the lateral geniculate nucleus of the cat. The dendrites look so much like an axonal terminal arbor that they have been called axoniform (Guillery, 1966). The axonal arbor distributes within the dendritic arbor, and they look so much alike that, with light microscopy, it is often impossible to distinguish the axon. However, because the axon is myelinated and the dendrites are not, they can readily be distinguished with an electron microscope. Also, much work at the electron microscopic level (Famiglietti & Peters, 1972; Guillery, 1969; Hamos et al., 1985; Ralston, 1971) has made it possible to distinguish the axonal terminals (called F1) from the dendritic terminals (called F2; see Figure 10.2C). One important distinction is that the axonal (F1) terminals are strictly presynaptic

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Cell Properties of Thalamic Relay Neurons

(to relay cells and other interneurons), whereas the dendritic (F2) terminals are both presynaptic (mostly to the grape-like clustered appendages of X cells; see Figure 10.2A and Wilson et al., 1984) and postsynaptic (mostly to retinal terminals). The F2 terminals are the only postsynaptic terminals so far described in the thalamus. The circuits entered into by these F2 terminals and the functional properties of interneurons are discussed further later in the chapter. Thalamic Reticular Nucleus Cells The final major cell type in the thalamus is the reticular cell, found in the thalamic reticular nucleus. These tend to have elongated dendritic arbors oriented parallel to the borders of the thalamic reticular nucleus (Figure 10.3; Uhlrich, Cucchiaro, Humphrey, & Sherman, 1991). Their axons project into the main relay nuclei of the thalamus and selectively target relay cells (Cucchiaro, Uhlrich, & Sherman, 1991), and local collaterals provide for contacts between reticular cells (Lam, Nelson, & Sherman, 2006; Sanchez-Vives, Bal, & McCormick, 1997). These cells are

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also functionally connected via gap junctions (Lam et al., 2006; Landisman et al., 2002).

CELL PROPERTIES OF THALAMIC RELAY NEURONS Thalamic relay cells, like cells throughout the central nervous system, have numerous voltage- and time-gated ionic channels in their membranes. The best known of these are the Na and K channels underlying the conventional action potential (see Figure 10.4). There are many others, including channels for other cations. One that is especially important to thalamic relay cells involves T-type Ca2 channels. (For details of T channel properties, see Huguenard & McCormick, 1994; Jahnsen & Llinás, 1984a, 1984b; and for other voltage gated channels in thalamic neurons, see Huguenard & McCormick, 1994; Sherman & Guillery, 2006.) The properties of these T channels are qualitatively the same as those of Na channels involved with the action potential. Figure 10.4 summarizes these properties, emphasizing the similarities with the T channels shown in Figure 10.5. Basic Properties of the T Channel

Figure 10.3 Example of cell in the thalamic reticular nucleus of Galago filled with neurobiotin. The star in the inset shows the location of the cell body. Redrawn from Figure II-12 of (Sherman and Guillery, 2006) from data supplied by P Smith, K Manning and D Uhlrich. Abbreviations: As in Figure 10.1, plus IC, internal capsule.

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Figure 10.4 is a review of the main properties of the Na (and K) channels underlying the action potential. When the Na channel is open, Na flows into the cell, producing a depolarizing current known as INa. However, the Na channel has two voltage sensitive gates—an activation gate and an inactivation gate—and both must be open for Na to flow into the cell. At a normal resting membrane potential (e.g., 65 mV), the inactivation gate is open, but the activation gate is closed, and thus there is no inward flow of Na (Figure 10.4A). Here INa is deactivated because the activation gate is closed, but it is also relieved of inactivation (or is de-inactivated) because the inactivation gate is open. When the membrane is depolarized to a certain level, the activation threshold for INa (Figure 10.4B), the activation gate pops open and so INa is both activated and de-inactivated; the result is that Na flows into the cell, producing the depolarizing upswing of the action potential. This depolarization, after a suitable period of 1 msec or so, leads to closing of the inactivation gate, and so while the Na channel remains activated, it is also inactivated (Figure 10.4C). This plus the opening of various slower K channels (channels that do not inactivate because they have only an activation gate), which produces a hyperpolarizing outward flow of K, repolarizes the membrane to near its starting position (Figure 10.1D). However, despite being repolarized, INa

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Figure 10.4 Schematic representation of voltage dependent Na+ and K+ channels underlying the conventional action potential. A–D show the channel events and E shows the effects on membrane potential. The Na+ channel has two voltage dependent gates: an activation gate that opens at depolarized levels and closes at hyperpolarized levels, and an inactivation gate with the opposite voltage dependency. Both must be open for the inward, depolarizing Na+ current (INa) to flow. The K+ channel (actually an imaginary combination of several different K+ channels) has a single activation gate, and when it opens at depolarized levels, an outward, hyperpolarizing K+ current is activated. A: At a resting membrane potential (roughly 60 to 65 mV), the activation gate of the Na+ channel is closed, and so it is deactivated, but the inactivation gate is open, and so it is de-inactivated. The single gate for the K+ channel is closed, and so the K+ channel is also deactivated. B: With sufficient depolarization

to reach its threshold, the activation gate of the Na+ channel opens, allowing Na+ to flow into the cell. This depolarizes the cell, leading to the upswing of the action potential. C: The inactivation gate of the Na+ channel closes after the depolarization is sustained for roughly 1 msec (“roughly,” because inactivation is a complex function of time and voltage), and the slower K+ channel also opens. These combined channel actions lead to the repolarization of the cell. While the inactivation gate of the Na+ channel is closed, the channel is said to be inactivated. D: Even though the initial resting potential is reached, the Na+ channel remains inactivated, because it takes roughly 1 msec (“roughly” having the same meaning as above) of hyperpolarization for de-inactivation. E: Membrane voltage changes showing action potential corresponding to the events in A to D. Redrawn from Figure IV-4 of (Sherman and Guillery, 2006).

remains inactivated because it takes roughly 1 msec of this hyperpolarization to open the inactivation gate, restoring the initial conditions of Figure 10.1A. Thus the two gates of the Na channel have opposite voltage dependencies and both respond relatively quickly to voltage changes. Finally, note that the roughly 1 msec of hyperpolarization needed to de-inactivate the Na channel provides a refractory period limiting firing rates for the action potential to 1000 Hz. While Figure 10.4 shows the basic voltage gated properties of the Na channel, one other feature is essential to propagating an all-or-none action potential. That is, the density of Na channels must be sufficiently high that, once threshold is reached, the further depolarization caused by the initial channels to open causes a self-regenerating,

explosive opening of other channels, and this propagates as an action potential. If the Na channel density were too low, the initial channels to open would lead only to a local depolarization that would decay exponentially. As shown in Figure 10.5, the voltage behavior of the T channel is qualitatively the same as that of the Na channel, with the same two types of voltage gate. At the starting position of Figure 10.5A, the activation gate is closed, but sufficient depolarization opens it (Figure 10.5B), allowing the inward IT that further depolarizes the cell. This depolarization eventually inactivates IT (Figure 10.5C) which, along with the activation of K channels, repolarizes the cell (Figure 10.5D). This repolarization eventually leads to de-inactivation of IT (Figure 10.5A).

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Figure 10.5 Schematic representation of actions of voltage dependent T (Ca2+) and K+ channels underlying low threshold Ca2+ spike; conventions as in Figure 10.4. Note the strong qualitative similarity between the behavior of the T channel here and the Na+ channel shown in Figure 10.4, including the presence of both activation and inactivation gates with similar relative voltage dependencies. A–D show the channel events and E shows the effects on membrane potential. A: At a relatively hyperpolarized resting membrane potential (roughly 70 mV), the activation gate of the T channel is closed, but the inactivation gate is open, and so the T channel is deactivated and de-inactivated. The K+ channel is also deactivated. B: With sufficient depolarization to reach its threshold, the activation gate of the T channel

opens, allowing Ca2+ to flow into the cell. This depolarizes the cell, providing the upswing of the low threshold spike. C: The inactivation gate of the T channel closes after roughly 100 msec (“roughly”, because, as for the Na+ channel in Figure 10.4, closing of the channel is a complex function of time and voltage), inactivating the T channel, and the K+ channel also opens. These combined actions repolarize the cell. D: Even though the initial resting potential is reached, the T channel remains inactivated, because it takes roughly 100 msec of hyperpolarization for de-inactivation. E: Membrane voltage changes showing low threshold spike corresponding to the events in A to D. Redrawn from Figure IV-5 of (Sherman and Guillery, 2006).

As in the case of Na channels, if a sufficiently high density of T channels exists, the threshold opening of the initial T channels leads to an explosive all-or-none spike. This is the case for thalamic relay cells, and the result is a spike-like depolarization of roughly 25 to 50 mV that propagates throughout the dendrites and soma. T channels are quite common in neurons throughout the central nervous system, but only in rare cells is the density high enough to support all-or-none Ca2 spikes. Thus, this property of all-or-none Ca2 spiking based on T channels is fairly unique to the thalamus. Every relay cell of every nucleus in every mammalian species so far tested shows this property (Sherman & Guillery, 2006). However, a further inspection of Figures 10.4 and 10.5 reveals certain important quantitative differences between the behavior of the Na and Ca2 channels. Perhaps most important are the temporal properties of

the inactivation gates. While the activation gates for both channels respond quickly to voltage changes, as does the inactivation gate of the Na channel, the inactivation gate of the T channel is much slower, requiring roughly 100 msec of a sustained polarization change to open or close. Actually, as is the case for the Na channel, the inactivation gate of the T channel has a complex voltage- and time-dependency, so that the greater the sustained polarization change, the more rapidly the gate opens or closes (Zhan, Cox, Rinzel, & Sherman, 1999). This temporal property for the T channel is important and will be considered further. Another quantitative difference is the functional voltage range: the T channel operates in a more hyperpolarized regime. In fact, because the T channel activates at a more hyperpolarized level, the resulting depolarization, which in thalamic relay cells is an all-or-none Ca2 spike, is also known as the “low threshold spike.”

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One other important difference not shown in Figures 10.4 and 10.5 is the distribution of these channels: T channels are effectively limited to the soma and dendrites, whereas Na channels, often found there as well, are notable for their distribution along the axon. This allows action potentials to travel from the soma to a target far away, and in the case of thalamic relay cells, this Na channel distribution permits action potentials to be delivered to cortical targets. While T channels underlie Ca2 spikes propagated in the dendrites and soma, these spikes do not propagate to the cortex. Thus the significance of these Ca2 spikes ultimately rests with their effect on conventional action potentials as described in the following section. This effect is dramatic and important. Burst and Tonic Firing Modes The primary functional significance of T channels for thalamic relay cells is that they are responsible for which of two very different response modes, called burst and tonic, characterize these cells’ responses (Jahnsen & Llinás, 1984a; Zhan et al., 1999). Figure 10.6 summarizes some of the features of these response modes. If the cell has been initially depolarized by just a few mV (5 mV) from rest, the T channels are inactivated and play no role in the response. This leads to tonic firing (Figure 10.6A) where a depolarizing current injection elicits a stream of unitary action potentials that lasts as long as the stimulus is suprathreshold. If, however, the cell has been hyperpolarized initially by 5 mV or so from rest, the T channels are de-inactivated and primed to respond to the next suitable depolarization, and the result is burst firing. This is shown in Figure 10.6B where the same depolarizing stimulus as in Figure 10.6A now evokes an all-or-none low threshold Ca2 spike with a burst of high frequency action potentials riding its crest. The exact same stimulus (think of this as the same excitatory postsynaptic potential or EPSP evoked from the same retinal input to a geniculate relay cell) creates a very different pattern of action potentials depending on the recent voltage history of the relay cell, and this pattern of firing is the only signal that reaches cortex. To summarize: The recent voltage history of a relay cells determines the inactivation state of its T channels, and this, in turn, determines whether the relay cell responds to its next, suprathreshold excitatory input in tonic or burst mode, a determination that dramatically affects the message relayed to the cortex. Significance of Response Mode for Thalamocortical Relays A major question for which we have only partial and largely hypothetical answers is: What is the functional significance

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Figure 10.6 Properties of IT and the low threshold Ca2+ spike. All examples are from relay cells of the cat’s lateral geniculate nucleus recorded intracellularly in an in vitro slice preparation. A, B: Voltage dependency of the low threshold spike. Responses are shown to the same depolarizing current pulse delivered intracellularly but from two different initial holding potentials. When the cell is relatively depolarized (A), IT is inactivated, and the cell responds in tonic mode, which is a stream of unitary action potentials to a suprathreshold stimulus. When the cell is relatively hyperpolarized (B), IT is de-inactivated, and the cell responds in burst mode, which involves activation of a low threshold Ca2+ spike (LTS) with multiple action potentials (8 in this example) riding its crest. C: Input-output relationship for another cell. The abscissa is the amplitude of the depolarizing current pulse, and the ordinate is the firing frequency of the cell for the first 6 action potentials of the response, since this cell usually exhibited 6 action potentials per burst in this experiment. The initial holding potentials are shown, and 47 mV and 59 mV reflects tonic mode, whereas 77 mV and 83 mV reflects burst mode. Redrawn from Figure IV-6 of Sherman and Guillery (2006).

of the burst and tonic response modes for thalamic relay performance? One answer comes from a consideration of the fact that the only message reaching the cortex is in the form of action potentials and they are evoked differently in the two response modes. During tonic firing, action potentials are

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Cell Properties of Thalamic Relay Neurons

directly evoked by an appropriate, suprathreshold depolarizing stimulus (e.g., an EPSP), and so a larger EPSP will evoke more firing. In other words, there is a relatively linear relationship between input (or EPSP) amplitude and firing rate. During burst firing, however, action potentials are not directly activated by the depolarizing input; instead they are activated by the large, depolarizing low threshold Ca2 spike. Because this Ca2 spike is all-or-none, a larger depolarizing input or EPSP will not evoke a larger Ca2 spike, and thus the input-output relationship during burst firing is highly nonlinear, approximating a step function. These differences are illustrated in Figure 10.6C (Zhan et al., 1999). Figure 10.7 shows related and additional effects of response mode. In this example, a geniculate relay cell is recorded intracellularly in an anesthetized cat while its responses to visual stimuli are monitored. These responses indicate how retinal input is relayed to the cortex. Because of the intracellular recording, it is possible to pass current into the cell either to depolarize its baseline level sufficiently to inactivate IT (e.g., baseline depolarized to –65 mV in Figure 10.7A) and promote tonic firing or to hyperpolarize it (e.g., baseline depolarized to –75 mV in Figure 10.7B) so as to deinactivate IT and promote burst firing. The visual stimulus in this case is a drifting sinusoidal grating, providing a visual stimulus in which contrast varies sinusoidally with time at 2 Hz. Figure 10.7A, lower, shows that the tonic response profile is sinusoidal and accurately reflects the contrast changes in the stimulus. The response in burst mode (Figure 10.7B, lower) does not accurately reflect the contrast changes, showing the sort of nonlinear distortion that can largely be predicted by the cellular properties shown in Figure 10.6C. This provides an obvious advantage for tonic firing because the nonlinear distortion caused by burst firing will limit the fidelity of the message relayed to the cortex. In other words, to faithfully reconstruct the visual world, the cortex is better served by tonic firing. What, then, is the purpose of burst firing? Two possible advantages have been suggested. One advantage is related to spontaneous firing, which is much lower during burst firing (Figure 10.7A, B, upper histograms; Guido, Lu, & Sherman, 1992; Guido, Lu, Vaughan, Godwin, & Sherman, 1995). Actually, the higher spontaneous activity helps preserve linearity during tonic firing because the raised level of activity allows inhibitory components of the visual stimulus to be represented; without this, the response would “bottom out,” reflecting rectification, which is itself a nonlinearity. There is another, perhaps more important, consequence of the difference in spontaneous activity levels. Spontaneous activity can be considered noise from the perspective of the cortex because, by definition, it represents firing in the geniculate relay cell that bears no relation to visual stimulation. The lower histograms in Figure 10.7A and B suggest that

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Figure 10.7 Responses of a representative relay cell in the lateral geniculate nucleus of a lightly anesthetized cat to a sinusoidal grating drifted through the cell’s receptive field. The trace at the bottom reflects the sinusoidal changes in luminous contrast with time. Current was injected into the cell through the recording electrode to alter the membrane potential. Thus in A, the current injection was adjusted so that the membrane potential without visual stimulation averaged 65 mV, promoting tonic firing, because IT is mostly inactivated at this membrane potential; in B, the current injection was adjusted to the more hyperpolarized level of 75 mV, permitting de-inactivation of IT and promoting burst firing. Shown are average response histograms to the visual stimulus (bottom histograms in A and B) and during spontaneous activity with no visual stimulus (top histograms), plotting the mean firing rate as a function of time averaged over many epochs of that time. The sinusoidal changes in contrast as the grating moves across the receptive field are also shown as a dashed, gray curve superimposed on the responses in the lower histograms. Note that the response profile during the visual response in tonic mode looks like a sine wave, but the companion response during burst mode does not. Note also that the spontaneous activity is higher during tonic than during burst firing. Redrawn from Figure VI-2 of Sherman and Guillery (2006).

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208 Thalamocortical Relations

the signal relayed during both response modes to visual stimulation are roughly equal in extent, and so the lower noise during burst firing suggests that the signal-to-noise ratio is higher during burst firing. A higher signal-to-noise ratio, in turn, suggests better detectability of the stimulus in the response of the geniculate relay cell, and this has been demonstrated (Guido, Lu, et al., 1995). Another advantage of burst firing is that it more powerfully affects the cortex (Swadlow & Gusev, 2001; Swadlow, Gusev, & Bezdudnaya, 2002). This is because the thalamocortical synapse shows strong paired-pulse depression (Abbott, Varela, Sen & Nelson, 1997; Castro-Alamancos & Connors, 1997; Chung, Li, & Nelson, 2002; Gil, Connors, & Amitai, 1999; Lee & Sherman, 2008, 2009) This is shown in Figure 10.8A, where a facilitating layer 6 corticothalamic synapse (Reichova & Sherman, 2004) is also shown for comparison. For a depressing synapse, action potentials

Figure 10.8 Examples of paired-pulse depression and pairedpulse facilitation. These recordings were made from in vitro slices of the mouse brain in which thalamocortical and corticothalamic projections are retained in the somatosensory system. When electrical stimulation is applied as a train of impulses at fixed frequency to afferents of a recorded cortical or thalamic cell, the resultant EPSPs decrease with stimulus number (paired-pulse depression) or increase (paired-pulse facilitation). A: Example of paired-pulse depression (upper trace; recording from a layer 4 cell and activating inputs from thalamus) and paired-pulse facilitation (lower trace; recording from thalamic relay cell and activating inputs from layer 6 of cortex). B: Time course of paired-pulse effects for the examples in A. The abscissa shows the interstimulus interval, and the ordinate, the measure of depression (left) or facilitation (right) expressed as the ratio of the amplitude of the second EPSP to the amplitude of the first. Unpublished data from laboratory of S.M. Sherman.

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arriving with interspike intervals of less than 50 to 150 msec (see Figure 10.8B) or so will depress the postsynaptic responses resulting in a smaller EPSP. During tonic firing, interspike intervals are sufficiently high to keep the thalamocortical synapses in more or less a constant state of depression. However, the dynamics of burst firing result in a synapse with no depression. This is because, to burst, a cell must be in a sustained state of hyperpolarization for 100 msec or so (to de-inactivate T channels) before responding to a depolarizing EPSP, and so there can be no action potentials during this period; this imposes a requisite silent period on a cell before each burst meaning that, when the burst is evoked, the thalamocortical synapse is free of depression. Elegant experiments by Swadlow and colleagues (Swadlow & Gusev, 2001; Swadlow et al., 2002) have directly confirmed this (Figure 10.9). Hypothesis for Burst and Tonic Firing To summarize the known functional consequences of firing mode (and there may be many other, undiscovered ones), tonic mode is associated with a more linear relay, while burst mode is associated both with superior signal detection and stronger cortical activation. This has led to the theory (Sherman, 1996, 2001; Sherman & Guillery, 2002, 2006) that burst mode may be involved in providing a strong “wake-up call” to the cortex that something has changed in the environment (e.g., the sudden appearance of a new visual stimulus), particularly in circumstances during which attention is not devoted to the relay under question (e.g., general drowsiness, or inattention, or for auditory thalamic relays while attention is diverted to visual stimuli). There are several very indirect lines of evidence in support of this. One is that bursting of thalamic relay cells increases with drowsiness (Massaux & Edeline, 2003; Ramcharan, Gnadt, & Sherman, 2000; Swadlow & Gusev, 2001). Another is that the initial cycle of a repeating visual tends more frequently to evoke bursting of geniculate relay cells (Guido & Weyand, 1995). Finally, studies of visual responses, including the use of natural visual scenes as stimuli, indicate that the best stimulus to evoke a burst is the replacement in the visual field of an inhibitory stimulus with an excitatory one, for instance, the replacement of a dark spot over the center of an on-center cell with a bright spot (Alitto, Weyand, & Usrey, 2005; Denning & Reinagel, 2005; Lesica & Stanley, 2004; Wang et al., 2007). For this hypothesis to make sense, thalamic circuitry must be arranged in a manner that can efficiently control response mode, promoting the transition between burst and tonic firing under appropriate conditions. That is, there must be inputs to relay cells that effectively control membrane potential for a sufficiently long period (i.e., for at least 100 msec or so) to control the inactivation state of IT. As the next section shows, this is indeed the case.

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Circuit Properties of Thalamic Relay Neurons

Figure 10.9 (Figure C.3 in color section) Current source density profiles in cortex generated from single spike in afferent thalamic neuron. Recordings were made simultaneously in an awake rabbit from a single neuron in the ventral posterior medial nucleus of the thalamus and from 16 probes at different depths along a column in the cortical target field of the recorded thalamic neuron. Spike triggered averaging was used to generate the synaptic sinks and sources as shown. A, B: Colorized current source density profile generated by tonic spike (A, ~120,000 thalamic action potentials) or first spike in burst (B, 2427 thalamic action potentials) in the thalamic afferent. The vertical orange line in each indicates the time of the action potential in the thalamic afferent. The red arrow in each shows the current source

evoked by the terminals of the thalamic afferent, and note that this is the same for the tonic and burst spike. The depths of layers 4 and 6 are also indicated. The vertical dashed white lines show the initial 1 msec of the postsynaptic responses, with large sinks in layers 4 and 6. Note the denser sinks for the burst spike (B) compared to the tonic spike (A). C, D: amplitude (peak peak) of the axon terminal response (C, indicated by the red arrows in A, B) and the magnitude of the initial 1 msec of the postsynaptic current sink (D) plotted at different recording sites for both the tonic spike and first spike in a burst. Note that there is no difference in the corticothalamic terminal responses for these two spikes but that the peaks in layers 4 and 6 are greater for the burst spike. Redrawn from Figure 3 of Swadlow et al. (2002).

CIRCUIT PROPERTIES OF THALAMIC RELAY NEURONS

Also intimately associated with relay cells are two types of local, GABAergic neurons that provide inhibitory input to relay cells: these are local interneurons and cells of the nearby thalamic reticular nucleus. Interneurons live among relay cells throughout the relay nuclei of the thalamus, and the ratio is roughly three relay cells to every interneuron across nuclei and species, with one curious exception (Arcelli, Frassoni, Regondi, De Biasi, & Spreafico, 1997). That is, while the lateral geniculate nucleus of the rat and mouse contain roughly 25% interneurons, the rest of the thalamus in these species contain almost no interneurons. This is not a rodent property because the thalamus of other rodent species, like squirrels, guinea pigs, and so on, contains normal numbers of interneurons. There are two major sources of extrinsic input to geniculate circuitry (see Figure 10.10). One is a feedback glutamatergic projection from layer 6 of the visual cortex, and the other is a mostly cholinergic input from

Fortunately, the detailed circuit properties of the thalamus are largely conserved among thalamic nuclei. To be sure, there are some differences in circuitry among thalamic nuclei. Certain ones will be discussed next. Because we know most about the lateral geniculate nucleus, this serves as a convenient model for all of the thalamus. Figure 10.10 schematically shows the main circuitry involving geniculate neurons, including the main transmitters and classes of postsynaptic receptor involved. (These circuit details are reviewed in Sherman & Guillery, 1996, 2004, 2006). Basic Anatomical Circuits Relay cells receive input from retinal axons and, in turn, project to visual cortex, mostly to layer 4 but also to layer 6.

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209

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210 Thalamocortical Relations

Figure 10.10 Schematic view of details of the main connections of the lateral geniculate nucleus. Indicated are the inhibitory or excitatory nature of the synapses, the postsynaptic receptors activated by each input on relay cells, and the neurotransmitters involved. Abbreviations: ACh, acetylcholine; GABA, -aminobutyric acid; Glu, glutamate; LGN, lateral geniculate nucleus; PBR, parabrachial region; TRN, thalamic reticular nucleus.

the parabrachial region of the midbrain. In both cases, individual axons branch to innervate all three thalamic cell classes: relay cells, interneurons, and reticular cells. Not shown for simplicity are various serotonergic, noradrenergic, GABAergic, and dopaminergic inputs from the brain stem and histaminergic inputs from the tuberomamillary nucleus of the hypothalamus. This is partly to avoid unnecessary complication and also because the functional significance of these other inputs is just beginning to be understood. (see Sherman & Guillery, 1996, 2004, 2006). Thus, not only do relay cells receive inputs from the retina, which represents the main input relayed to the cortex, but they also receive inputs from other sources as well. Postsynaptic Receptors on Relay Cells It is clear from Figure 10.10 that nonretinal inputs can influence retinogeniculate transmission. All of these inputs to relay cells operate via conventional chemical synapses, and thus their postsynaptic effects are largely controlled by postsynaptic receptors. These, too, are illustrated in Figure 10.10, and they are divided into two main groups: ionotropic and metabotropic. Examples of ionotropic receptors for the transmitter systems shown are AMPA receptors for glutamate, the GABAA receptor, and nicotinic receptors for acetylcholine; the equivalent metabotropic receptor examples are various metabotropic glutamate receptors, the GABAB receptor, and various muscarinic receptors for acetylcholine. Details of differences between these receptor classes are many (Brown et al., 1997; Conn & Pin, 1997; Mott & Lewis, 1991; Nicoll, Malenka, & Kauer, 1990; Pin &

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Duvoisin, 1995; Recasens & Vignes, 1995), but two major differences are particularly relevant here. First, excitatory and inhibitory postsynaptic potentials (EPSPs and IPSPs) generated via ionotropic receptors tend to be very brief, on the order of 10 msec or a few 10s of msec, whereas those via metabotropic are much more sustained, lasting 100s of msec to several seconds. Second, metabotropic receptors tend to be less sensitive in the sense that afferent firing rates usually need to be higher before they are activated; this is because these receptors tend to be a bit eccentrically located in the synapse with respect to ionotropic receptors (Lujan, Nusser, Roberts, Shigemoto, & Somogyi, 1996; Somogyi, Tamas, Lujan, & Buhl, 1998), and so more transmitter must be released to reach them. With these differences in mind, it is interesting that retinal input activates only ionotropic receptors (mostly AMPA), whereas all of the nonretinal inputs activate metabotropic receptors, often in addition to activation of ionotropic receptors. One input for which the postsynaptic receptor is not as clear is the input from interneurons to relay cells: clearly GABAA receptors are involved, but there have as yet been no definitive tests for the presence or absence of GABAB receptors for this input. Consequences of Type of Postsynaptic Receptor The fact that only ionotropic receptors are activated by retinal input is good for transfer of temporal information. That is, because the evoked EPSPs are brief, temporal summation does not occur until relatively high rates of firing in the retinal afferents, and thus it is possible to evoke a single EPSP for every retinal action potential for reasonable high rates of firing, thereby representing each input action potential as an EPSP in a one-to-one manner. Put another way, if retinal inputs activated metabotropic glutamate receptors, the sustained EPSPs would summate at lower firing rates, and no longer would postsynaptic responses ultimately relayed to the cortex be a precise copy of the retinal input. The representation of EPSPs evoked via metabotropic glutamate receptors would act like a low pass temporal filter, and temporal information would be lost. In this regard, the activation of metabotropic receptors, because of their long time course, would seem to provide a poor substrate for effective information transfer but an excellent one for modulation. In contrast, the sustained PSPs evoked by nonretinal inputs to relay cells means that sufficient activation of these inputs will provide rather lengthy effects on membrane potential, and thus excitability, of the relay cell. In this way, these nonretinal inputs will serve to modulate the gain or effectiveness of retinogeniculate transmission. Other consequences of these nonretinal inputs can be seen in their control of voltage gated ion channels, and a good

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Circuit Properties of Thalamic Relay Neurons

example of this is their ability to control response mode— burst or tonic—of the relay cell. Control of Response Mode Recall from the previous description of T channel behavior that inactivation or de-inactivation requires a change in membrane potential to be sustained for at least roughly 100 msec. PSPs activated via ionotropic receptors are poorly suited to this, because they are too brief. Thus, for instance, an AMPA- or nicotinic-mediated EPSP is too brief to inactivate many T channels for a cell in burst mode, and a GABAA-mediated IPSP is too brief to relieve many T channels of their inactivation for a cell in tonic mode. However, the sustained PSPs of metabotropic receptors, lasting 100s of msec, are ideally suited to control response mode. Thus activation of metabotropic glutamate receptors via layer 6 corticogeniculate input or muscarinic receptors via parabrachial input produces an EPSP sustained enough to inactivate T channels and switch relay cell firing mode from burst to tonic; likewise, activation of GABAB receptors produces an IPSP sustained enough to de-inactivate T channels and switch relay cell firing mode from tonic to burst. Further Details of Effects of Corticogeniculate or Parabrachial Inputs Another consequence of the postsynaptic receptor is that it often determines whether a given neurotransmitter acts in an excitatory or inhibitory manner. In the case of the circuitry shown in Figure 10.10, cholinergic inputs excite relay cells while they inhibit interneurons and reticular cells. This is achieved by two types of muscarinic receptors (McCormick, 1992). Those on relay cells are mostly of the M1 type, and activation of M1 receptors leads to closing of K channels, reducing the outward leakage of K ions and thereby resulting in an EPSP. Those on the GABAergic cells are mostly of the M2 type, and activation of M2 receptors leads to opening of K channels, increasing the outward leakage of K ions and thereby resulting in an IPSP. This allows cholinergic inputs to the thalamus to perform a neat trick: they directly excite relay cells while they indirectly disinhibit them. As a result, increasing activity of parabrachial neurons leads to more depolarized relay cells, making them more responsive to retinal input and biasing them toward the tonic firing mode. Indeed, parabrachial cells become more active with increasing vigilance (Datta & Siwek, 2002; Steriade & Contreras, 1995), and more vigilance is associated with increased retinogeniculate transmission and a shift toward tonic firing (Massaux & Edeline, 2003; Ramcharan et al., 2000; Swadlow & Gusev, 2001).

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211

The situation with corticogeniculate inputs is more complex. These inputs to relay cells and local GABAergic cells are all excitatory, and thus the circuitry shown in Figure 10.10 suggests that corticogeniculate input directly excites relay cells but indirectly inhibits them, and it is not clear from this perspective what purpose this serves or what effect corticogeniculate input has on the firing mode of relay cells. However, as Figure 10.11 indicates, Figure 10.10 may be misleading in terms of the specifics of corticogeniculate circuitry because it does not reveal important details. Figure 10.11 shows two distinct variants of this circuitry involving the thalamic reticular nucleus; one can imagine similar variants involving interneurons and, of course other variants are possible. The variant shown in Figure 10.11A is an example of classical feed forward inhibition. It might seem puzzling because increased activity leads to both depolarization and (indirect) hyperpolarization of the relay cell, with perhaps minimal effect on the relay cell’s membrane potential. This would have very little effect on T channel inactivation and thus little effect on response mode. However, the resultant increase in synaptic conductance would reduce input resistance of the relay cell, and this and other subtle effects pointed out by Chance, Abbott, and Reyes (2002) would result in a reduced retinogeniculate EPSP amplitude. In other words, this form of feedforward inhibition acts as an effective means of gain control for retinogeniculate transmission. The variant shown in Figure 10.11B has quite a different functional significance. This is no longer an example of feedforward inhibition, but instead, an active corticogeniculate axon will directly excite some relay cells and indirectly

Figure 10.11 Schematic view of different possible corticothalamic circuits involving the thalamic reticular nucleus that have quite different effects on relay cells. A: Feedback inhibitory arrangement. B: Arrangement in which activation of layer 6 cell monosynaptically excites some relay cells (e.g., cell 2) and disynaptically inhibits others (e.g., cells 1 and 3).See text for details.

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212 Thalamocortical Relations

inhibit others. In this specific example, increased activity in the corticogeniculate axon will depolarize geniculate cell 2, biasing it toward tonic firing, and hyperpolarize cells 1 and 3, biasing them toward burst firing. Evidence exists that activation of layer 6 corticogeniculate input can have dramatic effects on response mode, switching some relay cells from burst to tonic firing, and others, in the opposite direction (Wang, Jones, Andolina, Salt, & Sillito, 2006). Role of Interneurons Interneurons are particularly interesting cells because, among other properties, they have both axonal (F1) and dendritic (F2) output terminals. The axonal outputs seem to innervate both X and Y relay cells and other interneurons on proximal dendritic shafts with conventional, simple synapses. The dendritic outputs target relay X cells in complex synaptic arrangements known as triads (see Figures 10.12 and 10.13).

Figure 10.13 Schematic view of a synaptic triad. Arrows indicate direction of synaptic function, pointing from presynaptic to postsynaptic elements. The question marks indicate that the presence of the receptor indicated is unclear. Abbreviations: GABA, -aminobutyric acid; Glu, glutamate.

Triadic Circuits The F2 dendritic outputs of interneurons enter into a complex synaptic arrangement known as triads (see Figures 10.12 and 10.13). In the most common form, a retinal terminal

Figure 10.12 Electron micgrographs showing some properties of F2 terminals based on intracellular labeling with horseradish peroxidase of an interneuron in the cat’s lateral geniculate nucleus. A: F2 terminal appended to interneuron dendrite via long, thin process (arrow). B: Section through triad. A retinal terminal (R) synapses onto an F2 terminal and a relay cell dendrite (d), and the F2 terminal synapses onto the same dendrite. The arrows show the direction of the synapses, pointing from presynaptic to postsynaptic elements. Figure reassembled from Hamos et al. (1985).

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contacts an F2 terminal, and both of these terminals contact the same relay X cell, usually on a grape-like appendage (Hamos et al., 1985; Wilson et al., 1984). This would appear to be a form of simple feedforward inhibition, but a consideration of the postsynaptic receptors involved suggests a more interesting possibility. Release of GABA from the F2 terminal results in inhibition in the relay cell, and the rate of GABA release is strongly determined by the retinal input to the F2 terminal. The retinal input is glutamatergic. As noted, the retinal input to the relay cell acts via ionotropic receptors, but recent evidence (Cox & Sherman, 2000; Govindaiah & Cox, 2004) suggests that the retinal input to the F2 terminal operates mainly via metabotropic glutamate receptors (Figure 10.13). In Figure 10.13, arrows indicate the direction of synaptic function, pointing from presynaptic to postsynaptic elements. The question marks indicate that the presence of the receptor indicated is unclear. Also as noted, metabotropic receptor activation requires higher firing rates in the afferent. The implication here is that, at low firing rates, relay cells will be depolarized via the retinal input, but the feedforward circuit via the F2 terminal will not be activated, and so there will be no feedforward hyperpolarization. As the firing rate in the retinal afferent increases, more and more of the feedforward inhibition will be brought into play to offset the increasing, direct depolarization. There are two possible and related implications to this (Sherman, 2004). First, one function of this circuit is to extend the operating range of the retinogeniculate circuit.

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Drivers and Modulators 213

That is, if the retinal input fires at a high enough frequency to cause the relay cell to fire at its maximum frequency, thereby saturating its response, further increases in retinal firing cannot be represented in the relay. This triadic circuit would ensure that higher firing rates would be needed than without the circuit for the relay cell’s response to saturate. Second, this also means that as the firing rate in the retinal afferent increases, the gain of the retinogeniculate transmission is reduced, and furthermore, because the metabotropic response lasts so long, estimated to be several seconds in this example (Govindaiah & Cox, 2004), this reduced gain will continue for a period even if the retinal input reduces its firing level. Since retinal firing level generally increases monotonically with contrast in the visual stimulus, periods of higher stimulus contrast will produce a short, several-second period of reduced visual sensitivity. This phenomenon, known as contrast gain control, is a central feature of the visual system (Geisler & Albrecht, 1995; Määttänen & Koenderink, 1991; Ohzawa, Sclar, & Freeman, 1982). While there is evidence for contrast gain control having neuronal substrates in the retina and the cortex (Beaudoin, Borghuis, & Demb, 2007; Ohzawa et al., 1982; Bernardete, Kaplan, & Knight, 1992; Truchard, Ohzawa, & Freeman, 2000), this may also occur via thalamic processing (Sherman, 2004). Functioning of the Interneuron The F2 terminals are connected to each other and to the stem dendrite via long, thin processes (typically 10 m in length and 1 m in diameter; see Figure 10.12A). Modeling (Bloomfield & Sherman, 1989) suggests that, if there are not significant active processes in the membranes involved, a significant proviso, then any membrane potential changes generated in the F2 terminal (e.g., from activation of metabotropic glutamate receptors) would effectively decay before reaching the stem dendrite and thus have no discernable effects on other F2 terminals or on the cell body. This modeling further suggests that synaptic inputs that effectively control the axonal output are essentially limited to the soma itself and proximal dendrites. The hypothesis, then, is that the interneuron massively multiplexes, with an axonal output controlled in a conventional means via proximal inputs and dendritic outputs controlled locally and independently via direct inputs onto these F2 terminals (Sherman, 2004). Generality of Circuit Properties While Figures 10.10 through 10.13 refer specifically to the lateral geniculate nucleus, with minor exceptions, the principles they represent seem to be found throughout the thalamus. An important proviso is that these properties have

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been documented regarding thalamic nuclei for which sufficient information is available, but there are some that have not been much studied to date. Most of our knowledge is based on studies of thalamic nuclei that project mainly to layer 4 of the cortex, but some nuclei, such as the midline and interlaminar nuclei (see Figure 10.1) project largely to layer 1, and very little is known of their detailed cell and circuit properties.

DRIVERS AND MODULATORS A glance at Figure 10.10 reveals a common situation in brain circuitry that is often ignored or overlooked. That is, relay cells receive inputs from many different sources, but these do not act as some sort of anatomical democracy to equally affect relay cell responses. In fact, only one of these inputs, the retinal for the lateral geniculate nucleus and equivalent for other nuclei (e.g., lemniscal input for the ventral posterior nucleus and inferior collicular input for the medial geniculate nucleus), represents the actual input to be relayed to the cortex. In the case of the lateral geniculate nucleus, for example, the receptive fields of the relay cells represent the information relayed to the cortex, and these receptive fields have the same center/surround configuration as their retinal inputs but are very different from the orientation and direction selective receptive fields of layer 6 cells, not to mention the lack of clear visual receptive fields for parabrachial inputs (reviewed in Sherman & Guillery, 1996, 2006). The retinal input stands alone in terms of being the main information source to be relayed, but it also differs from nonretinal input along a number of anatomical, physiological, and pharmacological properties, and these differences extend to other thalamic nuclei. This has led to the conclusion that these form two different types of input exemplified by retinal and nonretinal input, and termed drivers (for the retinal equivalent because these provide a uniquely powerful drive of relay cells) and modulators (for the nonretinal equivalent because these chiefly modulate thalamic transmission of driver input; Sherman & Guillery, 1998). Table 10.1 summarizes these differences (reviewed in Sherman & Guillery, 1998, 2004, 2006); the 13 criteria in Table 10.1, in a roughly decreasing order of importance, are: 1. As already suggested for the lateral geniculate nucleus, drivers determine the main receptive field properties of the relay cell; modulator input does not. 2. Also as already noted, drivers activate only ionotropic receptors; modulators activate metabotropic as well as ionotropic receptors.

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214 Thalamocortical Relations TABLE 10.1 Criteria

Drivers and modulators in LGN plus layer 5 drivers

Retinal (Driver)

Layer 5 to HO (Driver) Modulator: Layer 6

Modulator: PBR

Modulator: TRN and Int

1

Determines relay cell receptive field

Determines relay cell receptive field*

Does not determine relay cell receptive field

Does not determine relay cell receptive field

Does not determine relay cell receptive field

2

Activates only ionotropic receptors

Activates only ionotropic receptors

Activates metabotropic receptors

Activates metabotropic receptors

TRN: Activates metabotropic receptors; Int:†

3

Large EPSPs

Large EPSPs

Small EPSPs

†

TRN: small IPSPs; Int:†

4

Large terminals on proximal dendrites

Large terminals on proximal dendrites

Small terminals on distal dendrites

Small terminals on proximal dendrites

Small terminals; TRN: distal; Int: proximal

5

Each terminal forms multiple contacts

Each terminal forms multiple contacts

Each terminal forms single contact

Each terminal forms single contact

Each terminal forms single contact

6

Little convergence on to target

Little convergence on to target*

Much convergence on to target

†

†

7

Very few synapses on to relay cells (5%)

Very few synapses on to relay cells (5%)

Many synapses on to relay cells (30%)

Many synapses on to relay cells (30%)

Many synapses on to relay cells (30%)

8

Often thick axons

Often thick axons

Thin axons

Thin axons

Thin axons

9

Glutamatergic

Glutamatergic

Glutamatergic

Cholinergic

GABAergic †

10

Synapses show pairedpulse depression (high p)

Synapses show paired-pulse depression (high p)*

Synapses show pairedpulse facilitation (low p)

†

11

Well localized, dense terminal arbors

Well localized, dense terminal arbors

Well localized, dense terminal arbors

Sparse terminal arbors

Well localized, dense terminal arbors

12

Branches innervate subtelencephalic targets

Branches innervate subtelencephalic targets

Subcortically known to innervate thalamus only

†

Subcortically known to innervate thalamus only

13

Innervates dorsal thalamus but not TRN

Innervates dorsal halamus but not TRN

Innervates dorsal thalamus and TRN

Innervates dorsal thalamus and TRN

TRN: both; Int: dorsal thalamus only

* †

Very limited data to date. No relevant data available.

3. Drivers evoke very large excitatory postsynaptic potentials; modulators generally evoke much smaller excitatory or inhibitory postsynaptic potentials. 4. Drivers form very large terminals on proximal dendrites; modulators usually form small terminals, and these can be on proximal or distal dendrites. 5. Each driver terminal forms multiple large synapses; each modulator terminal usually forms a single, small synapse. 6. Driver inputs show little convergence, meaning, for example, that one or a small number of retinal axons converge to innervate each geniculate relay cell; where evidence is available, modulator inputs show considerable convergence. 7. Driver inputs produce a small minority (5%) of the synapses onto relays cells; many modulator inputs produce

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8. 9. 10.

11.

larger synaptic numbers (e.g., the local GABAergic, cortical, and parabrachial modulator inputs in Figure 10.10 each produce about 30% to 40% of the synapses). Drivers have thick axons; modulators have thin axons. Drivers are glutamatergic; modulators can use a variety of neurotransmitters. Driver synapses show high release probability and paired-pulse depression; modulator synapses that have been tested so far show the opposite properties of low release probability and paired-pulse facilitation (see Figure 10.8). Driver terminal arbors are well localized with a dense array of terminals; modulator terminal arbors can be either well-localized and dense or relatively poorly localized and sparse.

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First and Higher Order Thalamic Relays 215

12. Branches of driver axons tend to innervate extrathalamic targets as well as the thalamus (e.g., many or all retinogeniculate axons branch and also innervate midbrain targets); those modulator inputs so far tested innervate the thalamus only. 13. Driver inputs innervate relay cells and interneurons in the dorsal thalamus but do not innervate the thalamic reticular nucleus; modulator inputs innervate relay cells, interneurons, and reticular cells. This driver/modulator distinction is clear not just in the lateral geniculate nucleus, but also in other thalamic relays for which sufficient information is available, such as the ventral portion of the medial geniculate nucleus (the primary auditory thalamic relay) and the ventral posterior nucleus (the primary somatosensory thalamic relay). The main point, again, is that not all anatomical pathways are functionally equivalent, and if we are to understand the functional organization of the thalamus and what it is that is being relayed, we must identify and characterize the driver input. This may also apply outside of the thalamus. This point is considered further in the next section.

FIRST AND HIGHER ORDER THALAMIC RELAYS There are two aspects of functional organization of thalamic nuclei that should be considered. One is the actual relay mechanisms, which are related to the cell and circuit properties defined earlier. The other is a determination of what, exactly, is being relayed by a given nucleus. This second functional property seems clearly defined for some nuclei, such as lateral geniculate nucleus, which relays retinal input, but until recently has not been so clear for many other nuclei, such as the pulvinar or medial dorsal nucleus. From the previous section, it should be clear that understanding this second property boils down to identifying the driver input to any particular nucleus. The recent ability to identify driver inputs to many heretofore rather mysterious nuclei, like the pulvinar or medial dorsal nucleus, has led to the further suggestion that, based on the origin of driver inputs, subcortical or cortical, thalamic nuclei can be identified as first order or higher order. Division of Thalamic Relays into First Order and Higher Order This distinction is well characterized by comparing the two main visual thalamic relays, the lateral geniculate nucleus and pulvinar (see Figure 10.14A). These two nuclei have the same general pattern of modulator inputs from local GABAergic neurons, the brain stem, and layer 6 of cortex.

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Data that have accumulated over the past few decades make it clear that the pulvinar receives its driver input from layer 5 of one cortical area and projects it to another (reviewed in Guillery, 1995; Guillery & Sherman, 2002a; Sherman & Guillery, 2006). This means that all thalamic nuclei receive a modulator projection from layer 6 that is mostly feedback but that some in addition receive a driver projection from layer 5 (instead of a subcortical driver, such as from the retina) that is feedforward (Van Horn & Sherman, 2004). As indicated in Figure 10.14A, this feedforward layer 5 input places these higher order thalamic nuclei in the middle of a cortico-thalamo-cortical route of information flow. The main sensory thalamic relays can be divided into first order and higher order. In addition to the lateral geniculate nucleus (first order) and the pulvinar (higher order) for vision, there is the ventral posterior nucleus (first order) and the posterior nucleus (higher order) for somesthesis, and the ventral division of the medial geniculate nucleus (first order) and its dorsal division (higher order) for hearing (reviewed in Guillery, 1995; Guillery & Sherman, 2002a; Sherman & Guillery, 2006). Other thalamic relays have also been so identified: The medial dorsal nucleus is mostly or wholly a higher order relay innervating prefrontal cortex; the ventral anterior and lateral nuclear complex, which innervates motor cortex, includes first order circuits based on cerebellar inputs and higher order circuits based on inputs from layer 5 of the motor cortex; and so on. While not all of the thalamus has been so identified yet as regards this division, it seems clear that most of the thalamic volume is involved in higher order relays. There is an important proviso to this, namely, that while first order nuclei seem fairly purely first order, those designated as higher order may have first order components as well. For instance, while most of the pulvinar receives layer 5 input from various regions of the visual cortex and thus appears to participate as a higher order relay in a corticothalamo-cortical circuit, parts of pulvinar are innervated by the superior colliculus. It is not entirely clear whether this colliculo-pulvinar pathway is a driver or modulator (or something else heretofore not described), but there is some anatomical evidence that at least some of the colliculopulvinar terminalis are quite large, suggesting that they are drivers (Kelly, Li, Carden, & Bickford, 2003). If so, then the pulvinar would represent a mixture of mostly higher order relays with some first order relays. Likewise, the posterior medial nucleus, which receives input from layer 5 of somatosensory cortex, also receives some direct spinothalamic input, but it is not known whether this latter input is a driver or modulator. A similar proviso exists for the dorsal portion of the medial geniculate nucleus, which we defined previously as a higher order nucleus: this receives input from the “belt” region of the inferior colliculus, but again,

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Figure 10.14 Schematic diagrams showing organizational features of first and higher order thalamic nuclei. A, B: Distinction between first order and higher order thalamic nuclei. A first order nucleus (A) represents the first relay of a particular type of subcortical information to a first order or primary cortical area. A higher order nucleus (B) relays information from layer 5 of one cortical area to another. This relay can be between first and higher order cortical areas as shown or between two higher order cortical areas. C: Role of higher order thalamic nuclei in cortico-cortical communication via cortico-thalamo-cortical circuits involving a

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projection from layer 5 of cortex to a higher order thalamic relay to another cortical area. As indicated, the role of the direct corticocortical projections, driver or modulator or other, is unclear. Note in A–C that the driver inputs, both subcortical and from layer 5, are typically from branching axons, the significance of which is elaborated in the text. Abbreviations: FO, first order; HO, higher order; LGN, lateral geniculate nucleus; MGNv or MGNd, ventral or dorsal division of medial geniculate nucleus; PO, posterior nucleus; TRN, thalamic reticular nucleus; VP, ventral posterior nucleus.

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it is not known if this is a driver or modulator. Finally, the medial dorsal nucleus, which has much layer 5 input from the prefrontal cortex also has input from the superior colliculus, and although this latter input is described as if it were a driver (Sommer & Wurtz, 2004a, 2004b), insufficient evidence exists as to its identity. Given the possibility that some thalamic nuclei defined here as higher order may also have first order components operating in parallel, we refer below to first order and higher order thalamic “relays” rather than “nuclei.” Implications for Cortical Functioning The concept of higher order relays offering a cortico-thalamo-cortical route for information processing should be seen in the context of the traditional view best expressed by Van Essen and colleagues (Felleman & Van Essen, 1991; Van Essen, Anderson, & Felleman, 1992), namely, that cortical areas communicate with one another via a plethora of direct cortico-cortical connections. In the visual cortex of rhesus monkeys, for instance, this view states that information is brought to the primary visual cortex by the lateral geniculate nucleus, and once it reaches the cortex, it stays there, being processed by the 30-odd visual areas of the cortex through a series of several parallel feedforward routes involving 4 or 5 hierarchical levels. This scheme also has feedback and lateral connections, and the direction of all of these pathways are defined by criteria dependent mostly on the laminar pattern of the cortico-cortical terminations. The cortico-thalamo-cortical pathways may be seen as a compl