Neural systems approaches to the neurogenetics of autism spectrum disorders

Neuroscience 164 (2009) 247–256

REVIEW NEURAL SYSTEMS APPROACHES TO THE NEUROGENETICS OF AUTISM SPECTRUM DISORDERS J. PIGGOT,a,c* D. SHIRINYAN,a,c S. SHEMMASSIAN,a,c S. VAZIRIANa,c AND M. ALARCÓNb,c

associated with these that could be incorporated into phenotypic assessments. © 2009 IBRO. Published by Elsevier Ltd. All rights reserved.

a Division of Child and Adolescent Psychiatry, Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, CA 90095-1769, USA

Key words: autisms, linkage, association, cytogenetics, neural systems.

b

Department of Neurology, University of California, Los Angeles, CA, 90095-1769, USA

Contents Genetic studies of autism Linkage studies Association studies Cytogenetic studies Phenotypic strategies used in genetic studies of autism What do we know about the neural systems involved in autism? Arousal systems in autism Reward systems in autism Face processing in autism How can neural systems knowledge inform genetic studies? Conclusion References

c

Center for Autism Research and Treatment, University of California, Los Angeles, CA 90095-1769, USA

Abstract—Autism is generally accepted as the most genetic of all the developmental neuropsychiatric syndromes. However, despite more than several decades of genetic study, the etiology of autism remains unknown, largely due to the genetic and phenotypic diversity, or heterogeneity, of this disorder, and the lack of biologically based classification systems. At the same time, in the neuroimaging literature, the body of research identifying candidate neural systems underlying aspects of autistic impairment has grown considerably, fueled by the advent of technologies such as functional magnetic resonance imaging (fMRI). Yet the findings from these neuroimaging studies have not been incorporated to inform the collection of samples for genetic studies of autism, which are predominantly based on a diagnosis of the disorder. This article presents a review of the genetics of autism and describes the genetic approaches that have been applied, including the phenotypic strategies that have been used to address heterogeneity and optimize the power of these genetic studies. With the increasing recognition that there may be different “autisms” (Geschwind and Levitt, 2007) with unique neural mechanisms, it is argued that neural systems research, using technologies such as fMRI, currently allows for the identification of more biologically informative phenotypes for genetic studies of autism and is positioned to identify informative neuroimaging markers for “neurogenetic” studies of the disorder. To illustrate this, we describe several candidate neural systems for the social communication impairment seen in autism, and the characteristic behavioral and physiological manifestations

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Although autism is described as a highly heritable developmental brain disorder (Steffenburg et al., 1989; Bailey et al., 1995; Schultz, 2005) it is diagnosed behaviorally through observation of impairment in three core behavioral domains. These domains referred to as the autistic “triad” of impairment include social communication impairment, restricted interests and repetitive behaviors, and communication and language impairment domains. Current clinical classification systems, such as the International Statistical Classification of Diseases, 10th Revision (ICD-10) (World Health Organization, 1994) and the Diagnostic and Statistical Manual of Mental Disorders (Fourth Edition, Text Revision) (DSM-IV-TR) (American Psychiatric Association, 2000), both categorize autism as a pervasive developmental disorder (PDD). This PDD category also includes Asperger syndrome (AS), pervasive developmental disorder-not otherwise specified (PDDNOS), Rett’s disorder, and childhood disintegrative disorder. The term autism spectrum disorders (ASD), which includes autism, AS and PDD-NOS, is now commonly used to capture autistic impairment upon a continuum including disorders that do not fulfill full diagnostic criteria for a diagnosis of autism (Wing, 1988). Importantly, the uniting feature of all ASD is social communication impairment, which has increasingly been referred to as the core deficit in this group of disorders (Tanguay et al., 1998; Schultz, 2005).

*Correspondence to: J. Piggot, Division of Child and Adolescent Psychiatry, Department of Psychiatry and Biobehavioral Sciences, Semel NPI, Suite 68 –237, 760 Westwood Plaza, Los Angeles, CA, 900951769, USA. Tel: ⫹1-310-206-4391; fax: ⫹1-310-825-2682. E-mail address: [email protected] (J. Piggot). Abbreviations: ADI-R, Autism Diagnostic Interview–Revised; ADOS, Autism Diagnostic Observation Schedule; AGRE, Autism Genetic Resource Exchange; AS, Asperger syndrome; ASD, autism spectrum disorders; CNV, copy number variation; FFA, fusiform face area; fMRI, functional magnetic resonance imaging; PDD, pervasive developmental disorder; PDD-NOS, pervasive developmental disorder—not otherwise specified; QTL, quantitative trait loci; SNPs, single nucleotide polymorphisms; SRS, Social Responsiveness Scale.

0306-4522/09 $ - see front matter © 2009 IBRO. Published by Elsevier Ltd. All rights reserved. doi:10.1016/j.neuroscience.2009.05.054

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Presently, in research settings, autism is diagnosed through the use of the Autism Diagnostic Observation Schedule (ADOS) (Lord et al., 1989) and the Autism Diagnostic Interview–Revised (ADI-R) (Lord et al., 1994). The ADOS is a standardized protocol requiring the observation and rating of a subject’s behavior on semi-structured tasks related to social behavior. The ADI-R, a structured, detailed interview of the participant’s parents covering the referred child’s developmental history, is used in conjunction with the ADOS. This combination of ADI-R and ADOS represent the current gold standard in autism diagnosis. Despite the nosological inference implicit in current categorical classification systems that autism is one disorder, there is little reason to assume that this is the case. There is indeed considerable evidence to support that autism is actually many disorders, suggesting that “autisms” may be a better terminology (Geschwind and Levitt, 2007).

GENETIC STUDIES OF AUTISM Autism is considered a complex trait and, as such, there are problems inherent in gene-identification, such as multiple gene effects, environmental risk factors, gene by gene and gene by environment interactions, variable penetrance and expressivity, and genetic diversity, or “heterogeneity” (Schork, 1997; Guo and Lange, 2000). Phenotypic and genetic heterogeneity has posed a particularly difficult challenge in autism genetics and has been blamed for the lack of replication of many reported chromosomal susceptibility regions. Although the identities of autism-causing genes remain elusive, there has been some progress in linkage, association, and cytogenetic studies. Analytic strategies that have been developed to circumvent and/or account for heterogeneity in linkage and association studies have met with some success (Wassink et al., 2008) and, where appropriate, these will be mentioned below. Linkage studies Linkage studies aim to identify genetic loci that are transmitted with autism in the families of affected individuals. Linkage analyses examine the co-transmission of a genetic marker (that represents a gene or a chromosomal region) with a trait of interest such as the diagnosis of autism, a quantitative measure of social communication or language, a neuroimaging variable, or a physiological measure, such as a measure of arousal. At least two qualitative linkage scans of autism have reported regions of interest on chromosomes 1p, 2q, 3q, 6q, 7q, 13, 16p, 17q, 19p and Xq (IMGSAC, 1998; Ashley-Koch et al., 1999; Barrett et al., 1999; Philippe et al., 1999; Risch et al., 1999; Auranen et al., 2000; IMGSAC, 2001; Shao et al., 2002; Yonan et al., 2003; Lamb et al., 2005). A recent high-density genome-wide linkage scan (Allen-Brady et al., 2009) genotyped 10,000 single nucleotide polymorphisms (SNPs) on seven affected and 22 unaffected members of a six-generation pedigree from Utah and reported significant peaks on three previously-identified regions (3q13.2-q13.31, 3q26.31-q27.3, and 20q11.21-q13.12). Recent reviews of the autism linkage literature (Abrahams and

Geschwind, 2008; Losh et al., 2008) agree that although many putative chromosomal regions have been identified, only 17q has been replicated in an independent sample (Cantor et al., 2005). The results of the largest multisite collaborative genome wide linkage study of autism, which included an international sample of 1168 affected families, were disappointing in that only one linkage peak on chromosome 11p was highlighted as “suggestive,” despite collection of the largest sample to date (Autism Genome Project Consortium, 2007). In this case, the anticipated advantage in power of pooling so many samples was not realized and in fact became a disadvantage as this strategy seemed to increase the heterogeneity in the combined sample. However, previous work does support the 11p linkage finding in 345 multiplex families from the Autism Genetic Resource Exchange or AGRE (Yonan et al., 2003). In an attempt to account for the heterogeneity in samples of sibling pairs with autism, Wassink and colleagues (2008) applied the posterior probability of linkage statistic to data from two sets of 57 families each from the Collaborative Linkage Study of Autism (CLSA). The bayesian statistic combines the evidence for and against linkage across multiple samples and family subgroups. Using this linkage method, the authors reported increased evidence for linkage of autism to four chromosomal regions (1q, 13p, 16q and Xq) and diminished evidence for two others (7q and 13q). As novel statistical tools that account for heterogeneity are applied to previously analyzed datasets, we can expect the current list of putative linkage regions to be reorganized and perhaps change altogether. Association studies Association studies aim to identify genetic variants that are over-represented in individuals with autism compared to control samples. Genetic association techniques rely on cross-sectional studies of populations to identify relationships between genetic polymorphisms and a specific phenotype’s presence in those populations (O’Roak and State, 2008). Several association studies have tested candidate genes for autism in regions of interest identified through genome-wide linkage scans (for a recent review see Yang and Gill, 2007). These studies have relied on typing a limited number of microsatellite markers or SNPs to determine variants within linkage peaks. The main problem with these candidate gene studies is the seemingly limitless number of potential “candidates” that are derived both from linkage studies and the biological literature (Schork, 1997). These candidate genes are then evaluated in isolation from other genetic investigations making it difficult to determine whether they themselves are disease-causing or whether they are in linkage disequilibrium with the true disease-causing gene. In an alternative approach, association studies have tested the contribution of candidate genes that represent pathways based on biological evidence of their involvement with autism. For example, Anderson and colleagues (2009) recently tested linkage and association of 10 5-HT pathway genes by genotyping 45 SNPs in 403 families with autism. Although their results showed no evidence for

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linkage to any of the SNPs, one marker for the gene HTR3A on chromosome 11 was significantly associated with the disorder and they identified a modest interaction of two SNPs on chromosomes 11 and 16. The results of this study illustrate the increased power of association versus traditional linkage methods. To date, there is only one published genome-wide association study of autism where 601 microsatellite markers (5.8 cM density) were typed in 12 individuals with ASD and 44 controls from the Faroe Islands. Six chromosomal regions were identified in the association scan: 2q, 3p, 6q, 15q, 16p and 18q (Lauritsen et al., 2006). In addition, results from this study also supported a region on 7q (about 20 cM away from 7q35-36) and another that overlapped with a promising linkage region on 17q (Alarcón et al., 2005). Cytogenetic studies The common variant disease model was assumed in the linkage and association studies presented previously. The common variant disease model of autism assumes that the disorder is caused by many variations or polymorphisms in the genome that are frequently present in the general population and are transmitted within families. In contrast to linkage and association approaches, cytogenetics is interested in determining the genetic etiology of disorders that fit with the rare variant genetic model, which assumes that the disorder is a result of unique rare mutations that present sporadically or “de novo” in the population and are not usually inherited. Cytogenetic approaches, such as copy number variation (CNV) detection, enable us to investigate the “rare-variant common disease model” for ASD, which refers to the idea that if a similar phenotype can be observed as a result of variation in a large number of genetic loci (genetic heterogeneity), a collection of rare changes may greatly contribute to the risk for a common disorder (O’Roak and State, 2008). CNV refers to the insertion or deletion of a fairly large (⬎50 kb) DNA fragment, which can be de novo or less frequently inherited, and may be an important cause of ASD, either as rare variants that affect risk or as potentially new syndromes related to the ASD. Standard cytogenetic studies have attributed 6%–7% of autism cases to rare structural variants. However, with higher resolution cytogenics techniques, researchers have found that some people with ASD have deletions and duplications of genetic material not found in their parents’ DNA such as 15q21 and 16p11 (Marshall et al., 2008; Weiss et al., 2008). In addition, de novo variation has been observed with much higher frequency in simplex ASD families (7%–10%) than in multiplex ASD families (2%–3%) (Sebat et al., 2007; Marshall et al., 2008). Indeed, these genetic structural abnormalities may account for 10%–20% of ASD cases, yet individually, each abnormality accounts for only 1%–2% of cases (Abrahams and Geschwind, 2008). This suggestion that the clinical presentation of autism has multiple etiologies arising from various genetic variations is consistent with the notion of “the autisms,” and if corroborated, will dramatically change our understanding of the disorder and

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have significant ramifications for its genetic and imaging studies. Phenotypic strategies used in genetic studies of autism The phenotypic expression of autism varies greatly among those affected with the disorder. Mild variations of the main impairment domains of autism, such as social or communicative impairments, repetitive or stereotyped behaviors and, especially, language deficits, are also often observed in the first- and second-degree relatives of autistic probands (Piven, 1997), irrespective of their own affected status; suggesting that they are also familial and may be genetically transmitted. These autism-related traits can be quantified and used to identify the genes underlying these specific aspects of the disorder in the families of individuals with autism. To increase phenotypic and genetic homogeneity of study samples, investigators of autism have used behavioral traits (e.g. language deficits or symptom counts) to: (1) stratify affected families into groups with common characteristics (Bradford et al., 2001; Buxbaum et al., 2001); (2) include the traits as covariates in traditional linkage analyses (Shao et al., 2002); or (3) use the traits themselves in quantitative linkage analyses to identify quantitative trait loci (QTL). QTL are genes with small to moderate effects that influence continuously distributed phenotypes, as opposed to categorical traits. In these ways, researchers can use behavioral traits to detect genes with small effects that are related to autism. The Psychiatric Genomewide Association Studies Consortium Steering Committee supports this view and the use of phenotypes rather than diagnoses for finding genes that confer risk for complex behavioral traits (Psychiatric GWAS Consortium Steering Committee, 2009). Language-related traits have been used to stratify families and identify subsets responsible for evidence of linkage to various susceptibility loci: families with language delay have been responsible for linkage results on chromosomes 2q (Buxbaum et al., 2001), 7 and 13 (Bradford et al., 2001). Other investigators have applied a more sophisticated method (ordered subset analysis) to a principal component representing “insistence on sameness,” and identified a subset of homogeneous families with autism that were responsible for linkage to a previously-reported region on 15q11-13 (Shao et al., 2003). A genome-wide linkage analysis of the quantitative trait “non-verbal communication” in families from the AGRE identified several putative QTL at 1p13-q12, 4q21-25, 7q35, 8q23-24, and 16p12-13 indicating that genes at these sites may contribute to the variation in non-verbal communication among families with ASD (Chen et al., 2006). In another genomewide quantitative trait linkage analysis, the endophenotype “age at first word” (an item from the ADI-R) was used to localize a novel region of interest on chromosome 7q35 (Alarcón et al., 2002, 2005) and to subsequently identify a gene associated with this trait in the AGRE sample (Alarcón et al., 2008). This work illustrates the progress that is possible by using quantitative traits in genetic re-

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search—from a novel linkage region to the detection of an actual autism-related gene. QTL analysis has also been applied to quantitative measures of autistic symptomatology such as the Social Responsiveness Scale (SRS), which is a dimensional measure of severity of social deficits related to ASD. The use of the SRS score to quantify social communication impairment in autistic individuals led to the identification of two loci on chromosomes 11 and 17; in contrast, no significant linkage signals were obtained with the use of the ADI-R scale in the same sample. This study highlights the potential utility of this dimensional scale in identifying susceptibility loci for social communication impairment in autism (Duvall et al., 2007). Recently, in an alternative approach, a latent class analysis method that incorporated familial dependence was applied to symptom counts from the ADI-R data of families from the AGRE (Bureau et al., 2008). The latent class-derived phenotypes showed stronger nonparametric linkage signals than the diagnosis of autism in chromosomes 3q24, 6q14, 8p12 and 16p13. The use of symptom counts to derive latent class phenotypes seems promising; however, their utility as quantitative traits in linkage analyses has not been supported (Liu et al., 2008). Liu and colleagues subjected two domains of the ADI-R from 976 families of the Autism Genome Project consortium to quantitative linkage analysis and reported no significant results. Interestingly, stratifying the sample as a function of normal cognitive ability (IQⱖ70) or first phrase delay did result in significant linkage findings on chromosomes 15q13.3-q14 and 11p15.4-p15.3, respectively. In all of these examples, the traits used to date consist of items or symptom counts from instruments based on the diagnostic criteria for autism such as the ADI-R and while these instruments have been useful to identify or narrow chromosomal regions of interest and to recently find a language gene in an autism sample (Alarcón et al., 2008), phenotypic traits representing neural systems that underlie autism behaviors and symptomatology may yield even more promising genes for autism.

WHAT DO WE KNOW ABOUT THE NEURAL SYSTEMS INVOLVED IN AUTISM? Neural systems research, primarily neuroimaging research, on the etiology of autism has identified a number of neural systems that map onto various components of the symptomatology seen in autistic disorders. The presentation of neural systems in this article is not meant to be an exhaustive review, but rather to demonstrate how neural systems research can potentially inform genetic studies of autism. In particular, candidate neural systems associated with the social communication impairment domain, the only behavior domain specific to ASD, are discussed. Three neural systems—arousal, reward, and face processing systems—are described to demonstrate the potential utility of neural systems knowledge in the selection of more biologically informative samples for genetic studies. The arousal and reward systems are likely to be involved in determining

the social significance or “salience” of stimuli, which has been shown to be difficult for individuals with autism (Klin, 2000). These neural systems are also likely to be involved in the primary regulation of some of the social communication behaviors that are reported as abnormal in autism. The face processing system is presented because there is considerable evidence of face processing abnormalities in individuals with ASD (Joseph and Tanaka, 2003; Barton et al., 2004), perhaps as a consequence of reduced experience and, subsequently, expertise in the perceptual processing of faces (Gauthier and Tarr, 1997; Grelotti et al., 2002). A seminal eye-tracking study of the social phenotype in autism related these face processing difficulties back to difficulties in determining what is socially salient in naturalistic social scenes (Klin et al., 2002a,b), which may be explained by abnormal arousal and reward responses to faces in individuals with ASD. Thus, the behavioral and physiological manifestations of these three neural systems will be reviewed as, in a practical sense, it is these phenotypes rather than imaging measures per se that are most likely to facilitate the initial selection of individuals expressing these more biologically relevant traits for largescale genetic studies. Arousal systems in autism Abnormal arousal processes have long been hypothesized as a component of autism (Hutt et al., 1965). More recently, numerous functional magnetic resonance imaging (fMRI) studies have suggested that the amygdala is a significant contributor to social processing in typically developing participants and to social processing deficits in autism (Baron-Cohen et al., 1999) specifically due to its involvement in arousal processes (Amaral and Corbett, 2003) and its role in emotional stimulus detection and evaluation (Whalen et al., 2001). It has been argued that an early abnormality in amygdala development may actually give rise to a cascade of brain developmental deficits that result in the social communication impairment seen in autism (Schultz, 2005). Most fMRI studies of autism have suggested a hypoactive “emotional brain” with less activation found in the amygdala (Whalen et al., 1998; Baron-Cohen et al., 1999; Critchley et al., 2000; Wang et al., 2004; Pinkham et al., 2008), and a reduced fusiform gyrus (Pierce and Courchesne, 2001; Piggot et al., 2004; Wang et al., 2004) compared to typically developing participants. A landmark study of gaze fixation duration, using concurrent eye tracking and fMRI, revealed that when patients with autism are actively looking at a person’s eyes, they exhibit hyperactivity in amygdala suggesting increased arousal associated with eye contact (Dalton et al., 2005); thus, reduced eye contact and gaze aversion may be a strategy to reduce amygdala-driven aversive autonomic arousal. This study also suggested that previous studies of fusiform gyrus hypoactivity may be secondary to the autistic group spending less time viewing the eyes as compared to a control group. Importantly, these abnormalities may be unique to socially relevant tasks as demonstrated by the typical performance of autistic participants on non-social tasks, which

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nevertheless rely on amygdala function (South et al., 2008). This significantly lower gaze fixation in autistic individuals was also then identified in their unaffected siblings (Dalton et al., 2007), supporting gaze fixation as a heritable trait or endophenotype in autism. The behavioral manifestations seen clinically include high baseline rates of arousal (Palkovitz and Wiesenfeld, 1980), abnormal arousal habituation rates (Barry and James, 1988), sensitivity to sensory stimuli, increased startle from unexpected sounds/speech, and slower habituation to startle stimuli than controls (Perry et al., 2007). Interestingly, higher functioning individuals with autism were found to exhibit higher skin conductance during gaze fixation and reported higher arousal than controls when viewing neutral pictures (Bolte et al., 2008). One study of electrodermal activity found that approximately 70% of the autism sample exhibited exaggerated electrodermal reactivity to social stimuli, suggesting a social-specific hyperarousal (Hirstein et al., 2001). Theoretically, amygdaladriven hyperarousal may mediate gaze aversion and, for some individuals with autism, represent the primary impairment that results in less experience and, thereby, reduced expertise with social stimuli (Gauthier et al., 1999). Although for many individuals with ASD there is an obvious hyperarousal to social stimuli, this is not measured as a phenotypic dimension of interest in individuals with autism. Elements of the ADI-R and ADOS do interrogate arousal, but these items are not aggregated to give a sense of whether the individual is or is not aroused by social situations. Indeed, these categorical diagnostic assessments are not designed to measure the severity of autistic symptomatology or determine the arousal to social situations in an affected individual; so the aggregation of relevant items would only be partially informative. The development of quantitative measures of arousal seems prudent in a disorder in which arousal abnormalities may be a primary underlying cause for many of the other autistic symptoms. Reward systems in autism Another neural system that could be explored in more depth as it may provide additional clues to the etiology of autism is that of reward systems in autism. One of the early signs of autism is reduced time spent attending to social stimuli such as faces (Dawson et al., 2004). Children who later develop autism show evidence of decreased motivation to attend to social stimuli as early as the child’s first birthday (Osterling and Dawson, 1994; Dawson et al., 1998). One hypothesis for the development of the disorder is based on this decreased motivation to attend to social stimuli (Dawson et al., 1998, 2005), reflecting a failure for children with autism to find these events “rewarding.” In this view, reduced time spent paying attention to faces, speech, and other social stimuli would result in decreased expertise in human face processing, leading to a cascade of negative consequences for the development of social cognition from social stimuli such as facial expressions (Schultz et al., 2000; Grelotti et al., 2002).

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The basal ganglia complex (including the caudate nucleus, the putamen, the globus pallidus, the subthalamic nuclei, and the substantia nigra) has been established as the major component of the brain’s reward circuit (Schultz et al., 1998). Recent neuroimaging data have begun to implicate the striatum (caudate nucleus and putamen) in high-level social functions (Delgado, 2007). Indeed, it has been demonstrated that the dorsal striatum is sensitive to the development of trust during an fMRI study of economic exchange (King-Casas et al., 2005). Another study suggested that the striatum is particularly involved when learning about people in the absence of prior information (i.e. good, or bad person) (Singer et al., 2004). A recent study demonstrated that the regions activated by social rewards overlap with those activated by monetary rewards, suggesting a unitary reward processing circuit in healthy participants (Izuma et al., 2008). Moreover, simply viewing smiling faces activates the basal ganglia (Phillips et al., 1998; Whalen et al., 1998; Phan et al., 2002). Evidence also exists for striatal activation in attachment-related relationships including mothers viewing smiling but not sad faces of their infants (Strathearn et al., 2008), participants viewing pictures of their significant others (Bartels and Zeki, 2004) and people viewing pictures of attractive people (Aharon et al., 2001). These studies suggest an important role of the human reward circuitry in developing and maintaining social relationships, in addition to face and emotion processing. While viewing faces, particularly smiling faces, is rewarding to controls (Phan et al., 2002) some have hypothesized that children with ASD do not experience activation of neural reward systems in response to socially relevant cues. Thus, viewing faces and identifying emotions may not be reinforcing in these children. For example, if a child is not rewarded for viewing faces and does so only occasionally and incidentally, the child may be more likely to develop the social deficits characteristic of ASD due to reduced experience with the social stimuli (Grelotti et al., 2002). A recent fMRI study found abnormal social reward processing in individuals with autism associated with decreased neural activation in the striatal reward systems of the autistic brain (Scott et al., 2008, submitted for publication). Reduced social reward from faces secondary to reduced activation in reward areas of the social brain, such as the striatum, offers another intriguing candidate neural system for the social communication impairment seen in autism. The lack of reward from social stimuli would be consistent with the hypothesis that children with ASD have an impairment in social reward processing (Dawson et al., 2005), and potentially offers an explanation for the social communication impairments seen in individuals with ASD for whom a lack of social motivation is a central feature of their presentation. Although for many individuals with ASD there is an obvious lack of social motivation, this is not measured as a phenotypic dimension of interest in individuals with autism. ADI-R and ADOS queries that probe for social motivation are not aggregated in the ADI-R or ADOS algorithms to give a sense of whether the individual is

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socially motivated or not. The development of quantitative measures of social motivation, and the measurement of pupillary response, which has been shown to reflect reward processing (Steinhauer and Hakerem, 1992; O’Doherty et al., 2003, 2006) would allow some insight into reward processing in ASD. Face processing in autism Some of the earliest neuroimaging studies pointed to a focal deficit in the lateral fusiform gyrus in individuals with autism, which has been termed by some as the fusiform face area (FFA) for its apparent selective activation to faces (Kanwisher and Yovel, 2006). Several investigations have reported that patients with autism do not activate, or activate to a lesser extent, the FFA (Critchley et al., 2000; Hall et al., 2003; Deeley et al., 2007). While this has been a common finding, other investigators have failed to find differences in this region (Ogai et al., 2003; Hadjikhani et al., 2004, 2007). Several studies have indicated that patients with autism activate a qualitatively different set of regions when viewing faces. Along with reduced FFA activation some studies have found that patients with autism showed an increased signal in the precuneus (Wang et al., 2004). Concurrent eye tracking and face viewing demonstrated that the level of signal in the FFA was positively correlated with time spent looking at the eyes (Dalton et al., 2005). This is an important finding in that it suggests that some (though likely not all) of the previous findings may be due to important differences in how faces are examined or “scanned” by the subject, rather than a deficit in the FFA per se. As has been pointed out (Klin, 2008) and demonstrated (Dalton et al., 2005), there must be careful consideration of differences in visual attention between groups as this alone may lead to group differences in level of activation of face processing centers. Additional considerations necessary when conducting and interpreting such studies are related to expertise. It is not clear that the FFA, in spite of its given name, is a selective face processing center rather than a center sensitive to perceptual specialization (Gauthier et al., 1999). This, paired with the fact that patients with autism spend less time attending to faces, arguably related to arousal and reward mechanisms, indicates that a deficit in the FFA is likely a secondary feature of the disorder pertaining to their reduced experience, and consequently, reduced expertise with faces (Grelotti et al., 2002). Although the primacy of deficits in the FFA is questionable, identification of individuals with ASD for whom face processing deficits may underlie their social communication impairment would remain important. Despite the swath of evidence for face processing deficits in ASD, there are often relatively few questions asked about face processing during the diagnostic assessment process. The use of screening instruments and quantitative measurements of face processing, such as eye tracking, in combination with measures of arousal and reward, would facilitate the necessary analysis and interpretation of fMRI findings during face processing in individuals with ASD.

HOW CAN NEURAL SYSTEMS KNOWLEDGE INFORM GENETIC STUDIES? While activation data from fMRI experiments for a particular neural system could ultimately be used directly to inform genetic studies, there are many issues associated with the use of functional imaging data in this way. Indeed, the use of neural heterogeneity to inform genetic studies by identifying subgroups (or symptom clusters) that map onto these systems may be problematic if the rare genetic variants hypothesis is correct, and expression of rare genetic variation or heterogeneity results in similar neural phenotypes or “phenocopies.” However, it seems unlikely from current studies that the phenotypic characteristics of individuals with differing neural etiologies are indistinguishable (Dalton et al., 2005; Dapretto et al., 2006; MeyerLindberg et al., 2008; Nacewicz et al., 2006). This approach would also be problematic if there were considerable phenotypic heterogeneity related to a particular genetic variant, or a genetic variant affected an aspect of neurodevelopment common to multiple neural systems that could account for the social communication impairment in ASD. Many of these conceptual limitations also limit current studies based on ASD diagnosis. Other issues related to using fMRI include those specific to the use of neural systems activation patterns in genetic research and those that are inherent in defining phenotypes across behavioral, imaging and genetic studies. The use of neural systems activation patterns in genetic research is increasing as evidenced by the recent emergence of so-called imaging genetic studies in the literature. There are three issues that pertain specifically to imaging genetic approaches (Hariri and Weinberger, 2003). The first is the problem of measuring effects that are relatively small, such as the likelihood of identifying significant gene-related inter-individual variability in neural function in a specific neural system, compared with the larger effects of variables such as age, IQ, gender and environmental factors. Particularly problematic in imaging genetics, especially if the rare variant hypothesis of autism is correct, is the fact that focusing on one polymorphism, set against a background of millions of polymorphisms, requires the stringent control of confounding genetic variability, such as variability due to population stratification. The second issue in imaging genetic studies is their reliance on the development of functional imaging tasks that are specific and sensitive to the neural systems under study. Thus, these tasks need to robustly engage the specific neural networks of interest as well as capture the variability across subject and control samples. The last factor that could confound the success of this approach returns to one of the main contentions presented in this article, namely the need for the selection of homogeneous groups for inclusion in these imaging genetic studies. Presumably this would be achieved through the use of behavioral and physiological measures that capture the biologically relevant phenotypic characteristics of the particular neural system being studied.

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Other challenges inherent in defining neural system phenotypes using functional imaging include numerous types of measurement error associated with the fMRI techniques themselves; cognitive confounds like attention to the stimuli that impact the intraindividual variability from trial to trial on the same fMRI experiment and inter-individual variability in participants performing the same task (Glatt and Freimer, 2002). There are also difficulties related to defining the neural systems phenotypes themselves, such as the IQ confound incurred when comparing ASD to age-matched controls and the age confound when comparing ASD to developmental controls. These issues, which are inherent in all levels of phenotypic study, are likely to be resolved only through an improved understanding of the developmental trajectories of the actual constructs being studied, be they behavioral, cognitive, neural or genetic. An improved understanding of the developmental trajectory of the cognitive constructs of interest at different developmental ages in normal samples would allow neural systems differences to be examined in the context of normative development and, potentially, avoid some of the confounds introduced when controlling for chronological age and/or IQ. While acknowledging that neuroimaging research literature is not without its inconsistencies for many of the reasons mentioned, we must also bear in mind that some heterogeneity in neural activation may actually be secondary to the inherent neural systems or biological heterogeneity present in behaviorally ascertained samples. This biological heterogeneity, in effect, confounds neuroimaging studies in much the same way that it confounds genetic research. Instead of viewing this heterogeneity as prohibiting sound neural systems investigations, the substantial variance within autism and could be viewed as offering the opportunity for better delineation of the various neural systems underlying the “autisms.” The incorporation of behavioral and physiological characteristics generated by neural systems research in neuroimaging experiments would facilitate the identification of biologically informed subgroups and quantification of biologically relevant behaviors, potentially reduce the inconsistencies in the literature, and enhance our ability to collect biologically informative samples for future genetic studies. The utility of such approaches is evident in imaging genetic studies, which are already identifying interesting candidate genes for autism susceptibility extrapolating from animal models and genetic studies. One such gene, AVPR1a, is considered a viable candidate for autism given its well-established role in determining the social behavior of the prairie vole and the marked social impairment that characterizes the disorder. Moreover, AVPR1a has been shown to be involved in the development of socialization skills in both affected and healthy controls, and to contribute to brain function in the amygdala of healthy controls (Meyer-Lindenberg et al., 2008). Not only is this gene linked to autism (on chromosome 12q; Wassink et al., 2004) but also it was recently reported that AVPR1a polymorphisms, previously reported as overand under-transmitted in autism (Kim et al., 2002), are associated with amygdala over- and under-activation and novelty seeking/harm avoidance personality traits, measured using

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the Triphasic Personality Questionnaire, in healthy controls. These studies indirectly link gene– brain– behavior in healthy controls and individuals with autism; they support the notion that individual variation in amygdala-driven arousal to fearful stimuli may be related to social communication impairment in autism and that this covariation may be governed by the same genetic variants that contribute to differences in these neural systems and social behaviors in the general population (Meyer-Lindenberg et al., 2008). These candidate-based imaging genetics approaches are extremely important; however, neural systems approaches that help identify candidate genes are also essential in the characterization of neural systems. Particularly those neural systems associated with the increasing number of rare variant cases of autism of known genetic etiology or “non-idiopathic” autism identified with the advent of high-resolution cytogenetic studies. For example, imaging studies of individuals with rare cytogenetic abnormalities such as 15q21 and 16p11 will permit a better understanding of the neural system deficits underpinning autism in these patients with a known chromosomal anomaly. This improved understanding of the neural systems, by virtue of increased genetic homogeneity, will further facilitate differentiation of unique neural mechanisms underlying the “common variant autisms.” Neural system–informed behavioral and physiological traits are imperative to: (1) stratify samples into groups in much the same way that other behavioral traits like language deficits have been used (Bradford et al., 2001; Buxbaum et al., 2001); (2) use as covariates in traditional linkage analysis; and, (3) identify QTL through quantitative linkage analyses. Indeed, behavioral and physiological traits have already begun to be used in this way. Neural systems–informed behavioral traits such as eye gaze fixation have been used for sample stratification. Indeed, one such study used this approach to split their sample into patients with and without eye gaze fixation deficits and found that those with deficits had smaller amygdalae than both patients without deficits and controls (Nacewicz et al., 2006). Such imaging findings support the existence of autistic subgroups with and without amygdala abnormalities, and further support the measurement of quantitative behaviors related to potential explanatory neural systems as a way of isolating more biologically homogeneous subgroups for genetic study. In a recent investigation of amygdala volume in individuals with autism, fixation time on the eye region of face stimuli was used as a covariate in the analysis. Inclusion of this covariate elucidated a correlation between greater amygdala volume and poorer social responsiveness measured using the SRS (Dalton et al., 2005). The incorporation of fixation time on the eye region, widely regarded as a behavioral manifestation of amygdala driven arousal to social stimuli, has further implicated this neural system in the social communication impairment seen in autism. Quantitative behavioral measurements have been successfully implemented to a certain degree already in functional neuroimaging studies. FMRI studies that have used quantitative approaches have found that activation in the pars opercularis within the autism group

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was negatively correlated with the social subscale scores from the ADOS and ADI-R (Dapretto et al., 2006). Thus, neural activations are already being related to aggregate items measured using standardized diagnostic instruments such as the ADOS and ADI-R.

CONCLUSION In conclusion, neural systems are poised to inform genetic studies by identifying the more biologically relevant behavioral and physiological characteristics of the disorder. Immediate application of neural systems knowledge to ensure the measurement of behavioral and physiological variables implicated by such neural systems research in our phenotypic batteries would enhance capacity to determine gene– brain– behavior relationships in autism. This may require the development of new quantitative instruments or a different compilation and interpretation of items in current instruments. As we improve our ability to capture genetically informative characteristics through the development of biologically informed phenotyping measures and instruments, our ability to differentiate the “autisms” will not only facilitate genetic studies but also accelerate our understanding of the neural substrates of these complex neurodevelopmental disorders.

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(Accepted 22 May 2009) (Available online 29 May 2009)