Neuroimaging and Human Genetics

NEUROIMAGING AND HUMAN GENETICS

Georg Winterer,*y Ahmad R. Hariri,z David Goldman,y and Daniel R. Weinberger* *Genes, Cognition and Psychosis Program, National Institute of Mental Health National Institutes of Health, Bethesda, Maryland 20892 y Laboratory of Neurogenetics, National Institute of Alcohol Abuse and Alcoholism National Institutes of Mental Health, Bethesda, Maryland 20892 z Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania 15213

I. Introduction II. Historical Perspective III. General Issues A. Why Study Genes? B. Why Neuroimaging? C. Neuroimaging and Genetics—Basic Principles IV. Heritability A. Heritability of Brain Structure B. Heritability of Brain Function V. Application of the Principles A. Dementia B. Mental Disability C. Schizophrenia D. Mood and Anxiety Disorders VI. Conclusions References

The past few years have seen a rapid expansion of the application of neuroimaging tools to the investigation of the genetics of brain structure and function. In this chapter, we will (1) highlight the most important steps during the historical development of this research field, (2) explain the major purposes of using neuroimaging in genetic research, (3) address methodological issues that are relevant with regard to the application of neuroimaging in genetic research, (4) give an overview of the present state‐of‐research, and (5) provide several examples of how neuroimaging was successfully applied. I. Introduction

Identifying the biological underpinnings that contribute to brain structure and complex cognitive and emotional behaviors is paramount to our understanding of INTERNATIONAL REVIEW OF NEUROBIOLOGY, VOL. 67 DOI: 10.1016/S0074-7742(05)67010-9

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how individual diVerences in these behaviors emerge and how such diVerences may confer vulnerability to neuropsychiatric disorders. Advances in both molecular genetics and noninvasive neuroimaging have provided us with the tools necessary to address these questions on an increasingly sophisticated level (Hariri and Weinberger, 2003). With completion of a rough draft of the reference human genome sequence (Lander et al., 2001; Venter et al., 2001), a major eVort is underway to identify common variations in this sequence that impact gene function and subsequently to understand how such functional variations alter human biology. Because approximately 70% of all genes are expressed in the brain, many of these functional gene variations will account for interindividual variability of brain structure and function. A variety of neuroimaging methods have the capacity to assay gene function in the brain. These methods are complementary with regard to their ability to characterize diVerent aspects of brain structure and function and currently include structural magnetic resonance imaging (MRI), functional magnetic resonance imaging (f MRI), magnetic resonance spectroscopy (MRS), positron emission tomography (PET), single photon emission tomography (SPECT), as well as the two related techniques electroencephalography (EEG) and magnetoencephalography (MEG). In the near future, this list of tools will probably be extended by additional imaging methods such as diVusion tensor imaging (DTI). In this chapter, we will describe (1) the conceptual basis for, and potential of, using neuroimaging in human genetic research; (2) propose guiding principles for the implementation and advancement of this research strategy; and (3) highlight recent studies that exemplify these principles.

II. Historical Perspective

The idea that human brain function might be influenced by genetic factors has a long tradition and can be traced back to Davis and Davis (1936) who studied brain function (i.e., the electroencephalogram [EEG]) of twins. They visually examined the frequency characteristics of resting EEG in eight monozygotic (MZ) twins and compared them with repeatedly conducted EEG recordings of 31 controls. In essence, they found a striking similarity of EEG between MZ twins at any point in time that was comparable to the similarity of recordings from the control individuals at multiple time points. Over the next few years, this finding was substantiated by Loomis et al. (1936), Raney (1937), and, in particular, Lennox et al. (1945), who conducted the first large‐scale EEG study in twins. A meticulous methodological basis to the investigation of the heritability of human EEG was then developed by Vogel (1958), who was the first to adopt both a geneticist’s and an electroencephalographer’s point of view in his work. Subsequently, the knowledge base on the genetic foundation of particular EEG

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patterns was rapidly expanded with contributions from numerous investigators who not only conducted twin studies but also described brain function abnormalities in subjects with chromosomal aberrations (for a review see Vogel [2000]) or in patients with mostly rare neuropsychiatric disorders with monogenic Mendelian inheritance (for a review see Naidu and Niedermeyer [1993]). A remarkable observation of some of these studies was that even clinically unaVected subjects with familial risk for a certain illness showed abnormal EEG patterns. For instance, Patterson et al. (1948) found bilateral groups of slow EEG waves in subjects at risk for Huntington’s disease, a rare autosomal dominant progressive disorder of motor, cognitive, and psychiatric disturbances. These findings had far‐reaching consequences, because they provided strong evidence of a higher sensitivity of measures of brain function compared with clinical symptom assessment and because this kind of investigation helped to understand the pathophysiology of this particular genetic illness. With the availability of modern computer algorithms, the entire field entered a new era. Now, the estimation of the heritability of particular EEG patterns or the description of the pathophysiology of genetic disorders can rely on exactly quantified EEG signals, and it is also possible to determine the genetic impact on event‐related potentials (ERPs) (Fig. 1) (for a comprehensive reviews of the literature, see van Beijsterveldt and Boomsma [1994]; Vogel [2000]; and van Beijsterveldt and van Baal [2002]). In addition, EEG/ERP scalp surface maps were increasingly used for genetic investigations to take advantage of the spatial information in the brain signal (Trubinikov et al., 1993; van Beijsterveldt et al., 1998b; Winterer et al., 2003), and this methodological approach was recently advanced by applying electromagnetic source analyses in realistic head models (Fig. 2) (Winterer et al., 2000a). Thus, electrophysiological phenotypes that were used for genetic analyses gradually adopted a ‘‘neuroimaging approach’’ in the narrow sense as more sophisticated data analysis techniques became available. These new tools turned out to be useful not only for the estimation of the heritability of certain brain operations (outlined later) but also improved the potential ability to detect subjects at risk for genetic disorders. In particular, the quantification of certain electrophysiological features now allowed phenotyping of subjects who are more or less at risk for polygenic disorders. As opposed to monogenic disorders, polygenic disorders are common in the general population and usually interact more strongly with environmental factors. In these complex polygenic disorders, phenotype expression is governed by the action of many genes, and, as a result, phenotype expression is likely characterized by a Gaussian distribution. In other words, subjects at risk for a polygenic disorder usually cannot simply be described by the presence or absence of a ‘‘qualitative’’ phenotype—as in subjects with monogenic disorders—but by the presence of a ‘‘quantitative’’ phenotype. Examples in which this approach of quantitative electrophysiological phenotyping has been most successfully adopted are the

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FIG. 1. Examples of the P300 targets measured on P3, Pz, and P4. In both figures is the P300 target depicted of the youngest (line) and oldest (line with dots) of two MZ twin pairs (A) and two DZ twin pairs (B). With permission, from Van Beijsterveldt et al. [1998a]).

two polygenic disorders alcoholism (for a review, see Porjesz and Begleiter [2003]) and schizophrenia (Freedman et al., 1999; Winterer et al., 2003). During the past 30 years, neuroimaging modalities such as computed tomography (CT) and particularly MRI were increasingly used for the purpose of genetic studies. The first genetic study using CT was conducted by Weinberger

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FIG. 2. First genetic investigation based on a tomographic event‐related potential analysis with LORETA (low‐resolution electromagnetic tomography analysis). Association of GABAA‐
2 polymorphism and prefrontal/temporal activation in 95 healthy subjects. The analysis was preceded by a more ‘‘robust’’ principal component analysis of second order (test–retest stability: Cronbach’s  > 0.9), which was based on ERP‐measures taken from the entire electrode grid across the scalp and diVerent task conditions (F ¼ 3.8, p ¼ 0.02). dbSNP/HGVbase: SNP001493976. Accession No.: AF165124 (OMIM). Adapted, with permission, from Winterer et al. [2000a]).

et al. (1981), who investigated the possibility that lateral cerebral ventricular size may be under genetic control. They compared CT scans of 17 healthy siblings from 7 normal sibships, as well as 10 patients with chronic schizophrenia and 12 of their siblings without schizophrenia. In essence, they found a trend for a correlation of ventricular size between siblings in the healthy sibships but not in schizophrenic sibships. As expected from earlier CT investigations of patients with schizophrenia (Johnstone et al., 1976; Weinberger et al., 1979), the patients with schizophrenia had the largest ventricles, exceeding the normal range in seven cases. In contrast, their discordant were all within the normal range;

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however, their ventricles were larger than those of the controls. Accordingly, the authors suggested that a genetic component may determine ventricular size and that some genetic predisposition to larger ventricles exists in families of patients with schizophrenia but that illness‐related state processes may also contribute to ventricular enlargement. In the subsequent years, several twin studies reported abnormal brain structures in other illnesses such as birth defects involving agenesis of corpus callosum (Atlas et al., 1988; Pascual‐Castroviejo and Izquierdo, 1982). In healthy twins, a strong similarity of corpus callosum size using MR technology was first described by Oppenheim et al. (1989). In 1997, Barteley et al. found that cortical gyral patterns are more similar in MZ than in DZ twins (Fig. 3). Since then, numerous (in part large‐scale) twin studies have been conducted that established heritability for a variety of brain structures, which is now

FIG. 3. Schematic illustration of cross‐correlation analysis. The left‐hand columns show the cortical renderings involved in each comparison; the right‐hand column contains perspective views of the 2‐D cross‐correlation matrices resulting from each comparison. The top row shows the analysis of lateral cortical renderings from 3D MRI of a rhesus monkey. Before cross‐correlation, the right hemisphere rendering is ‘‘flipped’’ to face in the same direction as the left. In the rightmost column, a perspective view shows the full cross‐correlation matrix whose peak value indicates a high degree of similarity (R ¼ 0.70). The lower portion is an example of applying this process to a pair of twins (middle) and a pair of unrelated subjects (lower). Visual scrutiny reveals more similarities between the gyral patterns of the twins than between the unrelated brains; this diVerence in the degree of similarity is reflected in the peak values (R ¼ 0.38 twins versus R ¼ 0.21 unrelated) of the cross‐correlation maps. With permission, from Bartley et al. [1997]).

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considered to be particularly high for entire brain and cortical and gray matter volume (see later). Other investigators focussed on the description of structural brain abnormalities in subjects with chromosomal aberrations (for a review, see Kumar et al. [1992]) and with rare monogenic disorders of known mode of inheritance. For instance, CT and structural MRI scans have demonstrated decreased basal ganglia volumes in patients with Huntington’s disease ( Jernigan et al., 1991; Sax et al., 1989) and also in clinically unaVected subjects at risk for the disease (Aylward et al., 2004). In the clinically aVected patients, volume reductions can be substantial. Harris et al. (1992) described the greatest volume reduction in the putamen (>50%), enabling 100% separation of patient and control groups when corrections were made for overall brain size. A considerable number of structural imaging studies also accumulated over the years that addressed the question of abnormal brain volumes in complex polygenic illnesses like Alzheimer’s disease (AD), schizophrenia, or alcoholism. It became rapidly apparent from these studies that structural abnormalities are found in patients with these illnesses and also to some extent in their unaVected relatives (e.g., Fox et al., 1996; McDonald et al., 2004; Rosenbloom et al., 2003; Shenton et al., 2001; Suddath et al., 1990). In general, however, a broad overlap of measurements between groups of patients and control groups, which is consistent with the notion of a Gaussian distribution of phenotype expression in polygenic‐complex disorders, has been observed. Functional neuroimaging using SPECT, PET, and, more recently, f MRI increasingly plays an important role in genetic studies. Early PET studies reported diVerences of cerebral metabolism in twins discordant for pathological conditions such as AD (Luxenberg et al., 1987) or schizophrenia (Weinberger et al., 1992). With the increasing use of radioligands for assaying receptor availability in the brain, researchers also started to investigate the genetic impact on receptor binding. In 1996, WolV et al. described for monozygotic twins discordant for Tourette syndrome severity diVerences in D2 dopamine receptor binding in the head of the caudate nucleus, which predicted diVerences in phenotypic severity (r ¼ 0.99). More recently, an increasing number of f MRI studies have been published reporting diVerences of brain activation in twins who were discordant for certain neuropsychiatric illnesses or handedness (Lipton et al., 2003; Sommer et al., 2002; 2004; Spaniel et al., 2003). Over many years, the most substantial contribution of functional neuroimaging, however, was the description of abnormalities in functional neuroanatomy of neuropsychiatric disorders including those with a genetic background. For example, SPECT and PET studies were able to demonstrate decrements in striatal functioning of patients with Huntington’s disease (Hasselbalch et al., 1992; Kuhl et al., 1982), which was well in accordance with the findings obtained by structural MRI investigations. A number of functional neuroimaging studies also investigated presymptomatic at‐ risk subjects for Huntington’s disease. In general, these studies found that the age

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of at‐risk subjects was an important factor (Ichise et al., 1993; Maziotta et al., 1987). Remarkably, one study (Harris et al., 1999) found that during the years before the onset of clinical symptoms, SPECT perfusion in the putamen starts to be abnormal at an earlier time than volumetric measures of this brain structure, which indicates that functional measures might be more sensitive than structural measures. In the meantime, innumerable functional neuroimaging studies have also been conducted on complex polygenic neuropsychiatric conditions such as AD, schizophrenia, or alcoholism. As with the electrophysiological and structural MRI findings, there is generally a considerable overlap of the obtained measurements with those in healthy control subjects—even more so when clinically unaVected subjects at risk for one of these disorders are investigated. On the other hand, with increasingly advanced data analysis techniques and the use of multivariate instead of univariate designs, it has turned out that it is possible to overcome this problem of overlap at least to some extent. For instance, a recent multicenter study with [18F]‐fluorodeoxy‐D‐glucose (FDG) PET was able to diVerentiate patients with AD from healthy controls relatively well (Herholz et al., 2002a). The study was composed of 110 normal controls and 395 patients, and FDG uptake was measured in the posterior cingulate, temporoparietal, and prefrontal association cortex. With these three variables for diVerentiation, 93% sensitivity and specificity was provided for distinction of mild to moderate AD from normal subjects and 84% sensitivity at 93% specificity for detection of very mild AD. Within the past 15 years, the molecular revolution has brought a profound change to the entire field of human genetics and is continuing to do so. This also has had a considerable impact on genetic studies using structural or functional phenotypes of the brain (‘‘endophenotyping’’ or ‘‘intermediate phenotyping’’). Now, it is feasible to assay endophenotypic changes that are associated with variations within specific chromosomal locations (markers) or within specific genes. In 1989, Delgado‐Escueta et al. conducted the first endophenotype study of this kind and reported linkage of the Bf‐HLA locus marker in chromosome 6p21.3 to the clinical manifestations of juvenile myoclonic epilepsy ( JME) and its associated EEG traits (e.g., epileptic spikes). Evidence of linkage, however, was stronger (lod score > 3) when clinically asymptomatic family members with similarly abnormal EEG traits were counted as ‘‘aVected.’’ Since this landmark study has been published, a number of marker‐based linkage studies have taken advantage of the high sensitivity of EEG measures to detect otherwise asymptomatic at‐risk subjects within families of symptomatic patients (probands). In particular, numerous studies have been undertaken to explain the genetics of a variety of epileptic disorders. However, the same approach has also been successfully adopted in genetic studies on alcoholism (Porjesz et al., 2002) using phenotypes such as EEG beta‐frequency enhancement or in schizophrenia

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studies (Blackwood et al., 2001; Freedman et al., 1997) with the event‐related potentials P50 or P300 as endophenotypes, which are both known as being generated in specific structures within temporal lobe area. For many years, such family‐based linkage analyses using genetic markers at specific loci of the chromosomes, which are thought to be in the vicinity of certain candidate genes, have been the preferred strategy to study simple and complex genetic diseases. However, as the base sequence of an increasing number of genes became known and particularly because a complete draft of the human genome sequence became available (Venter et al., 2001), researchers started to directly investigate the impact of sequence variations within specific candidate genes (genomics). Once again, research on Huntington’s disease played a prominent role in neuropsychiatric research. In 1993, a six‐team international research group discovered that Huntington’s disease is caused by excessive and unstable repeating of the DNA bases CAG (trinucleotide repeats) in the Huntington gene on chromosome 4 (The Huntington’s Disease Collaborative Research Group, 1993). The number of repeats is critical for the clinical penetrance of the illness, and some subjects with less than 40 repeats may remain clinically asymptomatic until old age. The discovery of this mutation made it possible to directly detect presymptomatic subjects, to estimate their likely age of onset of illness, and to investigate these at‐ risk subjects with neuroimaging (Harris et al., 1999). For instance, the number of CAG repeats was found to correlate with the degree of neurodegeneration (i.e., with an individual’s striatal N‐acetyl‐aspartate [NAA] loss and lactate increase) with correlation coeYcients of 0.8 and 0.7, respectively ( Jenkins et al., 1998). In contrast, earlier studies without knowledge of the genotype had to take an indirect approach and to investigate a number of family members of a clinically aVected patient under the assumption that at least some of them are becoming ill at a later point in time. During the past few years, this way of testing for association between specific gene variants on one hand and phenotypic changes on the other hand has become the predominant research strategy in human genetics as exemplified in Fig. 4.

III. General Issues

A. WHY STUDY GENES? Genes represent the ‘‘go’’ square on the Monopoly board of life. They are the biological toolbox with which one negotiates the environment (Hariri and Weinberger, 2003a). Although most human behaviors cannot be explained by genes alone, and certainly much variance in aspects of brain structure and function will not be genetically determined, variations in genetic sequence that

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FIG. 4. EVect of BDNF val66met genotype on in vivo hippocampal f MRI response. (A) Brain map showing locales where BDNF genotype groups diVered in blood oxygenation, an indirect measure of neuronal activity, measured with f MRI during a working memory task. Regions marked in red are groups of voxels (‘‘activation clusters’’) where subjects with the val/met genotype showed abnormal hippocampal activation and were significantly diVerent when compared with val/val subjects. The statistical results are rendered on a canonical averaged T1 brain image and localized according to the standard 3D stereotactic space of Talairach and Tournoux. The maximally activated voxels are: right hippocampus (t ¼ 3.77, p < 0.01, cluster size (k) ¼ 25, 3D coordinates: 26–22–12); left hippocampus (t ¼ 2.39, p ¼ 0.02, k ¼ 10, –38–5–12). Inset, these activation clusters are rendered on a canonical averaged smoothed 3D rendered brain. Color BAR ¼ t values. R ¼ right hemisphere. (B) BDNF genotype eVect on hippocampal f MRI response in a second independent cohort. The activation clusters are those areas where BDNF val/met subjects again showed abnormal activation of bilateral hippocampi locales during the 2‐back working memory task and were significantly diVerent compared with val/val subjects. f MRI data rendered in the same manner as in (A) ( p < 0.05, cluster size > 8). Two clusters were identified in the left hippocampus (–30–35–16, t ¼ 2.49, p < 0.01, k ¼ 40 and –30–14–13, t ¼ 2.45, p ¼ 0.01, k ¼ 13) and one in the right hippocampus (28–31–2, t ¼ 2.55, p ¼ 0.01, k ¼ 37). Adapted, with permission, from Egan et al. [2003]).

impact gene function will contribute some variance to these more complex biological phenomena. This conclusion is derived implicitly from the results of twin studies that have revealed heritabilities of 40–70% for various aspects of cognition, temperament, and personality and the recognition that these human characteristics are genetically correlated with brain structure and function, which themselves show heritabilities in about the same range (Winterer and Goldman, 2003). Genes are thought to have a considerable impact on all levels of biology. In the context of disease states, particularly neuropsychiatric disorders, genes not only transcend phenomenological diagnosis they also represent mechanisms of disease. Moreover, genes oVer the potential to identify at‐risk individuals and biological pathways for the development of new treatments. In many major

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psychiatric illnesses such as schizophrenia or bipolar disorder, genes seem to be the most relevant risk factors that have been identified across populations, and the lion’s share of susceptibility to these disorders is accounted for by inheritance (Moldin and Gottesman, 1997). Although the strategy for finding susceptibility genes for complex disorders by traditional linkage and association studies may seem relatively straightforward (albeit not easily achieved), developing a comprehensive understanding of the mechanisms by which such genes act and increase biological risk is a much more daunting challenge. How does a gene aVect brain structure and information processing with regard to certain personality traits or cognitive abilities, and how does it increase risk for a neuropsychiatric disorder? How many genes contribute to a particular complex behavior, clinical symptom, or disease? What genetic overlap exists across behaviors, symptoms, and diseases? How large are the eVects of candidate genes on particular brain functions? Most recently, the ‘‘candidate gene association’’ approach has been a particularly popular strategy for attempting to answer these questions. Genetic association is a test of a relationship between a particular phenotype and a specific allele of a gene. This approach usually begins with selecting a biological aspect of a particular condition or disease, then identifying variants in genes thought to impact on the candidate biological process, and next searching for evidence that the frequency of a particular variant (‘‘allele’’) is increased in populations having the disease or condition. A significant increase in allele frequency in the selected population is evidence of association. When a particular allele is significantly associated with a particular phenotype, it is potentially a causative factor in determining that phenotype. There are, however, caveats to the design and interpretation of genetic association studies. Among them are linkage disequilibrium and ancestral stratification, issues that have been discussed in detail elsewhere (Emahazion et al., 2001). Another caveat is related to the question of whether a particular genetic variation observed in association studies is actually of major relevance to a distinct human condition. That is, how, if at all, do associations with laboratory measures translate to daily functioning and well‐being?

B. WHY NEUROIMAGING? Traditionally, the impact of genetic variations on human behavior and disease has been examined using indirect assays such as personality questionnaires, neuropsychological batteries, or symptom‐based diagnostic categories. Although several of these studies reported significant associations between specific genetic variations and a particular behavior or diagnosis, their collective results have been weak or inconsistent in many cases (Malhotra and Goldman, 1999). This is not surprising given the interindividual variability and because the

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used assays and diagnostic categories are frequently imprecise, vague, and prone to subjectivity error. As a result, it has been necessary to use very large samples, often exceeding several hundred subjects, to identify even small gene eVects (Glatt and Freimer, 2002). In addition, behavioral probes and neuropsychological tests allow for the use of alternative task strategies by diVerent individuals that may obscure potential gene eVects on the underlying neural substrates meant to be engaged by the tests. Because the structure and response of brain regions subserving specific cognitive and emotional processes may be more objectively measurable, functional genetic variations may have a more robust impact at the level of brain than at the level of behavior and clinical symptoms. Thus, functional genetic variations weakly related to behaviors and, in an extended fashion, neuropsychiatric syndromes may be more strongly related to the structure and function of neural systems involved in processing sensorimotor, cognitive, or emotional information in brain. The potential for marked diVerences at the neurobiological level in the absence of significant diVerences in behavioral and clinical measures underscores the need for the application of direct assays of brain structure and function that have higher sensitivity. On a very practical level, another advantage of neuroimaging has recently emerged and is related to the problem that arose from the increasing number of positive genetic associations with certain behavioral or disease states that have been recently reported. Even when functionality has been established by in vitro experiments and/or transgenic animal studies for these gene variations that have been found to show significant association, it frequently remains unclear which of these genetic variations is actually relevant in humans. Given the enormous expenses that are required to develop a new drug, a preselection among the potential molecular targets is needed that should be based on positive answers to three questions: (1) Is functionality for any particular genetic variation likely in humans? (2) If yes, is the direction of the biological eVect plausible and consistent with existing data? (3) If yes, is the biological eVect relevant in humans with regard to eVect size and pathological conditions? These questions can hardly be answered suYciently by traditional research strategies alone. The ‘‘validation of relevance in humans’’ requires additional tools, such as neuroimaging, that enable us to measure a genetic eVect on the biological level in humans. C. NEUROIMAGING

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GENETICS—BASIC PRINCIPLES

1. Selection of Candidate Genes The direct implication of heritability of brain structure and function is that functional alleles inherited from parent to child influence brain structure and function. However, the complexity of molecular genetic mechanisms that could

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potentially be involved in human brain structure and function is overwhelming and, at first glance, might suggest that gene identification would fail. Fortunately, this is an empirical question. There are two main levels of complexity. The first is the sheer number of genes expressed in any region of the brain being involved in neurodevelopment and synaptic organization as well as presynaptic and postsynaptic neurotransmission plus secondary neuronal downstream eVects and neuronal/glial exchange. The second level of complexity is the linear and nonlinear gene–gene and gene–environment interactions within the context of cellular compartmentalization and neuronal networks. The complexity leads to genocopies in which entirely diVerent genotypes (Fig. 5) lead to the same

FIG. 5. Schematic illustration of the complexity of the molecular genetics of cognitive function. Major genes interact with modifying genes, random noise, and environmental influences giving rise to biological phenotypes and ultimately cognitive phenotypes. With permission, from Winterer and Goldman [2003]).

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functional pattern, and phenocopies in which diVerent environments (environtypes) do the same. In addition, it would not be surprising if epigenetic phenomena, such as genomic imprinting, were found to be operative in the control of expression or degree of expression of genes aVecting brain function. For instance, prefrontal function is critical in social interactions and social behaviors that diVer in their degree of selective advantage/disadvantage to the transmitting parent, maternal versus paternal. It is well known, for example, that oVspring behaviors may enhance the reproductive potential of the mother, but the same behavior may not contribute a commensurate advantage to the reproductive potential of the father. It is this complexity that led Venter et al. (2001) to the suggestion that mathematical models, borrowed from complex system theory, may be beneficial in decoding genetic information derived from the first draft of the human genome. In practice, the application of neuroimaging techniques toward the study of genetic eVects should start when studying gene eVects on behavior or clinical symptoms would also start (i.e., from well‐defined functional polymorphisms). The genetic variation in such genes should have already been associated with specific physiological eVects at the cellular level, and their impact should have been described in distinct brain regions and brain circuits in animal experiments. Preferentially, data from post mortem studies (e.g., gene expression) also should be available. Imaging paradigms can then be developed to explore their eVects on brain structure and information processing in both normal and impaired human populations (translational neuroscience). Short of well‐defined functional polymorphisms, candidate genes with identified single nucleotide polymorphisms (SNPs) or other allele variants in coding or promoter regions with likely functional implications (e.g., nonconservative amino‐acid substitutions or missense mutation in a promoter consensus sequence) involving circumscribed neuroanatomical systems would also be attractive substrates. The investigation of genes and variations without well‐established structural or functional implications in brain, however, necessarily requires greater caution not only in the design of imaging tasks but also in the interpretation of diVerential brain responses or variations in brain structure. However, the relative rapidity with which functional eVects at the systems level can be identified and replicated will lead to an increasing shift in the value imaging genetics research will have in informing and directing molecular and cellular dissection of specific polymorphisms. 2. Control for Nongenetic Factors The contribution of single genes to the characteristics of brain systems, although putatively more substantial than to the emergent behavioral or clinical phenomena, is still presumably small. Furthermore, typically large eVects of age,

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gender, and IQ, as well as environmental factors such as illness, injury, substance abuse, daytime measurements (e.g., drowsiness) on phenotypic variance can easily obscure these small potential gene eVects. Because association studies with neuroimaging are susceptible to population stratification artefacts, as in any case– control association study, ethnic matching is also potentially critical. Thus, the identification and contribution of genetic variation to specific phenotypes should be limited to studies in which other potential and contributing factors are carefully matched across genotype groups. In the best of all worlds, heritability and its interaction with environment should also be known before any imaging parameter is used for genetic studies, because this allows an educated guess on whether a particular imaging parameter is suitable for genetic studies at all. If the imaging protocol involves performance of a task, the groups should be carefully matched for level of performance, or, at least, any variability in performance should be considered in the analysis and interpretation of the imaging data. This is because task performance and imaging responses are linked pari passu, and systematic diVerences in performance between genotype groups could either obscure a true gene eVect or masquerade for one. 3. Task Selection The past 5 years have been witness to a tremendous proliferation of functional neuroimaging studies and, with them, behavioral tasks designed specifically for this experimental setting. Many of these are modified versions of classic neuropsychological (e.g., Wisconsin Card Sorting Task; Axelrod [2002]) or neurophysiological tests (e.g., Oddball Task; Linden et al. [1999]) designed to tap neural systems critical to particular behaviors. More recent paradigms have emerged that focus on interactions of specific behaviors and disease states as these questions have become newly accessible with noninvasive imaging (e.g., the emotional Stroop and OCD [Whalen et al., 1998]). Because of the relatively small eVects of single genes in complex polygenic brain responses that are associated with certain behavioral traits, even after having controlled for non‐genetic and other confounder variables, imaging tasks must maximize sensitivity and interferential value. Because the interpretation of potential gene eVects depends on the validity of the information‐processing paradigm, it is best to select well‐characterized paradigms that are eVective at engaging specific brain regions and systems, that have suYcient test–retest stability and that produce robust signals in every individual while showing variance across individuals and for which heritability has been established by twin studies (see later). In short, imaging genomics studies are probably not the appropriate venue to design and test entirely new functional tasks, although it will be critical in the future to continue to develop new phenotypes that eventually permit a deeper insight into brain processes that are under genetic control.

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IV. Heritability

The most common way for deciding whether, and to what extent, interindividual variation in a certain phenotype is caused by genetic variation is the study of MZ and DZ twins, their similarities and diVerences. Because DZ twins are thought to be aVected largely by the same environmental diVerences as MZ twins but to have only one half of their genes in common by descent, they are used as suitable controls (Vogel, 2000).

A. HERITABILITY

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BRAIN STRUCTURE

The question regarding the degree or extent to which the size of the entire brain or of gray and white matter compartments is under genetic control has been well investigated. In contrast, little is known about the heritability of deeper brain structures, including the hippocampus, brainstem, cerebellum, and midbrain. Heritability of the entire brain, as well as gray and white matter volume, is substantial. Derived in vivo by MRI, Pearson’s R and intraclass correlations between MZ twins range from 0.6–0.9 for these brain quantities and heritabilities have been estimated as 0.90–0.95 (Baare´ et al., 2001; Bartley et al., 1997; Bonan et al., 1998; Geschwind et al., 2002; Lohmann et al., 1999; Pennington et al., 2000; PfeVerbaum et al., 2001, 2004; Posthuma et al., 2002; Scamvougeras et al., 2003; Thompson et al., 2001; Todd et al., 1999; White et al., 2002; Wright et al., 2002). There is some evidence that heritability is higher for left hemispheric volumes, and this eVect was found most pronounced in the frontal and temporal lobe (Geschwind et al., 2002; Tramo et al., 1995). In addition, shared environmental influence was also observed to be twice as high for the left hemisphere. These findings, if replicated, could indicate that the development of the left hemisphere is more aVected by early acting (and thus shared) environmental perturbations and by early acting genetic variation. Interestingly, the authors also described that in non‐right‐handed individuals, brain volume measures, particularly frontal lobe measures, are less heritable than in right‐handed persons, indicating an increased role for environmental exposures in this population subgroup. Heritability of gyral/sulcal structures seems to be lower than volume measures, but potentially in part for methodological reasons (i.e., the diYculty to quantify these brain structures reliably). For instance, Wright et al. (2002) reported no evidence of heritability for gyral and sulcal pattern. Others have found that cortical gyral and sulcal patterns vary considerably between MZ twins, particularly in the more frontally located regions, and particularly the shallow, superficial sulci (Bartley et al., 1997; Bonan et al., 1998; Lohmann et al., 1999; White et al., 2002).

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Heritability of gyral and sulcal patterns was estimated to be 0.1–0.6, suggesting that these patterns are under substantial influence of nongenetic factors. So far, only one study estimated genetic and environmental contributions to individual diVerences in hippocampal volumes. Sullivan et al. (2001) investigated 44 MZ and 22 DZ male twins in their seventh and eighth decade of life (i.e., after a lifetime of environmental exposure). None of the subjects exhibited obvious signs of dementia. It was found that only 40% of hippocampal size variance was attributable to genetic influences, whereas 80% of the entire brain volume variance was estimated to be under genetic control in the same study. The authors concluded that environment, whether by itself or in interaction with genes, exerts greater and possibly longer control in modifying hippocampal size than in other brain regions. Most studies agree that general intelligence, which is about equally under genetic and environmental control ( Winterer and Goldman, 2003), correlates with entire brain and gray matter volume and that this correlation is in part under genetic control (i.e., ‘‘genetic correlation’’) (Pennington et al., 2000; Posthuma et al., 2002; Thompson et al., 2001; Tramo et al., 1995), although a few studies also reported negative findings (Anderson and Harvey, 1996; Eliez et al., 2001; Schoenemann et al., 2000). With regard to white matter volume, comparable correlations with cognitive ability have been less well established. Both positive and negative findings have been published (Andreasen et al., 1993; Eliez et al., 2001; Reiss et al., 1996; Yurgelun‐Todd et al., 2002). Relatively little is known about the genetic relationship between more circumscribed brain regions (e.g., prefrontal cortex) or cortical surface profile measures (e.g., variance of gyri) and cognitive ability. Of interest in the context is a recent study by Schoenemann et al. (2000). This study directly addressed the question of the genetic influence on the correlation between cognitive abilities and prefrontal cortex volume by investigating sibling pairs. The authors described a genetic correlation between frontal lobe volume and performance on the Stroop test, which is known to involve the prefrontal cortex—a correlation they did not find with respect to other brain volume measures. B. HERITABILITY

OF

BRAIN FUNCTION

1. Positron Emission Tomography/Single Photon Emission Tomography Functional imaging has been widely and successfully applied to the investigation of genetic neuropsychiatric disorders. So far, however, little is known from twin or family studies about the heritability of metabolic or hemodynamic activation patterns (i.e., brain function). The limited heritability data derive from older studies conducted before the time when functional images were coregistered with MRI scans informative for individual brain anatomy. Using FDG‐PET, Buchsbaum et al. (1984) found a low correlation of resting prefrontal

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glucose metabolism among MZ quadruplets with schizophrenia. However, Clark et al. (1988), using essentially the same method in a set of seven healthy MZ twin pairs, found intraclass correlations between 0.4–0.7 for frontal lobe and putamen/caudate activation and somewhat lower correlations for other cortical and subcortical regions of the brain. Despite these limited data, PET (and receptor‐ binding SPECT) investigations are principally useful for genetic studies, because the basic prerequisite (i.e., a relatively high test–retest stability) is generally obtained with these measures (e.g., Kegeles et al., 1999; Martinez et al., 2001; Seibyl et al., 1996; White et al., 1999; Yasumo et al., 2002). 2. Functional Magnetic Resonance Imaging The more recently introduced functional magnetic resonance imaging (f MRI) does not involve exposure to ionizing radiation and, therefore, oVers a critical advantage for genetic studies, in which no potential benefit to the individual participant is expected by allowing for much larger samples. However, at this point, data on f MRI heritability or intrafamilial correlations are entirely absent. Also, the currently available test–retest stability data—in particular for hemodynamic response patterns during cognitive tasks—frequently do not allow definitive conclusions on the stability of the f MRI phenotypes. In general, a signal change of 1% needs to be detected against a backdrop of noise reaching a signal‐ to‐noise ratio value of 3–5% in the averaged data of a single subject (Bandettini and Wong, 1997; Manoach et al., 2001; Rutten et al., 2002; Schaefer et al., 2000). Relevant signal changes after cognitive or emotional challenge may be even smaller in brain regions, contributing to cognitive information processing such as the prefrontal cortex. However, improvements can be achieved by comparing regions of interest or activation clusters or by using the more recently introduced independent component analysis ( ICA) (Calhoun et al., 2002; Kimura et al., 1999; Ojemann et al., 1998). In addition, there is preliminary evidence that high‐field scanners may yield better data under certain circumstances. Whenever possible, functional imaging measures for genetic investigations, therefore, should be accompanied by task‐ and scanner‐specific test–retest reliability data. When there are insuYcient data on measurement stability and heritability, functional imaging data may be best used in conjunction with neuropsychological and/or electrophysiological measures whose stability and heritability are known (Egan et al., 2001; Kwon et al., 2001) and when there is convergent molecular, cellular, and non‐human primate evidence for functionality (e.g., 5‐hydroxytryptamine transformer [HTT]). 3. Electrophysiology The heritability of electrophysiological parameters has been extensively described in literature (for a review, see Vogel [2000]). Historically, most studies on the heritability of electrophysiological parameters investigated ERPs and EEG

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oscillations that are generated in the posterior part of the brain. Only recently, a growing number of electrophysiological studies have provided insights for the genetic determination of frontal lobe–related electrophysiology. The largest body of studies exists on the resting EEG condition. According to Van Beijsterveldt et al. (1996), heritabilities of EEG power spectra range between 0.7–0.9 across frequency bands (0.5–30 Hz) and cortical areas, and the largest part of variance of the EEG is explained by additive genetic factors with some limited additional influence from nonshared environment. A structural equation model across electrode positions for the alpha frequency band (8.0–12.5 Hz) found high genetic correlations (0.8–1.0) between electrode positions, suggesting that the same genes contribute to the observed variance of the EEG (for a specific frequency band) at diVerent scalp positions—even if there are topographic maxima for certain EEG frequencies. On the other hand, there is also evidence that the heritability of EEG is somewhat lower in the frontal region, where slow activity (0.5–7.5 Hz) is predominant, than in posterior brain regions, where alpha activity is most abundant (8.0–12.5 Hz) (Trubnikov et al., 1993; Van Beijsterveldt et al., 1996). The view of a higher EEG heritability in the posterior compared with the anterior region is to some extent supported by a longitudinal genetic analysis of EEG coherence (i.e., functional coupling of EEG oscillations between or within cortical regions) (Van Baal et al., 1998, 2001). Between the ages of 5 and 7 years, there seems to be a gradual increase in heritability of coupling within the occipital cortical region, potentially indicating an age‐related decrease in environmental variance or the emergence of new genetic factors. Interestingly, it was also found that heritability decreased at the same time for prefrontal cortical connections, indicating a decrease of genetic variance. Overall, heritabilities were moderate to high for all intrahemispheric EEG coherences (average, 0.6). At the age of 7, heritabilities were in the range between 0.6–0.8 for posterior coherences, whereas heritabilities were substantially lower for frontal coherences (i.e., approximately 0.3–0.5). Heritabilities were highest for long‐range EEG coherences between the frontal and parietooccipital cortex ( 0.6–0.8) (i.e., cortical regions that have strong anatomical connections). Of note, heritabilities of frontal coherences seem to increase again at puberty, then reaching values in the range between 0.3–0.8, with the lowest heritability for the delta‐frequency and highest heritability for the alpha‐frequency band (Van Beijsterveldt et al., 1998c). Also, there is no longer any obvious diVerence of heritability between posterior and anterior brain regions. These findings suggest that there are diVerent dynamic interactions between genetic and environmental factors in diVerent brain regions at diVerent stages during development. Task‐ and event‐related electrophysiological pattern also seems to be under substantial genetic control and, in many cases, to the same extent as that described for resting EEG. However, some diVerences can be found in dependency of task condition and electrophysiological parameter (Van Bejisterveldt et al., 1994;

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Vogel, 2000). For instance, Hansell et al. (2001) have recently investigated 391 adolescent twin pairs (291 MZ and 100 DZ twins)—a study that has been part of a large international twin study funded by the Human Frontier Science Program. The authors addressed the question whether the increase of task‐related frontal slow‐wave activity during a working memory task (delayed‐response task) is genetically controlled. For comparison, they also investigated the same group with a sensory choice reaction task without a delay component but otherwise identical task conditions. As expected, they found a significantly stronger increase of task‐related frontal and parietal slow‐wave activity during the memory task compared with the choice reaction task. Structural equation modeling, however, revealed that this relative slow‐wave increase during the memory condition is under little, if any, genetic influence. Only the task‐independent frontal slow‐wave activity was influenced by a genetic factor (approximately 35% of genetic variance). Moreover, approximately 50% of the genetic variance was explained by the parietal slow‐wave increase in both task conditions. This suggests (from a geneticist’s perspective) that cortical activation related to working memory task performance is under stronger parietal than frontal control, which would be to some extent in agreement with functional neuroimaging literature showing not only frontal but also parietal activation during working memory tasks (Cornette et al., 2001). Also, the findings could indicate that one core component of working memory (i.e., the frontal lobe activation associated with the delay [‘‘hold on line’’] component) is not under substantial genetic influence. At least one study, which looked at the eVect of variation in a specific gene related to frontal lobe function, found that the delay component of working memory was not the primary component of the task predicted by genotype (Goldberg et al., 2003). It is important to note, however, that we are only beginning to understand the phenotypical relationship among behavior, hemodynamic, and neuronal response.

V. Application of the Principles

A. DEMENTIA Alzheimer’s disease (AD) is a complex polygenic disorder in most cases (Emahazion et al., 2001; Farrer et al., 1991) and is the most common form of dementia in adults, aVecting approximately 7% of people older than 65 and perhaps 40% of people older than 80 (Price, 2000). The disease is typically characterized by a severe decline in memory performance (American Psychiatric Association, 1995), and from an imaging perspective, slowing of resting EEG in the temporoparietal region (Dierks et al., 1991; DuVy et al., 1984), prolonged latency (and amplitude reduction) of the temporoparietal P300 event‐related

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potential component (Brown et al., 1983; Syndulko et al., 1982), volume reduction of the medial temporal lobe in MRI or CT scans ( Jack et al., 1992; Jobst et al., 1992; Scheltens et al., 1992), a decline of white and gray matter tissue anisotropy that is most prominent in the temporal lobe (Bozalli et al., 2001), and decreased parietotemporal regional blood flow and glucose‐uptake in PET and SPECT scans (Benson et al., 1981; Herholz et al., 2002a,b; Mazziotta et al., 1992). To some extent, comparable, but more subtle, abnormalities are also observed in subjects at familial risk for the disease (Boutros et al., 1995; Burggren et al., 2002; Green and Levey, 1999; Ponomareva et al., 1998) or in early stages of the illness (De Santi et al., 2001; Fellgiebel et al., 2004; Grundmann et al., 2002; Huang et al., 2000; Killiany et al., 2002). In the latter subject group, compensatory increases of activity in the prefrontal cortex also have been described (Grady et al., 1994). Hallmarks in post mortem investigations of patients with AD are senile plaques in the temporoparietal cortex with the principal components: neurofibrillary tangles in the neuronal cell bodies, neuropil threads, and neurites, as well as extracellular A amyloid (Price, 2000) that can now be detected in vivo using the most recently developed PET tracers (Nordberg, 2004). A amyloid is cleaved from a larger precursor protein of unknown function, amyloid precursor protein (APP), and it is encoded by the APP gene in the midportion of the long arm of human chromosome 21 (Selkoe, 1996). Mutations of this gene lead to an accumulation of A amyloid in patients with early‐onset AD (<60 years) and also in patients with Down’s syndrome. Cerebral ischemia as assessed by conventional CT or MRI is thought to chronically upregulate expression of the amyloid precursor protein (APP) and to damage the blood–brain barrier, aVecting A peptide clearance from the brain (Sadowski et al., 2004). Recognition of the importance of vascular risk factors for AD‐related dementia and their treatment, therefore, could be beneficial not only for preventing cardiac, cerebral, and peripheral complications of vascular disease but also will likely have a direct impact on the occurrence of AD in at‐risk subjects. A genetic variant that has been consistently associated with the more common form (90%) of AD (i.e., late‐onset AD [>60 years]), is the epsilon (") 4 allele of the apolipoprotein E (APOE) gene on the long arm of chromosome 19, whereas the epsilon (") 2 allele seems to be less frequently associated with AD and may be even protective (Corder et al., 1993; Farrer et al., 1997). The "4 allele has been shown to be more common among individuals from Northern countries and African‐Americans than in subjects with a Southern European origin (Gerdes et al., 1992; Pablos‐Mendez et al., 1997; Srinivasan et al., 1993) (i.e., population stratification can be a serious confounder when investigating this gene). The primary role of its plasma protein APOE is thought to remove lipoprotein particles from the circulation through binding to specific receptors belonging to the low‐density lipoprotein (LDL) family (Piedrahita et al., 1992; Puglielli et al., 2003; Zhang et al., 1992). The mechanism by which the "4 allele elevates risk for late‐onset AD is largely unknown; however, it has been

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suggested that increased cholesterol levels and its distribution within neurons may play a critical role (Puglielli et al., 2003). APOE is present in senile plaques, neurofibrillary tangles, and cerebrovascular amyloid, which is correlated with the gene dose for the "4 allele (Ohm et al., 1995). This gene‐dose eVect is also observed with respect to age of onset and risk for the disease (Corder et al., 1993). Episodic memory decline (Bondy et al., 1995) or cognitive decline (Harwood et al., 2002; Helkala et al., 2001; Reed et al., 1994; YaVe et al., 1997) has been demonstrated in older adults carrying the "4 allele, whereas the "2 allele may play a protective role in normal aging (Farrer et al., 1997). Also, APOE "4 allele status predicts slowing of resting EEG, regional cerebral blood flow, and glucose uptake in the temporoparietal region as measured with EEG, SPECT, and PET in patients with AD and in cognitively normal, even middle‐aged, subjects (Burggren et al., 2002; Higuchui et al., 1997; Lehtovirta et al., 1996, 2000; Reiman et al., 1996; Small et al., 1995, 2000). These findings also suggest that neuroimaging measures might be more sensitive than cognitive measures with regard to the genetic eVects on brain function. Taking an even more sophisticated approach with direct comparison of cognitive performance and brain activity, evidence of high sensitivity of functional imaging measures was also obtained by Bookheimer et al. (2000), who used f MRI during a challenging memory task to explore the genetic eVects of the APOE "4 allele on memory‐ related brain activity. In their landmark study, 16 subjects carrying the APOE "4 allele and 14 subjects homozygous for the APOE "3 allele, which is not associated with increased risk for AD, were asked to memorize and recall unrelated word pairs, a demanding memory task previously used to identify damage to the medial temporal lobe memory system (Rausch and Babb, 1993), while undergoing f MRI. Although all subjects were cognitively intact and performed the task equally well, the pattern of brain activation between the two groups was strikingly diVerent. Compared with subjects with the APOE "3 allele, those with the high‐ risk APOE "4 allele exhibited significantly greater activation (both magnitude and extent) in memory‐related brain regions such as the prefrontal cortex and left hippocampus. Such relatively increased neural activation in those with the at‐risk allele was interpreted by the authors as reflecting possible compensatory phenomena through the recruitment of additional cognitive resources in the face of greater task diYculty and demand. Interestingly, the magnitude of task‐related brain activity was significantly correlated with subsequent memory decline. These data suggest that changes in cortical information processing during declarative memory are associated with the biological eVects of APO "4 even if compensation is made at the level of observable behavior (i.e., task performance). Thus, the authors concluded that observed diVerences in memory‐related brain activity associated with the APOE gene in the absence of behavioral impairments may provide a useful tool for predicting the course of cognitive decline.

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B. MENTAL DISABILITY Trisomy 21 (Down’s syndrome) occurs at a frequency of 1.5 in 1000 live births and results in moderate to severe mental disability. The syndrome illustrates the complexity of the clinical, behavioral, functional, molecular, and genetic dimensions of cognitive ability. Trisomy 21 usually occurs as a new mutation resulting from nondisjunction during first meiotic prophase in the process of maternal gametogenesis and more rarely because of the transmission of an extra translocated segment of chromosome 21. The relevant chromosomal area has been localized on 21q21.3‐q22.2 (called Down’s Syndrome Critical Region [ DSCR]), although a few other chromosomal regions also may be involved (Epstein et al., 1991; Korenberg et al., 1990; McCormick et al., 1989). Human 21q21.3‐q22.2 is homologous to portions of mouse chromosome 16 (Kola and Herzog, 1998). Reciprocal translocation involving this area in mice results in learning defects (Reeves et al., 1995). In another mouse model of Down’s syndrome, transgenic mice with a 180‐kb YAC containing a 100‐kb segment of the human 21q22.2 locus develop learning deficits (Smith et al., 1997). Several genes in this region have been implicated, notably the amyloid precursor protein gene (Ohira et al., 1997). The localization of the amyloid gene to this region is congruent with the identification of point mutations on the amyloid precursor protein of several early‐onset Alzheimer families, although most familial AD is not caused by such mutations (Kamino et al., 1992; St. George‐Hyslop et al., 1987; Tanzi et al., 1992). Amyloid protein accumulates in plaques in older patients with Down’s syndrome (>30 years) and in patients with AD. The DSCR1 (Down Syndrome Candidate Region 1) gene is another gene in this chromosomal region whose transcripts have been detected in high concentrations in the brain. It is also expressed in muscle, placenta, and kidney coding for at least four amino‐ acid isoforms resulting from alternative splicing and an alternative promoter (fourth isoform) (Casas et al., 2001; Fuentes et al., 1995, 1997; Price, 2000). DSCR1 is a gene that has a high sequence identity with ZAKI‐4 (Myazaki et al., 1996), and both belong to a family of proteins called myocyte‐enriched calcineurin interacting protein, because they bind and inhibit calcineurin signaling (Miyazaki et al., 1996). Chronic overexpression of DSCR1 is found in AD (Ermak et al., 2001). Calcineurin is activated by calcium‐calmodulin signaling and regulates the nuclear import of NF‐AT (nuclear factor–activated T cells), which ultimately stimulates transcription of a variety of genes, including interleukin‐2 (IL2) (Porter et al., 2000). Release of cytokines such as IL‐2 may be critical to inflammatory aspects of CNS pathology in neurodegeneration (Borrell et al., 2002; Raber et al., 1998). The complexity of these processes is still far from being understood. Also, it is unclear how the chromosomal aberration exactly relates to the phenotypic changes seen in these patients (e.g., post mortem findings), which show that patients

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with Down’s syndrome have degeneration of cholinergic basal forebrain (CBF) neurons like patients with AD ( McGeer et al., 1985; Sendera et al., 2000). Patients with Down’s syndrome are usually severely impaired in a wide range of cognitive domains, including episodic learning deficits and capabilities that involve the prefrontal cortex such as working memory ( Jarrold and Baddeley, 2001; Numminen et al., 2001). Electrophysiological abnormalities in trisomy 21 encompass a variety of ERP components (N100/P200; P300) (Karrer et al., 1995; Vieregge et al., 1992), as well as EEG coherence (Schmid et al., 1992). These electrophysiological studies suggest that a wide variety of, or all, cortical areas are aVected in trisomy 21. The EEG diVerences are consistent with structural MRI studies showing smaller overall brain volume, although the cerebellum, brainstem, frontal lobe, and hippocampus seem to be disproportionately smaller (Aylward et al., 1999; Pinter et al., 2001; Weis et al., 1991; White et al., 2003). This generalized pattern of abnormal brain structure and function contrasts with that found in AD, where abnormal function and structural changes are more limited to the hippocampus and temporoparietal area and only during later illness stages may involve prefrontal regions (e.g., Burggren et al., 2002; Grady et al., 1988). In patients with Down’s syndrome, premature aging and dementia of the Alzheimer‐syndrome type is typically observed, and the onset of this decline is accompanied by an increase in slow‐wave resting EEG‐power (Murata et al., 1994). A recent voxel‐based morphometry study suggested that these clinical and functional changes have a structural equivalent with relatively selective cortical gray matter volume loss in the temporoparietal and frontal lobe area, whereas other brain areas are spared ( Teipel et al., 2004). Thus, it seems that the most devastating aspect of Down’s syndrome (i.e., premature aging and dementia) shares similarities with AD on various levels, including clinical symptoms, cognitive deficits, functional and structural changes, post mortem brain changes, and the molecular genetic level. It can be expected that in the near future imaging genomics—because of its high sensitivity with regard to pathological brain processes—will contribute significantly to the explanation of the responsible genetic mechanisms and by extension may even help to better understand the dysfunctional molecular cascades in AD.

C. SCHIZOPHRENIA Schizophrenia illness perhaps provides one of the best examples of how neuroimaging can contribute to the dissection of the genetic basis of neuropsychiatric disorders. Schizophrenia is a common mental disorder aVecting approximately 1% of the general population with frequent onset of illness during early adulthood (American Psychiatric Association, 1995). In a recent meta‐analysis of twin studies (Sullivan et al., 2003), heritability to schizophrenia was estimated to

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be high (80%) with evidence for substantial additive genetic eVects and for common or shared environmental influences of approximately 10%, which is consistent with the view of schizophrenia being a complex polygenic disorder. Clinically, schizophrenia is typically associated with delusions and hallucinations during acute psychotic episodes, whereas negative symptoms may predominate between episodes. Cognitive deficits in working memory or attention are also found and are thought to be more closely related to the neurobiology of the illness (Goldberg and Weinberger, 2004; Winterer and Weinberger, 2004). There is presently general agreement that abnormal brain function in schizophrenia involves an extended network of cortical and subcortical brain structures, including temporal and parietal cortices, the basal ganglia, cerebellum, hippocampus and thalamus, and particularly the prefrontal cortex (Weinberger et al., 2001). In fact, evidence for subtle gray matter pathology in schizophrenia is legion, as shown by innumerable structural neuroimaging studies and post mortem analyses and is further supported by PET, f MRI, and electrophysiological studies. Thus, structural deficits and functional impairments that are compatible with cortical pathology most notably of the prefrontal and temporal cortex have been found in patients with first‐episode (Andreasen et al., 1997; Braus et al., 2002; Salisbury et al., 1998; Zipursky et al., 1998) and chronic schizophrenia (Callicott et al., 2000a; Ingvar and Franzen, 1974; Morstyn et al., 1983; Weinberger et al., 1986; Wright et al., 1999; Zipursky et al., 1992), as well as before the outbreak of psychosis (Lim et al., 1996; Pantelis et al., 2003) and in nonpsychotic family members (Callicott et al., 2003a; Gogtay et al., 2003; Staal et al., 2000; Winterer et al., 2003a); the latter findings suggest a relationship to primary genetic susceptibility. However, there is also some evidence that gray matter volume changes occur during the early course of the illness (Rapoport et al., 1999; Wiegand et al., 2004), implicating dynamic processes related to gray matter volume. The molecular basis for cortical microcircuit dysfunction has been the subject of an increasing body of research. Although most of this work has been done in post mortem tissue, several clinical investigations using proton magnetic resonance spectroscopy (MRS) have found reduced concentrations of N‐acetyl aspartate (NAA) in hippocampal and prefrontal cortices of patients with schizophrenia and also in their healthy siblings, again suggesting a genetic basis of cortical pathology (Weinberger et al., 2001). NAA is an intraneuronal measure primarily of mature pyramidal neurons and their processes; NAA levels vary with changes in mitochondrial oxidative phosphorylation and with glutamate levels (PetroV et al., 2002), and reduced NAA levels are found in numerous brain disorders. As such, NAA is interpreted as a nonspecific but sensitive measure of synaptic activity and abundance, and low NAA in schizophrenia implicates abnormal synaptic activity in these cortical regions. A reduction of prefrontal NAA in patients with schizophrenia also has been found to predict abnormal prefrontal cortical activation patterns in patients as measured with PET and with f MRI (Bertolino et al., 2000a;

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Callicott et al., 2000b). In addition, cortical NAA concentrations seem to be inversely correlated with negative symptoms, which have been linked to prefrontal function (Callicott et al., 2000). Consistent with the notion that abnormal local circuit processing could have distributed ramifications in the brain ( Winterer and Weinberger, 2004), NAA concentrations in the prefrontal cortex also have been shown to predict cortical activity in a distributed cortical network engaging parietal and temporal cortices (Bertolino et al., 2000a) and to predict the exaggerated ‘‘downstream’’ response of dopamine neurons in the striatum to amphetamine in patients with schizophrenia (Bertolino et al., 2000b) which is thought to be related to positive symptoms in schizophrenia (Laruelle et al., 1996). Accumulating evidence of a disturbed cellular architecture of cortical gray matter neurons also comes from post mortem investigations of synaptic proteins. Reductions of presynaptic vesicle proteins such as the synapsins and, less consistently, synaptophysin have been described (Browning et al., 1993; Glantz and Lewis, 1997). Three proteins of the SNARE receptor complex, which are involved in neurotransmitter vesicle docking to the inner plasma membrane, have been found to be downregulated: synaptosomal‐associated protein‐25 (SNAP‐25) (Young et al., 1998) and complexin 1 and 2 (Eastwood and Harrison, 2001). Decreased densities of dendritic spines have also been reported (Glantz and Lewis, 2000) as has reduced expression of reelin (Impagnatiello et al., 1998), which is secreted by GABAergic neurons in association with dendritic postsynaptic specializations. The molecular genetic basis of schizophrenia illness is currently a field of intensive research. Family‐based association studies have lately provided strong evidence for several schizophrenia susceptibility genes such as NRG1, DTNBP1, G72, RGS4, CHRNA7, and GRM3, which are all thought to interfere with synaptic transmission (Harrison and Owen, 2003). Another potential risk gene for schizophrenia that has been recently a matter of extensive investigation including the application of neuroimaging for the purpose of ‘‘endophenotyping’’ is the COMT (catechol‐O‐methyltransferase) gene. There are diVerent reasons why this particular gene is studied. A microdeletion (22q11), containing the COMT gene, has been observed in conjunction with velo‐cardio‐facial syndrome, which carries with it distinct clinical phenotypes, including schizophrenia‐like psychotic features (Murphy et al., 1999; Pulver et al., 1994). In addition, a recent meta‐ analysis suggested on the basis of the available family‐based association studies that a functional polymorphism (val108/158met) in this gene on exon 4 (see later) might be a small but reliable risk factor for schizophrenia illness—at least for people of European ancestry (Glatt et al., 2003). Consistent with this interpretation of the data is another recent large‐scale case‐control study of an ethnically highly homogenous sample of Ashkenazi Jews (Shifman et al., 2002) showing a significant association of Val/Met with schizophrenia illness. The idea to study this particular gene was primarily encouraged by the notion that prefrontal

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synaptic dopamine (DA) signaling is altered in schizophrenia (Abi‐Dargham et al., 2002; Akil et al., 1999, 2003; Weinberger et al., 1988). COMT, a methylation enzyme that converts released dopamine to inactivate 3‐methoxytyramine, is believed to play an important role in DA neurotransmission (Weinshilboum et al., 1999). In rats, COMT accounts for >60% of DA degradation by methylation in the prefrontal cortex (Karoum et al., 1994). Microdialysis studies have shown that pharmacological inhibition of COMT aVects dopamine flux in the prefrontal cortex but has no eVect on norepinephrine and that COMT does not impact on DA flux in the striatum (Li et al., 1998; Tunbridge et al., 2004). Moreover, COMT knockout mice show increases in prefrontal DA levels in an allele dosage fashion and also show no changes in norepinephrine metabolism (Gogos et al., 1998; Huotari et al., 2002). Expression of COMT is especially abundant in the prefrontal cortex relative to the striatum in both human and rodent brains (Matsumoto et al., 2003). In humans, the COMT gene contains a highly functional and common variation in its coding sequence (i.e., a substitution of valine by methionine [val158/108met] in the peptide sequence), which is caused by a transition of guanine to adenine at codon 158 of the COMT gene (Lachman et al., 1996; Lotta et al., 1995). This single amino acid substitution aVects the activity and temperature lability of the enzyme; at body temperature the Met allele has significantly less enzyme activity than the Val allele and is a less stable protein (Chen et al., 2004; Lachman et al., 1996; Lotta et al., 1995; Weinshilboum et al., 1999). In addition, a recent post mortem analysis found that the Val/Met polymorphism aVects protein abundance and enzyme activity in human brain (Chen et al., 2004). Using site‐directed mutagenesis of mouse COMT cDNA followed by in vitro translation, Chen et al. (2004) demonstrated that conversion of leu at the homologous position into Val or Met progressively diminished enzyme activity. These data would suggest that individuals with Val alleles would have relatively greater inactivation of prefrontal DA, therefore less eVective prefrontal DA signaling, and, by extension, diminished prefrontal function as frequently seen in patients with schizophrenia illness (see preceding). To assay directly the impact of the COMT Val/Met polymorphism on prefrontal physiology, Egan et al. (2001) used fMRI during the performance of a well‐ characterized working memory test (the n‐back task) that has been eVective at engaging the dorsolateral prefrontal cortex in prior imaging studies (Callicott et al., 1999; Cohen et al., 1997). The authors found that in two separate cohorts of healthy volunteers (n ¼ 11–16), all matched for age, gender, education, and task performance, the load of the high‐activity Val allele consistently predicted a relatively exaggerated prefrontal response during the working memory task (Fig. 6). Notably, this study also highlights the statistical power of the endophenotype approach compared with more traditional behavioral measures. In addition to the f MRI investigation, Egan et al. (2001) also used a measure of executive cognition in terms of working memory test performance (Wisconsin Card Sorting

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FIG. 6. Abnormal cortical signal‐to‐noise pattern in schizophrenia. Patients with schizophrenia and their healthy siblings show ineYcient prefrontal engagement (measured using functional magnetic resonance imaging, f MRI) and increased prefrontal response variability (measured as an electroencephalogram, EEG). (A) Statistic maps from f MRI during an n‐back working memory task, showing areas where a group of patients (i) and a group of healthy siblings of patients with schizophrenia (ii) are ineYcient compared with normal controls when performance on the task does not diVer between comparisons groups (Callicott et al., 1999; 2003b). In f MRI data, ineYciency (an empirical term indicating excessive activity for a given level of performance) is assumed to reflect unfocused or unstable response circuits. (B) Topographic maps of event‐related EEG during an auditory oddball task, showing increased ‘‘noise’’ in patients with schizophrenia, their healthy siblings, and normal controls in delta (i) and theta (ii) frequency bands (Winterer et al., 2004). ‘‘R’’ and ‘‘L’’ indicate right and left sides of the brain, respectively. Based on the extensively investigated oddball‐ evoked potential paradigm, Winterer et al. (2000b; 2003; 2004) derived a measure of variability (‘‘noise’’) of the response to a P300 electromagnetic source in a large sample of patients with schizophrenia, their healthy siblings, and a normal comparison group. Response variability (‘‘noise’’; i.e., activity not time‐locked to the stimuli) was approximated by subtracting the mean magnitude of the single trials from the magnitude of the average potential. Winterer et al. showed that, in addition to the classic pattern of reduced P300 amplitude in parietal cortex of the patient sample, patients and their healthy siblings had a relatively unstable prefrontal response. In healthy siblings, this measure of cortical processing instability was intermediate between the patients and the controls. Moreover, intraclass correlation between siblings was 0.5–0.6, and nonpsychotic siblings were three to four times more likely to show increased variability than healthy control subjects with no family history of schizophrenia. These results suggest that schizophrenia and the genetic risk for schizophrenia involve unstable processing in prefrontal cortical microcircuits. In addition, this EEG measure was inversely correlated with working memory performance even in normal individuals, suggesting that it reflects a functional state of microcircuits subserving the cognitive behavior of prefrontal cortex (Winterer et al., 2004). Adapted, with permission, from Winterer and Weinberger [2004]).

Test, WCST). The small eVect size of genotype on WCST perseverative errors, in which COMT genotype predicted approximately 3–4% of the variance, required several hundred subjects to achieve statistical significance. In contrast, powerful statistical diVerences were observed in imaging samples of fewer than 15 subjects.

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Since this initial study, a number of studies in various clinical and healthy populations using neuropsychological, neurophysiological, and f MRI measures of prefrontal cortical function have consistently shown that COMT Val/Met genotype indeed aVects prefrontal function (Bearden et al., 2004; Bertolino et al., 2004; Bilder et al., 2002; Diamond et al., 2004; Egan et al., 2001; Foltynie et al., 2004; Gallinat et al., 2003a; Goldberg et al., 2003; Malhotra et al., 2002; Mattay et al., 2003; Rosa et al., 2004; Weickert et al., 2004). More recently, it also could be shown that the underlying functional deficit of the association between COMT genotype and abnormal prefrontal activation is characterized by a decreased signal‐to‐noise ratio (i.e., an increased variability of task‐related prefrontal response in val‐carriers) (Winterer et al., 2005). Prior studies of patients with schizophrenia and of their unaVected siblings have demonstrated that increased prefrontal response variability is highly heritable and related to genetic risk for schizophrenia (Winterer et al., 2004) (Fig. 6). The rationale to investigate the eVect of COMT genotype on prefrontal response variability came from computational models on the basis of electrophysiological primate data that suggested that an increased prefrontal response variability reflects a lack of stimulus‐induced cortical synchronization (i.e., phase‐resetting or ‘‘noise’’) and that the degree of the response variability depends at least in part on cortical DA signaling (Winterer and Weinberger 2004) (Fig. 7). Overall, the functional investigations of COMT genotype provide direct evidence that the eVects of the COMT Val/Met polymorphism may reflect alterations in prefrontal dopamine catabolism related to COMT enzymatic activity. The COMT endophenotype results also illuminate the aforementioned evidence from traditional genetic studies that the Val allele is a susceptibility allele for schizophrenia and possibly other psychoses (Eberhard et al., 1989; Nurnberger and Foroud, 2000).

D. MOOD

AND

ANXIETY DISORDERS

Mood and anxiety disorders are a heterogeneous group of clinically overlapping syndromes, which show high comorbidity among each other and symptomatic fluidity with frequent changes of diagnostic subtypes over time (Angst and Merikangas, 2001; Merikangas et al., 2003). Most of these disorders are thought to have a complex polygenic background in common with a substantial environmental component. Whereas heritability estimates are high for bipolar disorder (0.8–0.9), considerably lower heritability rates (0.35–0.45) are found for the genetically correlated syndromes major depression, anxiety disorder, phobia, and panic disorder, whereby anxiety disorder also seems to show a strong genetic correlation (0.8–0.9) with the personality trait neuroticism (Hettema et al., 2003, 2004; Kendler et al., 1995, 2001; Kieseppa¨ et al., 2004). Lifetime prevalences of

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FIG. 7. Cortical dopamine signaling and disruption of connectivity and cortical signal‐to‐noise ratio in schizophrenia. Reduced prefrontal dopamine D1/D2‐receptor activation ratio, together with decreased NMDA and GABA signaling and altered activity of synaptic proteins and signaling genes, at the synaptic level leads to unfocused cortical excitation and reduced recurrent inhibition (i.e., patients with schizophrenia are ‘‘D2‐receptor‐dominated’’). This results in lower cortical signal‐to‐ noise ratio (SNR). Patch‐clamp studies show that dopamine signaling by D1 receptors increases NMDA conductance and tends to increase GABA excitability, whereas D2 stimulation tends to have opposing eVects. In computational network models (Durstewitz et al., 2000; 2002), the bidirectional dopamine eVects on GABAA and NMDA conductances were simulated by changing the equations for ionic currents according to in vitro patch‐clamp data. At the microcircuit level, early D2‐mediated decrease of inhibition might allow multiple cortical representations of an event to be activated closely in time, and even weak representations could pop easily into the delay‐active state (state 1). Conversely, weakly active representations would be subsequently suppressed by D1‐mediated activation, and a single or limited number of strongly active representations would become stable and resistant to additional inputs and noise (state 2). In other words, D1 stimulation can be conceived as widening and deepening the basins of attraction of low‐activity (e.g., spontaneous) and high‐activity (e.g., working memory) states of the network, whereas the opposite eVect is found with D2 activation. The lower SNR subsequently leads to impaired macrocortical connectivity (downstream eVects). The clinical syndrome is assumed to represent behavioral phenomena related to these changes in microcircuit and macrocircuit dynamics. Abbreviation: PFC, prefrontal cortex. Adapted, with permission, from Seamans et al. [2001] and Winterer and Weinberger [2004]).

aVective disorders may range up to 20%, with the notable exception of bipolar disorder with a morbidity risk of 1% in the general population (Faraone et al., 1999; Nurnberger and Berretini, 1998). A number of mostly relatively recent structural and functional neuroimaging studies have pointed out that the amygdala, among other brain areas, might be a

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particularly promising region to study in aVective and anxiety disorders. The amygdala is a limbic brain structure important for the generation of both normal and pathological emotional behavior, especially fear (LeDoux and Muller, 1997). Across diVerent mood and anxiety disorders, including bipolar disorder, evidence has been obtained from f MRI and FDG‐PET studies for elevated amygdala activity under fearful or stressful conditions and, less consistently, amygdala volume reduction also has been described (Blumberg et al., 2002; Drevets et al., 1992, 2002; Hasler et al., 2004; Hastings et al., 2004; Massana et al., 2003; Pissiota et al., 2003; Stein et al., 2002; Tillfors et al., 2001; Wik et al., 1997). As of today, however, it is not suYciently resolved whether the observed structural and functional changes reflect state or trait characteristics and whether equivalent changes are also seen in family members with genetically increased risk for these illnesses (Blumberg et al., 2002; Drevets, 2003). In addition, the amygdala is a small, complex, and heterogenous brain structure diYcult to quantify, characteristics that may, in part, account for the reported inconsistencies of volumetric studies (Hasler et al., 2004). The amygdala is densely innervated by serotonergic neurons, and 5‐HT receptors are abundant throughout amygdala subnuclei (Azmitia and Gannon, 1986). Thus, the activity of this subcortical region may be uniquely sensitive to alterations in serotonergic neurotransmission, and any resulting variability in amygdala excitability is likely to contribute to individual diVerences in emergent phenomena such as mood and temperament (Hariri and Weinberger, 2003b). For several reasons, this notion rests on solid ground: (1) the amygdala is thought to be critically involved in processing fearful and stressful conditions (Davis and Whalen, 2001; Zald, 2003), (2) the therapeutic eVect of serotonin‐reuptake inhibitors in mood and anxiety disorders is well established (Schatzberg and NemeroV, 2001), and (3) reduced serotonin (5‐hydroxytryptamin, HT) transporter (5‐HTT) availability has been associated with mood disturbances, including major depression (Malison et al., 1998) and the severity of depression and anxiety in a variety of psychiatric disorders (Eggers et al., 2003; Heinz et al., 2002; Willeit et al., 2000). Therefore, it has been logical to ask whether functional variations in the 5‐HTT gene are associated with mood and anxiety disorders and, by extension, whether genetic eVects are observable at the level of amygdala biology. In 1996, a relatively common polymorphism was identified in the human 5‐HTT gene (SLC6A4) located on chromosome 17q11.1‐q12 (Heils et al., 1996). The polymorphism is a variable repeat sequence in the promoter region (5‐HTTLPR), resulting in two common alleles: the short (s) variant composed of 14 copies of a 20–23 base pair repeat unit, and the long (l) variant composed of 16 copies. In populations of European ancestry, the frequency of the s allele is approximately 0.40, and the genotype frequencies are in Hardy–Weinberg equilibrium (l/l ¼ 0.36, l/s ¼ 0.48, s/s ¼ 0.16). These relative allele frequencies, however, can vary substantially across populations (Gelernter et al., 1997).

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After the identification of this polymorphism, Lesch and colleagues demonstrated in vitro that the 5‐HTTLPR alters both SLC6A4 transcription and the level of 5‐ HTT function (Lesch et al., 1996). Cultured human lymphoblast cell lines homozygous for the l allele have higher concentrations of 5‐HTT mRNA and express nearly twofold greater 5‐HT reuptake compared with cells possessing either one or two copies of the s allele. Subsequently, both in vivo imaging measures of radioligand binding to 5‐HTT (Heinz et al., 2000) and post mortem calculation of 5‐ HTT density (Little et al., 1998) in humans reported nearly identical reductions in 5‐HTT binding levels associated with the s allele as observed in vitro. These data are consistent with ‐CIT SPECT studies in humans and nonhuman primates reporting an inverse relationship between 5‐HTT availability and CSF concentrations of 5‐hydroxyindoleacetic acid (5‐HIAA), a 5‐HT metabolite (Heinz et al., 1998, 2002) and indicate that the 5‐HTTLPR is functional and impacts on serotonergic neurotransmission. In their initial study, Lesch and colleagues also demonstrated that individuals carrying the s allele are slightly more likely to display abnormal levels of anxiety in comparison to l/l homozygotes (Lesch et al., 1996). Since their original report, others have confirmed the association between the 5‐HTTLPR s allele and heightened anxiety (Du et al., 2000; Katsuragi et al., 1999; Mazzanti et al., 1998; Melke et al., 2001) and have also demonstrated that individuals possessing the s allele more readily acquire conditioned fear responses (Garpenstrand et al., 2001) and develop aVective illness (Lesch and Mossner, 1998) compared with those homozygous for the l allele. Recent studies that used pharmacological challenge paradigms of the 5‐HT system suggest that these diVerences in aVect, mood, and temperament may reflect 5‐HTTLPR–driven variation in 5‐HTT expression and subsequent changes in synaptic concentrations of 5‐HT (Moreno et al., 2002; Neumeister et al., 2002; Whale et al., 2000). Not surprisingly, however, several additional studies have failed to identify a relationship between the 5‐HTTLPR genotype and subjective measures of emotion and personality (Ball et al., 1997; Deary et al., 1999; Flory et al., 1999; Glatt and Freimer et al., 2002; Katsuragi et al., 1999), likely reflecting the vagueness and subjectivity of the behavioral measurements but also raising some concern that the relationship may be spurious (Ohara et al., 1998). In addition, such replication failures may reflect inadequate control for nongenotype factors such as gender and ethnicity (Williams et al., 2003), as well as chronic alcohol use (Heinz et al., 1998; Little et al., 1998) and exposure to environmental stress (Caspi et al., 2003), all of which have been shown to influence the eVect of the 5‐HTTLPR on both brain and behavior. Although the potential influence of genetic variation in 5‐ HTT function on human mood and temperament was bolstered by subsequent studies demonstrating increased anxiety‐like behavior and abnormal fear conditioning in 5‐HTT knockout mice (Holmes et al., 2003), the underlying neurobiological correlates of this functional relationship remained unknown. Because the physiologic response of the amygdala during the processing of fearful stimuli may

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be more objectively measurable than the subjective experience of emotionality, the 5‐HTTLPR may have a more obvious impact at the level of amygdala biology than at the level of individual responses to questionnaires or ratings of emotional symptoms. In 2002, Hariri et al. (2002) used f MRI to directly explore the neural basis of the apparent relationship between the 5‐HTTLPR and emotional behavior (Fig. 8). Specifically, it was hypothesized that 5‐HTTLPR s allele carriers, who presumably have relatively lower 5‐HTT function and higher synaptic concentrations of 5‐HT (analogous to the 5‐HTT knockout mice) and have been reported to be more anxious and fearful, would exhibit greater amygdala activity in response to fearful or threatening stimuli than those homozygous for the l allele, who presumably have lower levels of synaptic 5‐HT and have been reported to be less anxious and fearful (analogous to the contrasting wild‐type mice). Critical to this study was that this hypothesis was tested in normal subjects with no history of depression or anxiety disorders. It was found that subjects carrying the less eYcient 5‐HTTLPR s allele exhibited significantly increased amygdala activity compared with subjects homozygous for the l allele. In contrast, there were no significant group diVerences in subjective behavioral measures of anxiety‐like or fear‐related traits. In fact, the diVerence in amygdala

FIG. 8. Genotype‐based parametric comparisons illustrating significantly greater activity in the right amygdala of the s group versus the l group in both the first and second cohort. BOLD f MRI responses in the right amygdala (white circle) are shown overlaid onto an averaged structural MRI in the coronal plane through the center of the amygdala. Talairach coordinates and voxel level statistics (p < 0.05, corrected) for the maximal voxel in the right amygdala for the first and second cohort are as follows: x ¼ 24 mm, y ¼ –8 mm, z ¼ –16 mm; cluster size ¼ 4 voxels; voxel level corrected p value ¼ 0.021; t ¼ 2.89, and x ¼ 28 mm, y ¼ –4 mm, z ¼ –16 mm; cluster size ¼ 2 voxels; voxel level corrected p ¼ 0.047; t ¼ 2.03, respectively. With permission, from Hariri et al. [2002]).

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activity between 5‐HTTLPR genotype groups in this study was nearly fivefold, accounting for 20% of the total variance in the amygdala response during this experience, an eVect size 10‐fold greater than any previously reported behavioral associations. This finding suggests that the increased anxiety and fearfulness associated with individuals possessing the 5‐HTTLPR s allele may reflect the hyperresponsiveness of their amygdala to relevant environmental stimuli. Recently, three independent functional imaging studies have reported identical 5‐HTTLPR s allele–driven amygdala hyperreactivity in cohorts of healthy German (Heinz et al., 2005) and Italian (Bertolino et al., 2005) volunteers, as well as Dutch patients with social phobia (Furmark et al., 2004). Moreover, Hariri et al. (2005) have also replicated their initial finding of 5‐HTTLPR s eVects on amygdala reactivity in a large, independent cohort of volunteers (n ¼ 92). This large sample also allowed for the exploration of both gender‐specific and s allele load eVects on amygdala function and, in turn, dimensions of temperament associated with depression and anxiety. Specifically, Hariri et al. again observed that 5‐HTTLPR s allele carriers exhibit significantly increased right amygdala activation in response to their f MRI challenge paradigm (Hariri et al., 2004). In addition, their latest data reveal that 5‐HTTLPR s allele–driven amygdala hyperresponsivity is equally pronounced in both genders and independent of s allele load. The equivalent eVect of one or two s alleles on amygdala function is consistent with the original observations of Lesch et al. (1996) on the influence of the 5‐HTTLPR on in vitro gene transcription eYciency and subsequent 5‐HTT availability. The absence of gender diVerences suggests that the increased prevalence of mood disorders in females may be related to factors other than the direct risk eVect of the 5‐HTTLPR s allele. The collective results of these imaging genomics studies reveal that the 5‐HTTLPR s allele has a robust eVect on human amygdala function. Importantly, the absence of group diVerences in age, gender, IQ, and ethnicity in each of these studies indicates that the observed eVects are not likely a reflection of systematic variation in such nongenotype factors. Rather, the data suggest that heritable variation in 5‐HT signaling associated with the 5‐HTTLPR results in relatively heightened amygdala responsivity to salient environmental cues. That these results primarily emerged in samples of ethnically matched normal volunteers carefully screened to exclude any lifetime history of psychiatric illness or treatment argues that they represent genetically determined biological traits not related to manifest psychiatric illness. In contrast to these striking imaging genomics findings of 5‐HTTLPR short allele–driven amygdala hyperreactivity, attempts to link these eVects on brain function with measures of emergent behavioral phenomena, namely the personality trait of harm avoidance, have failed to detect any significant relationships. Specifically, in both the initial and replication studies of Hariri et al., there were

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no significant 5‐HTTLPR genotype eVects on subjective behavioral measures of anxiety‐like or fear‐related traits as indexed by the Harm Avoidance (HA) component of the Tridimensional Personality Questionnaire, a putative personality measure related to trait anxiety and 5‐HT function (Cloninger, 1986; Cloninger et al., 1993). Although the sample sizes and thus power in both imaging cohorts were small relative to traditional behavioral association studies, the absence of an eVect of 5‐HTTLPR on HA is consistent with several published reports in larger samples (Schinka et al., 2004). Thus, these results and those of previous studies suggest that the 5‐HTTLPR does not have a robust and consistent eVect on the dimensions of anxious and fearful personality measured by the HA subset of the TPQ. Moreover, in their replication study, Hariri et al. failed to find any relationship, independent of 5‐HTTLPR genotype or other factors (e.g., age, gender, IQ), between amygdala reactivity and HA in a subsample of 83 subjects with overlapping f MRI and behavioral data sets) (Hariri et al., 2005). These findings provide compelling evidence that genetically driven diVerences in the response of brain regions underlying emotional behavior may be readily investigated in relatively small sample populations in the absence of significant diVerences in behavioral measures. They also raise the intriguing possibility that 5‐HTTLPR s allele–driven variation in phasic amygdala function biases toward a heightened brain response to environmental threat, but that this relative hyperresponsivity alone does not predict individual diVerences in harm avoidance. Although it is likely that constitutive variation in 5‐HT signaling impacts on the biology of distributed brain systems beyond the amygdala, these investigations have focused on the eVects of the 5‐HTTLPR on amygdala function, because this region plays a central role in the generation of behavioral arousal and orientation, as well as specific emotional states such as fear. It is important to emphasize that the 5‐HTTLPR s allele eVect on amygdala reactivity in the Hariri et al. studies, as well as those by Heinz et al. and Bertolino et al., exist in samples of healthy volunteers with no history of aVective or other psychiatric disorders. This is consistent with a recent f MRI study reporting that whereas amygdala hyperexcitability reflects a stable, heritable trait associated with inhibited behavior, it does not by itself predict the development of aVective disorders (Schwartz et al., 2003). The study of Caspi et al. (2003) suggests that the existence of significant stressors in the environment of individuals carrying the 5‐HTTLPR s allele is necessary to further tip the balance toward the development of pathology and illness. Similarly, abnormal social behavior (Champoux et al., 2002) and 5‐HT metabolism (Bennett et al., 2002) have been reported in rhesus macaques with the 5‐HTTLPR s allele homologue, but only in peer‐reared, and thus environmentally stressed, individuals. This shift toward pathology may reflect the eVects of environmental stress on brain regions, most notably the prefrontal cortex, critical in the regulation of amygdala activity (Hariri et al., 2003; Keightley et al., 2003; Rosenkranz et al.,

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2003). For example, the experience of environmental insult before the maturation of relatively late developing prefrontal regulatory circuits (Lewis, 1997) may result in further biased amygdala drive in s allele carriers. Such relative hyperamygdala and hypoprefrontal activity has been documented in aVective disorders (Phillips et al., 2003; Siegle et al., 2002) and thus, may reflect a critical predictive biological marker. The importance, and perhaps even necessity, of such environmental stressors acting on an extended neural circuitry in facilitating 5‐HTTLPR s allele influences on behavior is underscored by the absence of significant genotype or genotype‐by‐gender eVects on HA, as well as correlations between amygdala reactivity and HA in the replication study in healthy subjects by Hariri et al. (2005). This suggests that 5‐HTTLPR–driven variation in the responsivity of the amygdala, although robust and consistent, does not necessarily result in altered mood and temperament per se. Rather, these results suggest that individual diVerences in complex, emergent phenomena, such as harm avoidance, will likely reflect the eVects of genetic variation on a distributed brain system involved in not only mediating physiological and behavioral arousal (e.g., amygdala) but also regulating and integrating this arousal in the service of adaptive responses to environmental challenges (e.g., prefrontal cortex). Along these lines, recent imaging genomics studies have reported changes of prefrontal and auditory cortex activation (Fallgatter et al., 1999, 2004; Gallinat et al., 2003), as well as of functional coupling of the amygdala and prefrontal cortex during aVect processing (Heinz et al., 2005; Pezawas et al., 2005) in healthy s allele carriers. Pezawas et al. have demonstrated that s allele carriers exhibit altered functional coupling of the amygdala and subgenual PFC and that this coupling contributes to individual diVerences in HA. Thus, intact dynamic interactions of the amygdala and prefrontal cortex may be critical for normal behavioral responses in individuals possessing the 5‐HTTLPR s allele. Because the impact of genetically driven variation in dopamine availability (e.g., COMT) on prefrontal function has been well documented (Egan et al., 2001; Mattay and Goldberg, 2004; Winterer et al., 2005), it will be of increasing importance to model heritable variation in both amygdala and prefrontal activity in exploring the influence of genes on behavior. Furthermore, it will be of critical importance to explore the impact of acute and/or chronic environmental stress on such genetically driven variation in brain function contributing to the etiology of mood and other aVectively laden disorders. Some clarification on the system level eVects of 5‐HTTLPR beyond the amygdala may be provided by electrophysiological studies (Fallgatter et al., 1999, 2004; Gallinat et al., 2003b). These three studies investigated the impact of 5‐HTTLPR on three diVerent electrophysiological measures of inhibition (i.e., [1] on the nogo P3, which is generated in the anterior cingulate cortex [ACC] when inhibiting an anticipated motor response [Fallgatter et al., 2002], [2] on the error‐related

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negativity that is seen during conflict monitoring and contains an inhibitory subcomponent that is generated in the ventral prefrontal cortex; Lavric et al., 2004], and [3] on loudness dependence, i.e., the dependence of the [tangential] N1/P2 component amplitude increase in response to diVerent stimulus intensities—a component that is thought to be generated in layer IV of the primary auditory cortex [Hegerl and Juckel, 1993] and that has been proposed to reflect a central protection mechanism from sensory overload [Buchsbaum et al., 1976]). Notably, one of these three electrophysiological components (i.e., the loudness‐dependent N1/P2 component) has been well investigated as an indicator of serotonin neurotransmission in humans and animal models (Hegerl and Juckel, 1993; Juckel et al., 1997, 1999; Pogarell et al., 2004; von Knorring and Perris, 1981). A strong loudness dependence N1/P2‐component is thought to indicate low serotonergic activity and vice versa. As far as 5‐HTTLPR is concerned, the finding of Gallinat et al. (2003b) indicates that homozygous carriers of the 5‐HTTLPR l allele have a weaker loudness dependence, indicating higher serotonergic neurotransmission while at the same time a higher 5‐HT uptake must be assumed (Heinz et al., 2000; Lesch et al., 1996). In line with this, a higher transport capacity of the l/l genotype was suggested to exert a somatodendritic 5‐HT1a‐receptor–mediated negative feedback with an overall increase of 5‐HT neurotransmission (Lesch and Mossner, 1998). The observed 5‐HTTLPR mechanism could thus be summarized so that l‐carriers are characterized by increased serotonergic neurotransmission, which provides them with an inhibitory protective mechanism against sensory overload. Accordingly, the result is to some extent analogous to what has been described by Hariri et al. (2002) for the amygdala, whereas s‐ allele carriers are hyperresponsive. By extension, the argument could be made that the physiological 5‐HTTLPR gene eVect is observable in diVerent brain circuits in a comparable way. However, this physiological gene eVect may not be easily generalized to the entire brain. Thus, the findings of Fallgatter (1999, 2004) actually suggest for the ACC and ventral PFC an opposite mechanism of 5‐HTTLPR. Here, inhibitory eVects seem to be mediated the other way around by the s allele. Currently, it can only be speculated that the eVect of the s allele on the ACC and ventral prefrontal cortex may mediate behavioral inhibition in patients with depression and anxiety disorders. Taken together, the f MRI and electrophysiological results on the eVect of 5‐HTTLPR are striking for several reasons: they provide evidence for genetically driven diVerences in the response of brain regions that underlie emotional behavior and sensory and motor inhibition. In addition, these genetic diVerences at the neurobiological level were marked in relatively small sample populations in the absence of significant diVerences in behavioral measures. Moreover, the imaging results provide an explanation of a potential biological mechanism for the genetic association of the 5‐HTTLPR with vague psychiatric disturbances, including various dimensions of anxiety and neuroticism. Although the finding of amygdala hyperexcitability in 5‐HTTLPR s allele carriers using f MRI provides a

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potential breakthrough in our understanding of the neurobiological underpinnings of abnormal mood and aVect associated with variation in 5‐HT signaling, the intrinsic mechanisms by which this brain response bias emerges remain poorly understood. The application of additional functional neuroimaging modalities, most notably radioligand‐specific positron emission tomography (PET), may provide a powerful tool for explaining these pathways. For example, PET‐radioligands of increasing specificity and flexibility have been developed to probe both 5‐HT synthesis (Hagberg et al., 2002) and 5‐HTT availability (Meyer et al., 2001; Sandell et al., 2002; Szabo et al., 1999; Wilson et al., 2000) and could be used to determine and substantiate presumably diVerential transporter and 5‐HT levels based on the 5‐HTTLPR genotype. In this field of research, the major challenge will be to provide radioligands of suYcient sensitivity as the inherent dosing limitations when injecting radioactive substances. For instance, a lack of sensitivity, among other reasons, might have been one important issue why several independent studies using the SPECT‐ligand [123I ]beta‐CIT failed to demonstrate an unambiguous eVect of the 5‐HTTLPR on SERT availability in the area of the ncl raphe, where SERT accumulation is highest in brain (Heinz et al., 2000; Jacobsen et al., 2000; Van Dyck et al., 2004). If a lack of sensitivity, and by extension a lack of statistical power, were indeed a major reason for these contradictory results, suYciently large sample sizes would be required, which in the case of applying [123I ]beta‐CIT means more than 100 subjects, because the most recent study by Dyck et al. investigated 96 subjects without obtaining the expected results. Thus, although it seems at first glance to be particularly attractive to use pharmaco‐PET and SPECT for endophenotyping because it is the most direct (i.e. molecular) approach and therefore allows us to build hypotheses in a straightforward way, methodological and logistic limitations can be a major challenge.

VI. Conclusions

The application of neuroimaging in genetic research has experienced an exponential growth during the past decade. In the beginning of this development, electrophysiologists worked out the basic scientific principles, which were then adopted by researchers using other imaging modalities. Today, a multitude of imaging tools are available that complement each other and allow probing the genetic foundation of brain structure and function from diVerent perspectives. The entire field of genetics was revolutionized when modern molecular genetic methods became available, and it is now possible to assay directly the eVects of variations within specific genes on brain structure and function. The value and relevance of using imaging for genetic investigations became

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particularly apparent when it turned out that specific genetic eVects are not easily discernible on the behavioral or clinical level, because the latter phenotypes are in many cases under the control of multiple genes in complex interaction with the environment. The ‘‘black box’’ between genotype on one side and clinical/ behavioral phenotype on the other side is the true realm of neuroimaging in genetic research, because neuroimaging provides ‘‘endophenotypes’’ that are more directly related to the genetic level, allowing at the same time a deeper ‘‘neuroscientific’’ insight into the genetics of the brain. In the years ahead, we will likely see numerous studies conducted in this rapidly expanding field that will help us to better understand the molecular genetic mechanisms of brain function and particularly how these mechanisms interact with environmental influences. However, because the number of discovered mechanisms is growing, it will be increasingly necessary to obtain estimates on the relevance of any single and combined gene eVect in quantitative terms. Beyond the necessity to study the quantitative genetic eVect on local brain circuits, it will also be required to assess the genetic downstream eVect on distributed brain systems and how these eVects relate to clinical symptoms and behavior. Here, a critical issue will be to take into account arguments from complex system theory suggesting that large‐scale interactions between distant and indirectly connected brain regions are generally more susceptible to intervening influences than interactions between closely neighboring and directly linked brain areas (Winterer et al., 2003b). To deal with this complexity, it can be anticipated that after the ‘‘molecular genetic age’’ the next revolution will be the implementation of tools from the emerging field of computational neuroscience. Increasingly sophisticated models of the brain, which incorporate multiple layers from the network system level down to the levels of synaptic transmission and genetic transcription, will be fed with experimental data, which will be provided to some considerable extent by neuroimaging experiments. These models will allow the simulation and analysis of specific genetic eVects and how they interact with other brain mechanisms, which eventually will make it possible to make increasingly precise predictions on the properties and relevance of any genetic eVect. Ultimately, it can be expected that computational models derived from neuroimaging data will considerably contribute to the dissection of those molecular mechanisms that will give rise to new treatment options with respect to neuropsychiatric disorders.

References

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Further Readings

Bondi, M. W., Salmon, D. P., Monsch, A. U., Galasko, D., Butters, N., Klauber, M. R., Thal, L. J., and Saitoh, T. (1995). Episodic memory changes are associated with the APO"4 allele in nondemented older adults. Neurology 45, 2203–2206. Bozzali, M., Franceschi, M., Falini, A., Pontesilli, S., Cercignani, M., Magnani, G., Scotti, G., Comi, G., and Filippi, M. (2001). Quantification of tissue damage in AD using diVusion tensor and magnetization transfer MRI. Neurology 57, 1135–1137. Buchsbaum, M. (1976). Self‐regulation of stimulus intensity: Augmenting reducing and the average evoked response. In ‘‘Consciousness and self‐regulation’’ (G. E. Schwartz, and D. Shapiro, Eds.). Plenum Press, New York. Durstewitz, D., and Seamans, J. K. (2002). The computational role of dopamine D1 receptors in working memory. Neural Netw. 15, 561–572. Grundman, M., Sencakova, D., Jack, C. R., Petersen, R. C., Kim, H. T., Schultz, A., Weiner, M. F., De Carli, C., De Kosky, S. T., van Dyck, C., Thomas, R. G., and Thai, L. J. (2002). Brain MRI

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hippocampal volume and prediction of clinical status in mild cognitive impairment. J. Mol. Neurosci. 19, 23–27. Hettma, J. M., Prescott, C. A., and Kendler, K. S. (2004). Genetic and environmental sources of covariation between generalized anxiety disorder and neuroticism. Am. J. Psychiat. 161, 1581–1587. Higuchi, M., Arai, H., Nakagawa, T., Higuchi, S., Muramatsu, T., Matsushita, S., Kosaka, Y., Itoh, M., and Sasaki, H. (1997). Regional cerebral glucose utilization is modulated by the dosage of apolipoprotein E type 4 allele and alpha1‐antichymotrypsin type A allele in Alzheimer’s disease. Neuroreport 8, 2639–2643. Hirayasu, Y., Tanaka, S., Shenton, M. E., Salisbury, D. F., De Santis, M. A., Levitt, J. J., Wible, C., Yurgelun‐Todd, D., Kikinis, R., Jolesz, F. A., and McCarley, R. W. (2001). Prefrontal gray matter volume reduction in first episode schizophrenia. Cereb. Cortex 11, 374–381. Kendler, K. S., and Aggen, S. H. (2001). Time, memory and the heritability of major depression. Psychol. Med. 31, 923–928. Kiesepa¨ , T., Partonen, T., Haukka, J., Kaprio, J., and Lo¨ nnquist, J. (2004). High concordance of bipolar I disorder in a nationwide sample of twins. Am. J. Psychiat. 161, 1814–1821. Kim, J. J., Mohamed, S., Andreasen, N. C., O’Leary, D. S., Watkins, G. L., Boles Ponto, L. L., and Hichwa, R. D. (2000). Regional neural dysfunctions in chronic schizophrenia studied with positron emission tomography. Am. J. Psychiat. 157, 542–548. Kinnunen, E., Juntunen, J., Ketonen, L., Koskimies, S., Konttinen, Y. T., Salmi, T., Koskenvuo, M., and Kaprio, J. (1988). Genetic susceptibility to multiple sclerosis. A co‐twin study of a nationwide series. Arch. Neurol. 45, 1108–1111. Mazziotta, J. C., Phelps, M. E., Pahl, J. J., Huang, S. C., Baxter, L. R., Riege, W. H., HoVman, J. M., Kuhl, D. E., Lanto, A. B., Wapenski, J. A., et al. (1987). Reduced cerebral glucose metabolism in asymptomatic subjects at risk for Huntington’s disease. N. Engl. J. Med. 316, 357–362. Tramo, N. J., Loftus, W. C., Stukel, T. A., Green, R. L., Weaver, J. B., Gazzaniga, and M. S. (1998). Brain size, head size, and intelligent quotient in monozygotic twins. Neurology 50, 1246–1252. Van Beijserveldt, C. E., Molenaar, P. C. M., de Geus, E. J. C., and Boomsma, D. I. (1998a). Individual diVerences in P300 amplitude: A genetic study in adolescent twins. Biol. Psychology 47, 97–120. Wolf, S. S., Jones, D. W., Knable, M. B., Gorey, J. G., Lee, K. S., Hyde, T. M., Coppola, R., and Weinberger, D. R. (1996). Tourette syndrome: Prediction of phenotypic variation in monozygotic twins by caudate nucleus D2 receptor binding. Science 273, 1225–1227. Yasuno, F., Hasnine, A. H., Suhara, T., Ichimiya, T., Sudo, Y., Inoue, M., Takano, A., Ou, T., Ando, T., and Toyama, H. (2002). Template‐based method for multiple volumes of interest of human brain PET images. NeuroImage 16, 577–586.