Search tips
Search criteria 


Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
Neuron. Author manuscript; available in PMC 2013 September 6.
Published in final edited form as:
PMCID: PMC3454529

Autism-Associated Promoter Variant in MET Impacts Functional and Structural Brain Networks


As genes that confer increased risk for autism spectrum disorder (ASD) are identified, a crucial next step is to determine how these risk factors impact brain structure and function and contribute to disorder heterogeneity. With three converging lines of evidence, we show that a common, functional ASD risk variant in the Met Receptor Tyrosine Kinase (MET) gene is a potent modulator of key social brain circuitry in children and adolescents with and without ASD. MET risk genotype predicted atypical fMRI activation and deactivation patterns to social stimuli (i.e., emotional faces), as well as reduced functional and structural connectivity in temporo-parietal regions known to have high MET expression, particularly within the default mode network. Notably, these effects were more pronounced in individuals with ASD. These findings highlight how genetic stratification may reduce heterogeneity and help elucidate the biological basis of complex neuropsychiatric disorders such as ASD.

Keywords: Met Receptor, Autism Spectrum Disorder, Emotion Processing, Brain Connectivity, Default Mode Network, White Matter Integrity


Over the past decade significant strides have been made toward understanding the genetic basis of autism spectrum disorder (ASD; see Geschwind, 2011 and State and Levitt, 2011 for review), a highly heritable psychiatric disorder (Bailey et al., 1995; Rosenberg et al., 2009; Hallmayer et al., 2011). Yet, due to the complexities of both ASD genetic architecture and brain-behavior relationships, great challenges remain in delineating how ASD risk genes shape the circuits underlying social behavior. Brain imaging studies have demonstrated that individual variation in task-based functional MRI activation patterns, resting state functional connectivity (rs-fcMRI), and structural connectivity measures have a strong genetic component (Chiang et al., 2010; Kochunov et al., 2010; Fornito et al., 2011; Glahn et al., 2010; Koten et al., 2009) and are altered in ASD (see Di Martino et al., 2009 and Vissers et al., 2012 for review). Thus, neuroimaging endophenotypes are ideal for bridging the gap in our understanding of how genetic risk impacts brain circuitry. Yet, both behavioral and imaging phenotypes in ASD present significant heterogeneity and substantial overlap with typical populations, often leading to discrepant findings (e.g., Cheng et al., 2010). A critical question then is how genetic variability underlies phenotypic heterogeneity, and consequently, whether stratifying by genetic risk factors can improve our understanding of the neurobiology of ASD.

Although recent estimates suggest that hundreds of genes are likely to contribute to ASD risk (Buxbaum et al., 2012), the vast majority of evidence comes from rare mutations, such as the recently described copy number variants (CNV; Marshall et al., 2008; Pinto et al., 2010) and de novo single nucleotide variants (SNV; Sanders et al., 2012; O’Roak et al., 2012; Neale et al., 2012; Lossifov et al., 2012). These mutations are rare (occurring in less than 1% of the population), may be unique to the individual, and have an estimated collective impact of 10–20% of the ASD diagnosed population. Therefore, while de novo events are conceptually important for understanding the many potential biological routes to ASD etiology, their utility for understanding phenotypic heterogeneity across the ASD population remains to be determined. Perhaps due to clinical heterogeneity, small estimated effect sizes, and limited statistical power, genome wide association (GWA) studies focusing on common variants (>5% allele frequency) have failed to yield conclusive evidence for any specific common variants influencing ASD risk when pooling data across studies (Wang et al., 2009; Weiss et al., 2009; Anney et al., 2010). However, a few notable ASD candidate genes with common variants –namely CNTNAP2 and MET– have been identified using large samples. Importantly, these variants have been replicated in independent cohorts, and follow-up studies have characterized the functional consequences of the genetic variant on gene or protein expression providing additional support (see State & Levitt 2011, and independent autism risk gene databases: SFARI Gene Base:; and Autism Knowledge Base; Xu et al., 2012). Interestingly, common variation in CNTNAP2 has been previously found to impact functional (Scott-Van Zeeland et al., 2010) and structural (Dennis et al., 2012) brain connectivity in healthy control participants. Despite a replicated common variant (MET rs1858830; Campbell et al., 2006; Campbell et al., 2008; Jackson et al., 2009) and convergent lines of molecular and cellular evidence for autism risk (Judson et al., 2011a), the impact of MET on human brain circuitry has not yet been examined.

MET is one of multiple genes encoding proteins in the ERK/PI3K signaling pathway including PTEN, NF1, and TSC1 that have been implicated in syndromic and idiopathic causes of ASD (Levitt and Campbell, 2009). In the forebrain, MET gene and protein expression is highly regulated in excitatory projection neurons during synapse formation (Judson et al., 2009; Eagleson et al., 2010; Judson et al., 2011b). MET is expressed widely in the mouse neocortex (Judson et al., 2009), but in monkeys (Judson et al., 2011b) and humans (Mukamel et al., 2011) it is far more limited, restricted to regions of temporal, occipital and medial parietal cortex - regions that contain circuits underlying the processing of socially relevant information. The clinical relevance of MET cortical expression has been exemplified by post-mortem brain studies, whereby individuals with ASD displayed 50% lower levels of MET protein in superior temporal gyrus (Campbell et al., 2007), and did not display the same temporo-frontal differential expression pattern as control subjects (Voineagu et al., 2011).

Three common variants in MET have been associated with ASD across independent cohorts (Campbell et al., 2006; Campbell et al., 2008; Jackson et al., 2009; Sousa et al., 2009; Thanseem et al., 2010). The ‘C’ variant of rs1858830 is particularly interesting because it is located in the promoter region of MET, and is functional (Campbell et al., 2006; Campbell et al., 2008; Jackson et al., 2009). The presence of the ‘C’ variant reduces nuclear protein binding to the promoter region, and decreases gene transcription in vitro by 50% (Campbell et al., 2006). As expected for a common functional variant, the ‘C’ allele correlates with lower levels of MET transcript and protein expression independent of diagnostic status (Campbell et al., 2007; Heuer et al., 2011). Common variants may increase risk but are not ‘disorder-causing.’ Intriguingly, however, rs1858830 ‘C’ allele moderates the severity of social symptoms in ASD, whereby individuals with ASD who carry this risk allele have more severe social and communication phenotypes than those who do not (Campbell et al., 2010).

The neurobiological correlates of the impact of reduced MET expression in humans have been examined in Met conditional knockout (Met-cKO) mice (Judson et al., 2009; Judson et al., 2010; Qiu et al., 2011). Neocortical pyramidal neurons in Met-cKO mice exhibited altered dendritic architecture and increased spine head volume (Judson et al., 2010), as well as a concomitant increase in local interlaminar excitatory drive onto corticostriatal neurons (Qiu et al., 2011). This finding of heightened local-circuit connectivity is highly relevant to ASD risk and the current hypothesis regarding increased local circuit connectivity and decreased long-range connectivity of brain networks in individuals with ASD (Belmonte et al., 2004; Just et al., 2004; Courchesne and Pierce, 2005; Geschwind and Levitt, 2007). MRI evidence of long-distance underconnectivity in ASD using both structural and functional MRI is extensive, and although heterogeneity is common among ASD and even TD subjects, some consistent themes have emerged (Vissers et al., 2012). For example, reduced functional connectivity in distributed brain networks in ASD has been reported across a variety of cognitive tasks (e.g., Castelli et al., 2002; Just et al., 2004; Villalobos et al., 2005; Kleinhans et al., 2008) and when measuring task-independent (intrinsic) connectivity for interhemispheric (Dinstein et al., 2011; Anderson et al., 2011b) and anterior-posterior connections (Cherkassky et al., 2006; Kennedy and Courchesne, 2008; Monk et al., 2009; Weng et al., 2010; Assaf et al., 2010; Rudie et al., 2011), particularly within the default mode network (DMN; Raichle et al., 2001). The DMN is involved in socio-emotional processing including mentalizing and empathizing, which are classically impaired in individuals with ASD. Additionally, several diffusion tensor imaging (DTI) studies have reported reduced white matter integrity of anterior-posterior and interhemispheric tracts in ASD (Barnea-Goraly et al., 2004; Alexander et al., 2007; Sundaram et al., 2008; Shukla et al., 2011). However, DTI studies have been less consistent with regard to the precise tracts involved, with some studies even reporting tracts with higher fractional anisotropy (FA) in ASD (Cheung et al., 2009; Cheng et al., 2010; Bode et al., 2011). Interestingly, a recent study found that unaffected siblings of individuals with ASD have similar alterations in FA (Barnea-Goraly et al., 2010), suggesting the alterations in white matter integrity may represent a marker of genetic risk for ASD.

Based on the convergent genetic, clinical, and neurobiological findings regarding MET as a candidate for mediating ASD risk, the dramatic restriction of primate neocortical expression to regions that are implicated in ASD dysfunction (Judson et al., 2011b: Mukamel et al., 2011), and the functional nature of the common risk allele in regulating levels of gene expression, we hypothesized that analysis of the MET promoter variant would be a powerful tool to examine functional heterogeneity in structural and functional neuroimaging endophenotypes. We tested this prediction by examining the relationship between MET risk genotype and functional activation patterns to social stimuli, DMN functional connectivity, and the integrity of major white matter tracts. Additionally, we hypothesized that the MET promoter variant would help address ASD heterogeneity by clustering a unique subset of individuals with the diagnosis such that individuals with ASD and the rs1858830 MET risk allele would exhibit the greatest alterations in structural and functional endophenotypes. In addition to characterizing MET’s role in these circuits, our findings support a basic strategy of population stratification with multimodal imaging and genetics that may reveal specific mechanisms underlying phenotypic heterogeneity.


A total of 162 children and adolescents including 75 with an ASD and 87 who were typically developing (TD) contributed data to one or more of the three neuroimaging experiments (Table S1). This included a task-based fMRI experiment involving the passive observation of emotional faces (N=144), a resting state fMRI scan (N=71), and a diffusion-weighted scan (N=84). DNA was extracted from saliva samples and the MET variant, rs1858830, was genotyped by direct resequencing. Individuals carried 0, 1, or 2 of the rs1858830 C “risk” alleles. There were three genotype groups: a CC homozygous risk group (30.2% of sample), a CG heterozygous intermediate risk group (49.4% of the sample), and a GG homozygous non-risk group (20.3% of the sample). Thus, the terminology (i.e., “risk” versus “non-risk” group) used hereafter refers to both TD and ASD individuals with specific MET genotypes. Genotypes observed Hardy-Weinberg Equilibrium (χ2 = 0.001, p = 0.973) and in this sample we did not observe an enrichment of the risk allele in individuals with ASD (Fisher’s exact test, p = 0.654). However, it should be noted that our sample, like other neuroimaging studies, is small for standard genetic association testing, and the study sample consisted of high functioning individuals with ASD. Prior studies have shown an enrichment of the MET risk allele in individuals with ASD, particularly in multiplex families (two or more children with ASD; Campbell et al., 2006) and in the most highly impaired individuals with ASD (Campbell et al., 2010).

In each of the three datasets, genotype groups did not differ by age, gender, head motion, IQ, or ASD diagnoses; similarly, there were no differences between diagnostic groups in age, gender, or head motion (Table S1). However, consistent with prior reports (Campbell et al., 2010), ASD homozygous risk and heterozygous risk groups had significantly higher levels of social impairment (Autism Diagnostic Observation Scale, ADOS, Lord et al., 2000, social subscale, p = 0.001) than the ASD homozygous non-risk group. IQ did not differ between the ASD homozygous non-risk group and all TD groups (homozygous risk, heterozygous risk, and homozygous non-risk) but was significantly lower in both ASD homozygous risk and heterozygous risk groups; thus, we included full scale IQ as a covariate in all analyses examining the effect of an ASD diagnosis. Additionally, given that the inheritance pattern (additive, dominant, or recessive) of the genotype effect is not clearly established, for all datasets we first focused on a direct contrast between the homozygous risk (CC) and non-risk (GG) groups collapsed across diagnostic status (with diagnostic status as a covariate). In addition, we performed whole brain analyses comparing TD and ASD groups collapsed across genotype. Following these initial whole-brain analyses, we used the regions differing between the homozygous risk and non-risk groups as a single region of interest (ROI) in analyses that included the intermediate genotype group and which were further stratified by diagnostic status. This approach allowed us to compare all possible subgroups in a sensitive and unbiased fashion.

Functional Activation Patterns to Emotional Faces

We performed fMRI in a cohort of 144 children and adolescents, including 78 typically-developing (homozygous risk: N=28, heterozygous risk: N=34, homozygous non-risk: N=16) and 66 diagnosed with ASD (homozygous risk: N=15, heterozygous risk: N=39, homozygous non-risk: N=12; Table S1), during passive observation of faces displaying different emotions (angry, fearful, happy, sad, and neutral; with fixation crosses directing attention to the eye region as previously reported (Dapretto et al., 2006; Pfeifer et al., 2008; Pfeifer et al., 2011). Across all subjects (independent of diagnosis), we observed strong correlations between the MET risk allele and unique patterns of functional brain activity. Remarkably, compared to the non-risk group (N=28), the risk group (N=43) displayed a pattern of hyperactivation and reduced deactivation in the specific regions in which MET is expressed in primates and humans (Mukamel et al., 2011; Judson et al., 2011b; Figure 1A, Table S2). The risk and non-risk groups both activated primary/secondary visual cortices, thalamus, and amygdala; however, the risk group activated amygdala and striatum more robustly than the non-risk group. Additionally, the non-risk group displayed widespread deactivation (i.e., reduced activity while viewing faces vs. fixation crosses). The deactivation was most prominently displayed in midline structures of the DMN including the posterior cingulate cortex (PCC) and perisylvian regions centered on primary auditory cortex. In contrast, the intermediate risk group did deactivate, but not to the same extent as the non-risk group, and the risk group appeared to show slight activation in these regions on average (Figure 1B). In a whole-brain comparison between TD and ASD groups there was also evidence for reduced deactivation in similar temporal, frontal, and subcortical regions in individuals with ASD (Figure S1A). To investigate the risk allele’s inheritance pattern, we compared the average activity across regions differing between the risk and non-risk groups for all three genotype groups stratified into either TD or ASD subgroups. We found that the MET promoter variant has a differential penetrance between neurotypical and autistic individuals. Specifically, TD individuals with one risk allele showed a similar deactivation pattern to those without a risk allele (Figure 1B). In contrast, in individuals with ASD, one MET risk allele was sufficient to give rise to the atypical pattern of functional activity, showing less deactivation than the non-risk group. In fact, when comparing those with one risk allele, individuals with ASD exhibited significantly less deactivation in these regions compared to TD subjects, indicative of an even more atypical phenotype in the clinical population with the same MET risk genotype. Consistent with the ROI analysis, a whole-brain comparison of TD versus ASD subgroups within the heterozygous risk group found stronger and more widespread differences than those observed when comparing the TD and ASD groups across genotype (Figure S1B; Table S3).

Figure 1
Functional MRI activation patterns to emotional faces in MET risk carriers. A) Within group whole-brain activation (orange) and deactivation (blue) maps for CC “risk” group, GG “non-risk” group, and between groups (risk>non-risk; ...

Default Mode Network Functional Connectivity

Based on prior reports of altered DMN function in ASD (Kennedy et al., 2006; 2008) and MET’s high expression in the PCC (Judson et al., 2011b), as well as our finding of atypical DMN deactivation in MET risk carriers, we next examined the extent to which the MET functional risk variant modulates intrinsic DMN functional connectivity. We used a seed centered in the PCC (Fox et al., 2005) for whole-brain functional connectivity analyses in rs-fcMRI data in a matched sample of 33 typically-developing and 38 children and adolescents diagnosed with ASD. The results were remarkably consistent with the functional activation findings; the MET risk genotype significantly modulated functional connectivity, such that those in the highest risk group (CC; N=16) had reduced intrinsic connectivity between the PCC and MPFC as well as other nearby regions in the PCC compared to the non-risk group (N=16; Figure 2A, Table S4). In agreement with the functional activation analyses, the heterozygous risk group diagnosed with ASD (N=24) showed a pattern of functional connectivity similar to that observed in the homozygous risk group, whereas functional connectivity in the TD heterozygous risk group (N=15) was no different than the homozygous non-risk group. Collapsed across genotype, the ASD group exhibited reduced PCC-MPFC connectivity relative to the TD group (Figure 2B). A whole-brain analysis comparing TD and ASD groups independent of genotype revealed similar, and even more extensive, reductions in DMN connectivity as a function of ASD diagnosis (Figure S2B). This diagnostic effect appeared to be partially driven by a stronger penetrance of the MET risk allele in the ASD group, as significant differences between TD and ASD subgroups were only observed in risk carriers (Figure 2B); indeed, MET genotype explained 1.7 times as much variance in DMN connectivity in autistic relative to neurotypical individuals. Using an additional seed within the MPFC, we confirmed that both short- and long-range intrinsic DMN functional connectivity was reduced as a function of both MET risk genotype and ASD diagnosis (Figure S2D; Table S5).

Figure 2
Reduced default mode network (DMN) functional connectivity in MET risk carriers. A) DMN connectivity within CC “risk” group, GG “non-risk” group, and between groups (risk>non-risk; purple). B) Averages and standard ...

White Matter Structural Connectivity

To obtain a third line of evidence for the impact of the MET risk allele on brain circuitry, we examined the structural integrity of white matter tracts across the whole brain in a cohort of 84 children and adolescents (TD: N=38, ASD: N=46). Notably, the MET risk genotype predicted marked reductions in FA across a restricted number of major white matter tracts known to connect the very same regions previously implicated in our functional connectivity analyses. Compared to non-risk allele homozygotes (N=19), risk allele homozygotes (N=23) displayed lower FA in multiple major tracts in temporo-parieto-occipital regions that exhibit high MET expression developmentally (i.e., splenium of the corpus callosum, superior/inferior longitudinal fasciculus, and cingulum; Figure 3A; Table S6). Consistent with the observed functional connectivity patterns, in these tracts the MET risk allele had a stronger impact in individuals with ASD (Figure 3B), explaining nearly twice (1.9 times) as much variance in the ASD group. More specifically, ASD heterozygous risk allele carriers (N=25) and homozygous risk allele carriers (N=12) both exhibited strong reductions in FA, whereas structural connectivity was only significantly impacted in TD homozygous risk carriers (N=11). This was also true for follow-up whole-brain analyses looking at the additive effect of the MET risk allele in the TD and ASD groups independently (Figure S3). Somewhat surprisingly, whole-brain analyses directly comparing TD and ASD groups, independent of genotype, found relatively minimal reductions in FA for the ASD compared to TD group (Figure S3; Table S6).

Figure 3
Reduced white matter integrity in MET risk carriers. A) Results of Tract-Based Spatial Statistics analysis comparing fractional anisotropy (FA) in GG “non-risk” group vs. CC “risk” group (p<0.05, corrected). B) ...

Correlation Between Imaging and Behavioral Measures

Within the ASD group, we correlated scores on the ADOS social subscale (Lord et al., 2000), with measures derived from the imaging analyses. Lower levels of deactivation while viewing emotional expressions, as well as functional and structural connectivity were significantly associated with higher levels of social impairment in the ASD group overall (Figure S4). However, as previously noted, we also found a direct relationship between MET risk genotype and increased symptom severity within individuals with ASD. Indeed, the relationship between brain circuitry and symptom severity was no longer significant when co-varying for MET risk genotype, suggesting that MET risk genotype may contribute to both alterations in brain circuitry and disrupted social behavior.


In the present study, we used a multimodal imaging genetics approach to examine the impact of a common functional variant in MET on neuroimaging endophenotypes known to be disrupted in ASD. First, we found that, irrespective of clinical diagnosis, the functional promoter ‘C’ allele of MET alters functional activity patterns to social stimuli, DMN functional connectivity, and white matter integrity. Second, individuals with ASD exhibited similar circuit alterations for all three measures. Third, the MET risk allele appeared to have a stronger impact across individuals with ASD, especially within the heterozygous risk group. Fourth, the most impacted circuits in our study included the very regions that exhibit the greatest MET expression in the developing neocortex, including circuits that subserve processing of socially-relevant information. And lastly, measures of structural and functional circuitry correlated with symptom severity in the expected direction, although this correlation was driven by the fact that MET risk genotype was associated with both increased symptom severity and alterations in brain circuitry. These findings highlight a key principle that is consistent with the concept of endophenotypes (Gottesman et al., 2003), whereby a functional risk allele predisposing to a disorder will have a larger impact on disorder-relevant phenotypes (i.e., relevant to the function of the gene) than the disorder itself. Thus, the present data suggest that taking into account MET risk genotype will serve as a sound strategy to stratify individuals with ASD and gain insight into the neurobiological bases of the functional heterogeneity that characterizes ASD (Figure 4).

Figure 4
Schematic depicting a strategy for addressing phenotypic heterogeneity (applicable across different disorders). The shading of the ovals indicates variability in a given phenotypic measure (e.g., brain connectivity). The green outline of the ovals indicates ...

Functional Activation Patterns

In our analyses, we first focused on functional activation patterns in response to the passive observation of emotional facial expressions in a large sample of 66 ASD and 78 TD subjects. The high expression of MET in ventral temporal cortex, including the amygdala and fusiform gyrus, prompted us to test whether the C risk allele might impact activity in these regions in response to socially-relevant and affect-laden stimuli. While early studies of emotional face processing documented amygdala and fusiform hypoactivation in ASD (Baron-Cohen et al., 2000; Critchley et al., 2000; Schultz et al., 2000), later studies that better controlled for eye gaze (such as a fixation cross that directs gaze at the eyes, similar to the one used in the present study) found either no differences or hyperactivation in these regions (Hadjikani, 2004; Peirce et al., 2004; Dalton et al., 2005; Monk et al., 2010). Here we found that MET risk genotype was associated with hyperactivation of amygdala and striatum, as well as the relatively unexpected finding of reduced deactivation in temporal and midline neocortex. These latter areas comprise circuits that have the highest MET expression in developing humans and monkeys (Judson et al., 2011b; Mukamel et al., 2011). In whole-brain analyses comparing TD and ASD groups, we also found evidence for reduced deactivation in temporal and DMN regions in ASD subjects, although there were no significant differences in amygdala and regions of occipital fusiform gyrus corresponding to the fusiform face area.

Overall, the MET risk group and ASD subjects (particularly the intermediate risk group) showed less deactivation in multiple cortical and subcortical regions. Deactivation is a less well-characterized phenomenon in fMRI, but the DMN is known to show signal decreases in response to a variety of tasks requiring externally directed attention (Raichle et al., 2001). Interestingly, task induced DMN deactivation was shown to have a neuronal origin (Lin et al., 2011), so it may relate to intrinsic inhibitory properties of local cortical circuits. Few studies have focused on differences in deactivation in ASD, but our findings are highly consistent with those of Kennedy, et al. (2006), who reported that individuals with ASD exhibit less deactivation within regions of the default mode network. The auditory cortex is also known to deactivate during visual tasks (Laurienti et al., 2002; Mozolic et al., 2008) and, in our study, the auditory cortex exhibited the strongest deactivation differences between genotype groups during this visual task. These findings of reduced deactivation of perisylvian and DMN regions in MET risk carriers may relate to a failure to appropriately suppress neuronal activity, perhaps through an enhancement of local connectivity that was influenced by MET during development, as reported in the Met mutant mouse (Qiu et al., 2011). Future imaging and neurophysiological studies are needed to test this hypothesis.

Functional And Structural Connectivity

The fact that MET risk carriers displayed altered DMN deactivation patterns further prompted us to test whether the risk allele impacts intrinsic functional connectivity in this network, particularly since DMN connectivity has consistently been shown to be disrupted in ASD (Cherkassky et al., 2006; Kennedy and Courchesne, 2008; Monk et al., 2009; Weng et al., 2010; Assaf et al., 2010; Rudie et al., 2012). Indeed we found that MET risk carriers and individuals with ASD exhibited reductions in long- as well as short-range DMN connectivity. The combination of reduced deactivation and connectivity supports the notion that the DMN is both less integrated with itself and less segregated from other neural systems in both MET risk carriers and individuals with ASD (Rudie et al., 2012). Additionally, these findings suggest that functional alterations in the DMN represent a trait marker shared in those with, or at risk for, ASD. Future work should characterize functional connectivity alterations in other networks as a function of the MET risk allele.

Next, we examined whether structural connectivity was altered in MET risk carriers, as the MET protein is highly expressed during axon outgrowth in specific white matter tracts in primates (Judson et al., 2011b). Remarkably, the presence of the MET risk allele was associated with much stronger disruptions in white matter integrity than having an ASD diagnosis. The effects were most pronounced in temporo-parietal regions of high MET expression and especially within the splenium, which includes fiber pathways originating from the posterior cingulate/precuneus of the DMN. This hub region, implicated in all three imaging analyses, has been characterized as the structural core of the human connectome (Hagmann et al., 2008). The combined array of imaging findings is consistent with experiments showing the involvement of MET in neurodevelopmental processes including dendritic and axon growth and synaptogenesis that underlie circuit development (Judson et al., 2011a for review). The reduction in MET expression due to the functional promoter polymorphism may affect structure formation and ongoing synaptic function independently. Additional work is needed to clarify structure-function relationships with regard to both MET-mediated and ASD-general alterations in connectivity.

Enhanced Effect of MET Risk Allele in ASD

Perhaps most surprising, the cumulative data suggest that the MET ‘C’ risk allele has a greater effect in individuals with ASD. Beyond the rare, highly penetrant SNVs and CNVs, ASD appears to have a combinatorial etiology (Geschwind, 2010), likely due to the influence of other factors that shape circuits underlying social behavior and communication. Across all three imaging measures, the neuroimaging endophenotypes of the ASD intermediate-risk (heterozygote) group were similar to those observed in the high-risk (homozygote) group, whereas the neuroimaging phenotypes of the TD intermediate-risk group resembled those of the non-risk group. This is consistent with the notion that multiple genetic and/or environmental factors contribute to both disrupted MET expression and atypical circuitry in individuals with ASD. In fact, we previously found that carriers of a common risk allele in CNTNAP2 also display alterations in functional and structural connectivity (Scott-Van Zeeland et al., 2010; Dennis et al., 2012). In addition to CNTNAP2 and MET modulating brain connectivity, transcription of both genes is regulated by FOXP2 (Vernes et al., 2008; Mukamel et al., 2011), which is known to pattern speech and language circuits in humans (Konopka et al., 2009). Consistent with a multiple hit model, these findings collectively indicate that in individuals with ASD, who likely have additional alterations in the MET signaling pathway, the presence of the MET promoter risk allele results in more severely impacted brain circuitry and social behavior.

Relevance to ASD Connectivity Theories

The converging imaging findings reported here provide a mechanistic link, through MET disruption, to the previously hypothesized relationship between altered local circuit and long-range network connectivity in ASD (Belmonte et al., 2004; Courchesne et al., 2005; Geschwind and Levitt, 2007; Qiu et al., 2011). Moreover, the present results draw a striking parallel with alterations in neuronal architecture and synaptic functioning abnormalities found in Met-disrupted mice (Judson et al., 2010; Qiu et al., 2011). Local circuit hyperconnectivity at the neocortical microcircuit level seen in conditional Met null/heterozygous mice may lead to the hyperactivation/reduced deactivation we observed in humans with MET risk alleles. While speculative at this point, this may in part account for the presence of enhanced visual and auditory discrimination (Baron-Cohen et al., 2009; Jones et al., 2009; Ashwin et al., 2009) or sensory over-responsivity, observed in some individuals with ASD (Ben-Sasson et al., 2007; Baranek et al., 2006). Alterations in local-circuit connectivity and/or structural connections may ultimately hinder the typical formation of long-range connectivity (Dosenbach et al., 2010) observed in both MET risk allele carriers and individuals with ASD.

Addressing Phenotype Overlap and Heterogeneity in Neurodevelopmental Disorders

We found that structural and functional connectivity were related to autism symptom severity, particularly in the social domain. However, this relationship was mediated by the fact that the MET risk allele was associated with increased symptom severity and reduced functional and structural connectivity. This result, in combination with the finding that, across all imaging measures, TD individuals with two risk alleles exhibited more ‘atypical’ brain circuitry than individuals with ASD carrying no risk alleles, reveals one possible generalized mechanism for phenotype overlap that is observed across non-clinical and clinical groups (Figure 4). This raises critical issues regarding the causal nature of altered connectivity findings in ASD, and the role of a combination of genetic and environmental factors that may contribute to phenotypes that collectively lead to a clinical diagnosis. The idea that functional and structural alterations may at least in part reflect genetic vulnerability is also supported by recent studies showing greater similarity in brain measures between individuals with ASD and their unaffected siblings than between controls and unaffected siblings (Kaiser et al., 2010; Spencer et al., 2011), which is particularly the case for DTI measures (Barnea-Goraly et al., 2010). The present study highlights the critical need for future research to take into consideration relevant genetic factors to parse the heterogeneity present in neurodevelopmental disorders and behavioral phenotypes (Figure 4) to ultimately improve diagnostic or prognostic tools (Fox and Greicius, 2010).

Limitations and Future Directions

Although these findings are useful for developing a more mechanistic understanding of the neurobiology of ASD, the present study focuses on common variation in a single candidate gene. Future work should characterize the additive effects of, and interactions between, multiple risk alleles in the context of both typical and atypical development. Future research should also attempt to combine different genetic, structural and functional measures to test the direction of influence that these may have on one another at the individual level. These types of analyses will require much larger datasets likely available only through large-scale collaborative efforts such as the human connectome project (HCP; Marcus et al., 2011) and the autism brain imaging data exchange (ABIDE), a grass roots initiative under the international neuroimaging datasharing initiative (INDI; Biswal et al., 2010). Additionally, given that some network alterations are present in typical individuals who simply carry risk alleles, future study designs should include unaffected siblings to tease apart alterations that are related to genetic risk for ASD (i.e., present in both affected and unaffected siblings) from those that are specific to actually having the disorder (i.e., present only in sibling with an ASD diagnosis).


Here we show how a functional ASD risk allele predisposes to ASD by affecting functional activity, connectivity and white matter tract integrity in regions involved in social cognition. To our knowledge, this is the first study to report converging evidence of altered brain function and connectivity across three different brain measures, both in individuals with a disorder and those carrying a genetic risk factor for that disorder. These findings have a number of broad implications. First, these results reveal an enhanced penetrance of a risk allele within individuals with ASD, reflecting a novel mechanism whereby a common functional variant that is not disorder-causing, but in the context of other factors related to ASD etiology, has a larger effect on network structure and function than in neurotypical individuals. Second, given that differences between ASD and controls were moderated by MET risk genotype and in the case of functional activity were only revealed when the cohort was stratified by MET genotype, these data demonstrate the power of utilizing genetic data for understanding and parsing phenotypic heterogeneity in ASD as well as other neuropsychiatric disorders characterized by considerable heterogeneity (e.g., Rasetti and Weinberger, 2011; Figure 4). This approach may provide a more sensitive means to identify subgroups of individuals with particular risk alleles and brain circuitry for whom targeted treatments may be developed. Finally, expanding upon our prior findings linking a CNTNAP2 common variant to brain connectivity (Scott-Van Zeeland et al., 2010; Dennis et al., 2012), the discovery that the MET risk allele has large effect sizes on structural and functional brain circuitry in both typical and atypical development indicates that some alterations in brain networks in ASD may, in part, reflect genetic vulnerability, or liability, rather than causal mechanisms. Taken together, the current results indicate that considering relevant genetic factors when interpreting neuroimaging data will greatly aid in understanding, and ultimately treating, ASD and other clinically and genetically heterogeneous neuropsychiatric disorders.



High-functioning children and adolescents with ASD and TD children were recruited from the greater Los Angeles area to participate in this study. Details regarding recruitment, consent and sample demographics are included in Supplemental Experimental Procedures and Table S1.


Subjects provided saliva samples for genetic analysis. DNA was isolated from saliva using standard protocols from the OraGene Collection Kit (DNA GenoTek, Ontario, Canada). Genotypes at rs1858830 were determined by direct sequencing, as described elsewhere (Campbell et al., 2007) and detailed in the Supplemental Experimental Procedures.

MRI Data Acquisition

A total of 75 individuals with ASD and 87 TD individuals were included in at least one of the three datasets (fMRI, rs-fcMRI, and DTI) detailed in Table S1. The fMRI data were collected across two scanners (Siemens 3T Trio and Siemens 3T Allegra), while all of the DTI and rs-fcMRI data were collected on a Siemens 3T Trio scanner. See Supplemental Experimental Procedures for MRI acquisition details.

Functional MRI Task Data Analysis

Participants underwent a rapid event-related fMRI paradigm in which participants simply observed faces displaying different emotions (see Dapretto et al., 2006; Pfeifer et al., 2008; Pfeifer et al., 2011). These data underwent standard fMRI preprocessing including motion correction, brain extraction, spatial smoothing and normalization to standard space. The contrast of all emotional faces versus null events was examined at the group level using a mixed effects model. See Supplemental Experimental Procedures for further details.

Resting State functional connectivity MRI Data Analysis

In a single resting state session, subjects were told to relax and keep their eyes open while a fixation cross was displayed on a white background for 6 minutes. In addition to all of the pre-processing steps described above for the task-related fMRI scan, we band pass filtered (0.1 Hz > t > 0.01 Hz) the data and regressed out nuisance covariates, including 6 rigid body motion parameters, volumes corresponding to motion spikes, and average white matter (WM), cerebrospinal fluid (CSF) and global time-series. Average time-series from 5mm radius spheres in the PCC and MPFC within the default mode network (Fox et al., 2005) were correlated with every voxel in the brain to generate connectivity maps for each subject, which were compared between participants using ordinary least squares regression. See Supplemental Experimental Procedures for further details.

DTI Data Analysis

We examined fractional anisotropy across the whole brain using Tract Based Spatial Statistics (TBSS version 1.2; Smith et al., 2006). Data analysis consisted of removal of images with gross artifacts, motion and eddy current correction, brain extraction, fitting a tensor model and calculating FA at each voxel, non linear registration to a template brain in standard space, skeletonization of tracts and voxelwise inference testing through permutation testing as implemented with randomise. See Supplemental Experimental Procedures for further details.

Supplementary Material


This work was supported by NICHD grant P50 HD055784 (S.Y.B), NIMH grants R01 HD06528001 (S.Y.B), NIMH 1R01 MH080759 (P.L), T32 GM008044 (J.D.R), T32 MH073526-05 (J.D.R), NIH grants (RR12169, RR13642, and RR00865) and Autism Speaks. We thank Z. Shehzad, B. Abrahams, and K. Eagleson for commenting on the manuscript as well as J. Pfiefer, K. McNeally, L. Borofsky, and B. Way for help with data collection.


Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

Supplemental Information

Supplemental Information includes four figures, six tables, and Supplemental Experimental Procedures, which can be found with this article online.


  • Alexander AL, Lee JE, Lazar M, Boudos R, DuBray MB, Oakes TR, Miller JN, Lu J, Jeong EK, McMahon, et al. Diffusion tensor imaging of the corpus callosum in Autism. Neuroimage. 2007;34:61–73. [PubMed]
  • Anderson JS, Druzgal TJ, Froehlich A, Dubray MB, Lange N, Alexander AL, Abildskov T, Nielsen JA, Cariello AN, Cooperrider JR, et al. Decreased Interhemispheric Functional Connectivity in Autism. Cereb Cortex. 2010;21:1134–1146. [PMC free article] [PubMed]
  • Anderson JS, Nielsen JA, Froehlich AL, Dubray MB, Druzgal TJ, Cariello AN, Cooperrider JR, Zielinski BA, Ravichandran C, Fletcher PT, et al. Functional connectivity magnetic resonance imaging classification of autism. Brain. 2011;134:3742–3754. [PMC free article] [PubMed]
  • Anney R, Klei L, Pinto D, Regan R, Conroy J, Magalhaes TR, Correia C, Abrahams BS, Sykes N, Pagnamenta, et al. A genomewide scan for common alleles affecting risk for autism. Hum Mol Genet. 2010 [PMC free article] [PubMed]
  • Ashwin E, Ashwin C, Rhydderch D, Howells J, Baron-Cohen S. Eagle-eyed visual acuity: an experimental investigation of enhanced perception in autism. Biol Psychiatry. 2009;65:17–21. [PubMed]
  • Bailey A, Le Couteur A, Gottesman I, Bolton P, Simonoff E, Yuzda E, Rutter M. Autism as a strongly genetic disorder: evidence from a British twin study. Psychol Med. 1995;25:63–77. [PubMed]
  • Baranek GT, David FJ, Poe MD, Stone WL, Watson LR. Sensory Experiences Questionnaire: discriminating sensory features in young children with autism, developmental delays, and typical development. J Child Psychol Psychiatry. 2006;47:591–601. [PubMed]
  • Barnea-Goraly N, Kwon H, Menon V, Eliez S, Lotspeich L, Reiss AL. White matter structure in autism: preliminary evidence from diffusion tensor imaging. Biol Psychiatry. 2004;55:323–326. [PubMed]
  • Barnea-Goraly N, Lotspeich LJ, Reiss AL. Similar white matter aberrations in children with autism and their unaffected siblings: a diffusion tensor imaging study using tract-based spatial statistics. Arch Gen Psychiatry. 2010;67:1052–1060. [PubMed]
  • Baron-Cohen S, Ashwin E, Ashwin C, Tavassoli T, Chakrabarti B. Talent in autism: hyper-systemizing, hyper-attention to detail and sensory hypersensitivity. Philos Trans R Soc Lond B Biol Sci. 2009;364:1377–1383. [PMC free article] [PubMed]
  • Baron-Cohen S, Ring HA, Bullmore ET, Wheelwright S, Ashwin C, Williams SC. The amygdala theory of autism. Neurosci Biobehav Rev. 2000;24:355–364. [PubMed]
  • Ben-Sasson A, Cermak SA, Orsmond GI, Tager-Flusberg H, Carter AS, Kadlec MB, Dunn W. Extreme sensory modulation behaviors in toddlers with autism spectrum disorders. Am J Occup Ther. 2007;61:584–592. [PubMed]
  • Biswal BB, Mennes M, Zuo XN, Gohel S, Kelly C, Smith SM, Beckmann CF, Adelstein JS, Buckner RL, Colcombe S, et al. Toward discovery science of human brain function. Proc Natl Acad Sci U S A. 2010;107:4734–4739. [PubMed]
  • Bode MK, Mattila ML, Kiviniemi V, Rahko J, Moilanen I, Ebeling H, Tervonen O, Nikkinen J. White matter in autism spectrum disorders - evidence of impaired fiber formation. Acta Radiol. 2011 [PubMed]
  • Buxbaum JD, Betancur C, Bozdagi O, Dorr NP, Elder GA, Hof PR. Optimizing the phenotyping of rodent ASD models: enrichment analysis of mouse and human neurobiological phenotypes associated with high-risk autism genes identifies morphological, electrophysiological, neurological, and behavioral features. Mol Autism. 2012;3:1. [PMC free article] [PubMed]
  • Campbell DB, D'Oronzio R, Garbett K, Ebert PJ, Mirnics K, Levitt P, Persico AM. Disruption of cerebral cortex MET signaling in autism spectrum disorder. Ann Neurol. 2007;62:243–250. [PubMed]
  • Campbell DB, Li C, Sutcliffe JS, Persico AM, Levitt P. Genetic evidence implicating multiple genes in the MET receptor tyrosine kinase pathway in autism spectrum disorder. Autism Res. 2008;1:159–168. [PMC free article] [PubMed]
  • Campbell DB, Sutcliffe JS, Ebert PJ, Militerni R, Bravaccio C, Trillo S, Elia M, Schneider C, Melmed R, Sacco R, Persico AM, Levitt P. A genetic variant that disrupts MET transcription is associated with autism. Proc Natl Acad Sci U S A. 2006;103:16834–16839. [PubMed]
  • Campbell DB, Warren D, Sutcliffe JS, Lee EB, Levitt P. Association of MET with social and communication phenotypes in individuals with autism spectrum disorder. American Journal of Medical Genetics Part B: Neuropsychiatric Genetics. 2009;153B:438–446. [PubMed]
  • Casanova MF, Buxhoeveden DP, Brown C. Clinical and macroscopic correlates of minicolumnar pathology in autism. J Child Neurol. 2002;17:692–695. [PubMed]
  • Castelli F, Frith C, Happé F, Frith U. Autism, Asperger syndrome and brain mechanisms for the attribution of mental states to animated shapes. Brain. 2002;125:1839–1849. [PubMed]
  • Cheng Y, Chou KH, Chen IY, Fan YT, Decety J, Lin CP. Atypical development of white matter microstructure in adolescents with autism spectrum disorders. Neuroimage. 2010;50:873–882. [PubMed]
  • Cherkassky VL, Kana RK, Keller TA, Just MA. Functional connectivity in a baseline resting-state network in autism. Neuroreport. 2006;17:1687–1690. [PubMed]
  • Cheung C, Chua SE, Cheung V, Khong PL, Tai KS, Wong TK, Ho TP, McAlonan GM. White matter fractional anisotrophy differences and correlates of diagnostic symptoms in autism. J Child Psychol Psychiatry. 2009;50:1102–1112. [PubMed]
  • Chiang MC, McMahon KL, de Zubicaray GI, Martin NG, Hickie I, Toga AW, Wright MJ, Thompson PM. Genetics of white matter development: A DTI study of 705 twins and their siblings aged 12 to 29. Neuroimage. 2010;54:2308–2317. [PMC free article] [PubMed]
  • Courchesne E, Pierce K. Why the frontal cortex in autism might be talking only to itself: local over-connectivity but long-distance disconnection. Curr Opin Neurobiol. 2005;15:225–230. [PubMed]
  • Critchley HD, Daly EM, Bullmore ET, Williams SC, Van Amelsvoort T, Robertson DM, Rowe A, Phillips M, McAlonan G, Howlin P, Murphy DG. The functional neuroanatomy of social behaviour: changes in cerebral blood flow when people with autistic disorder process facial expressions. Brain. 2000;123(Pt 11):2203–2212. [PubMed]
  • Dalton KM, Nacewicz BM, Johnstone T, Schaefer HS, Gernsbacher MA, Goldsmith HH, Alexander AL, Davidson RJ. Gaze fixation and the neural circuitry of face processing in autism. Nat Neurosci. 2005;8:519–526. [PMC free article] [PubMed]
  • Marcus DS, Harwell J, Olsen T, Hodge M, Glasser MF, Prior F, Jenkinson M, Laumann T, Curtiss SW, Van Essen DC. Informatics and Data Mining Tools and Strategies for the Human Connectome Project. Front Neuroinformatics. 2011;5 [PMC free article] [PubMed]
  • Dapretto M, Davies MS, Pfeifer JH, Scott AA, Sigman M, Bookheimer SY, Iacoboni M. Understanding emotions in others: mirror neuron dysfunction in children with autism spectrum disorders. Nat Neurosci. 2006;9:28–30. [PMC free article] [PubMed]
  • Di Martino A, Ross K, Uddin LQ, Sklar AB, Castellanos FX, Milham MP. Functional brain correlates of social and nonsocial processes in autism spectrum disorders: an activation likelihood estimation meta-analysis. Biol Psychiatry. 2009;65:63–74. [PMC free article] [PubMed]
  • Dinstein I, Pierce K, Eyler L, Solso S, Malach R, Behrmann M, Courchesne E. Disrupted neural synchronization in toddlers with autism. Neuron. 2011;70:1218–1225. [PMC free article] [PubMed]
  • Dosenbach NU, Nardos B, Cohen AL, Fair DA, Power JD, Church JA, Nelson SM, Wig GS, Vogel AC, et al. Prediction of individual brain maturity using fMRI. Science. 2010;329:1358–1361. [PMC free article] [PubMed]
  • Fornito A, Zalesky A, Bassett DS, Meunier D, Ellison-Wright I, Yücel M, Wood SJ, Shaw K, O'Connor J, Nertney, et al. Genetic Influences on Cost-Efficient Organization of Human Cortical Functional Networks. J Neurosci. 2011;31:3261–3270. [PubMed]
  • Fox MD, Greicius M. Clinical applications of resting state functional connectivity. Front Syst Neurosci. 2010;4:19. [PMC free article] [PubMed]
  • Fox MD, Snyder AZ, Vincent JL, Corbetta M, Van Essen DC, Raichle ME. The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci U S A. 2005;102:9673–9678. [PubMed]
  • Geschwind DH. Genetics of autism spectrum disorders. Trends Cogn Sci. 2011;15:409–416. [PMC free article] [PubMed]
  • Glahn DC, Winkler AM, Kochunov P, Almasy L, Duggirala R, Carless MA, Curran JC, Olvera RL, Laird AR, Smith SM, et al. Genetic control over the resting brain. Proc Natl Acad Sci U S A. 2010;107:1223–1228. [PubMed]
  • Gottesman II, Gould TD. The endophenotype concept in psychiatry: etymology and strategic intentions. Am J Psychiatry. 2003;160:636–645. [PubMed]
  • Hadjikhani N, Joseph RM, Snyder J, Chabris CF, Clark J, Steele S, McGrath L, Vangel M, Aharon I, et al. Activation of the fusiform gyrus when individuals with autism spectrum disorder view faces. Neuroimage. 2004;22:1141–1150. [PubMed]
  • Hagmann P, Cammoun L, Gigandet X, Meuli R, Honey CJ, Wedeen VJ, Sporns O. Mapping the structural core of human cerebral cortex. PLoS Biol. 2008;6:e159. [PubMed]
  • Hallmayer J, Cleveland S, Torres A, Phillips J, Cohen B, Torigoe T, Miller J, Fedele A, Collins J, Smith K, et al. Genetic Heritability and Shared Environmental Factors Among Twin Pairs With Autism. Arch Gen Psychiatry. 2011;68:1095–1102. [PMC free article] [PubMed]
  • Heuer L, Braunschweig D, Ashwood P, Van de Water J, Campbell DB. Association of a MET genetic variant with autism-associated maternal autoantibodies to fetal brain proteins and cytokine expression. Translational Psychiatry. 2011;1:e48. [PMC free article] [PubMed]
  • Honey CJ, Sporns O, Cammoun L, Gigandet X, Thiran JP, Meuli R, Hagmann P. Predicting human resting-state functional connectivity from structural connectivity. Proc Natl Acad Sci U S A. 2009;106:2035–2040. [PubMed]
  • Jones CR, Happé F, Baird G, Simonoff E, Marsden AJ, Tregay J, Phillips RJ, Goswami U, Thomson JM, Charman T. Auditory discrimination and auditory sensory behaviours in autism spectrum disorders. Neuropsychologia. 2009;47:2850–2858. [PubMed]
  • Judson MC, Bergman MY, Campbell DB, Eagleson KL, Levitt P. Dynamic gene and protein expression patterns of the autism-associated met receptor tyrosine kinase in the developing mouse forebrain. J Comp Neurol. 2009;513:511–531. [PMC free article] [PubMed]
  • Judson MC, Eagleson KL, Wang L, Levitt P. Evidence of cell-nonautonomous changes in dendrite and dendritic spine morphology in the met-signaling-deficient mouse forebrain. J Comp Neurol. 2010;518:4463–4478. [PMC free article] [PubMed]
  • Judson MC, Eagleson KL, Levitt P. A new synaptic player leading to autism risk: Met receptor tyrosine kinase. J Neurodev Disord. 2011a;3:282–292. [PMC free article] [PubMed]
  • Judson MC, Amaral DG, Levitt P. Conserved Subcortical and Divergent Cortical Expression of Proteins Encoded by Orthologs of the Autism Risk Gene MET. Cereb Cortex. 2011b;21:1613–1626. [PMC free article] [PubMed]
  • Just MA, Cherkassky VL, Keller TA, Minshew NJ. Cortical activation and synchronization during sentence comprehension in high-functioning autism: evidence of underconnectivity. Brain. 2004;127:1811–1821. [PubMed]
  • Kaiser MD, Hudac CM, Shultz S, Lee SM, Cheung C, Berken AM, Deen B, Pitskel NB, Sugrue DR, Voos AC, et al. Neural signatures of autism. Proc Natl Acad Sci U S A. 2010;107:21223–21228. [PubMed]
  • Kennedy DP, Redcay E, Courchesne E. Failing to deactivate: resting functional abnormalities in autism. Proc Natl Acad Sci U S A. 2006;103:8275–8280. [PubMed]
  • Kleinhans NM, Richards T, Sterling L, Stegbauer KC, Mahurin R, Johnson LC, Greenson J, Dawson G, Aylward E. Abnormal functional connectivity in autism spectrum disorders during face processing. Brain. 2008;131:1000–1012. [PubMed]
  • Kochunov P, Glahn DC, Lancaster JL, Winkler AM, Smith S, Thompson PM, Almasy L, Duggirala R, Fox PT, Blangero J. Genetics of microstructure of cerebral white matter using diffusion tensor imaging. Neuroimage. 2010;53:1109–1116. [PMC free article] [PubMed]
  • Konopka G, Bomar JM, Winden K, Coppola G, Jonsson ZO, Gao F, Peng S, Preuss TM, Wohlschlegel JA, Geschwind DH. Human-specific transcriptional regulation of CNS development genes by FOXP2. Nature. 2009;462:213–217. [PMC free article] [PubMed]
  • Koten JW, Wood G, Hagoort P, Goebel R, Propping P, Willmes K, Boomsma DI. Genetic contribution to variation in cognitive function: an FMRI study in twins. Science. 2009;323:1737–1740. [PubMed]
  • Laurienti PJ, Burdette JH, Wallace MT, Yen YF, Field AS, Stein BE. Deactivation of sensory-specific cortex by cross-modal stimuli. J Cogn Neurosci. 2002;14:420–429. [PubMed]
  • Levitt P, Campbell DB. The genetic and neurobiologic compass points toward common signaling dysfunctions in autism spectrum disorders. J Clin Invest. 2009;119:747–754. [PMC free article] [PubMed]
  • Lin P, Hasson U, Jovicich J, Robinson S. A neuronal basis for task-negative responses in the human brain. Cereb Cortex. 2011;21:821–830. [PMC free article] [PubMed]
  • Lord C, Risi S, Lambrecht L, Cook EH, Leventhal BL, DiLavore PC, Pickles A, Rutter M. The autism diagnostic observation schedule-generic: a standard measure of social and communication deficits associated with the spectrum of autism. J Autism Dev Disord. 2000;30:205–223. [PubMed]
  • Lord C, Rutter M, Le Couteur A. Autism Diagnostic Interview-Revised: a revised version of a diagnostic interview for caregivers of individuals with possible pervasive developmental disorders. J Autism Dev Disord. 1994;24:659–685. [PubMed]
  • Iossifov I, Ronemus M, Levy D, Wang Z, Hakker I, Rosenbaum J, Yamrom B, Lee YH, Narzisi G, Leotta A, et al. De novo gene disruptions in children on the autistic spectrum. Neuron. 2012;74:285–299. [PMC free article] [PubMed]
  • Marshall CR, Noor A, Vincent JB, Lionel AC, Feuk L, Skaug J, Shago M, Moessner R, Pinto D, Ren, et al. Structural variation of chromosomes in autism spectrum disorder. Am J Hum Genet. 2008;82:477–488. [PubMed]
  • Monk CS, Peltier SJ, Wiggins JL, Weng SJ, Carrasco M, Risi S, Lord C. Abnormalities of intrinsic functional connectivity in autism spectrum disorders(,) Neuroimage. 2009;47:764–772. [PMC free article] [PubMed]
  • Monk CS, Weng SJ, Wiggins JL, Kurapati N, Louro HM, Carrasco M, Maslowsky J, Risi S, Lord C. Neural circuitry of emotional face processing in autism spectrum disorders. J Psychiatry Neurosci. 2010;35:105–114. [PMC free article] [PubMed]
  • Mozolic JL, Joyner D, Hugenschmidt CE, Peiffer AM, Kraft RA, Maldjian JA, Laurienti PJ. Cross-modal deactivations during modality-specific selective attention. BMC Neurol. 2008;8:35. [PMC free article] [PubMed]
  • Mukamel Z, Konopka G, Wexler E, Osborn GE, Dong H, Bergman MY, Levitt P, Geschwind DH. Regulation of MET by FOXP2, Genes Implicated in Higher Cognitive Dysfunction and Autism Risk. J Neurosci. 2011;31:11437–11442. [PMC free article] [PubMed]
  • Müller RA, Shih P, Keehn B, Deyoe JR, Leyden KM, Shukla DK. Underconnected, but How? A Survey of Functional Connectivity MRI Studies in Autism Spectrum Disorders. Cereb Cortex. 2011 [PMC free article] [PubMed]
  • Neale BM, Kou Y, Liu L, Ma'ayan A, Samocha KE, Sabo A, Lin CF, Stevens C, Wang LS, Makarov, et al. Patterns and rates of exonic de novo mutations in autism spectrum disorders. Nature. 2012 [PMC free article] [PubMed]
  • O'Roak BJ, Vives L, Girirajan S, Karakoc E, Krumm N, Coe BP, Levy R, Ko A, Lee C, Smith, et al. Sporadic autism exomes reveal a highly interconnected protein network of de novo mutations. Nature. 2012 [PMC free article] [PubMed]
  • Pfeifer JH, Iacoboni M, Mazziotta JC, Dapretto M. Mirroring others' emotions relates to empathy and interpersonal competence in children. Neuroimage. 2008;39:2076–2085. [PMC free article] [PubMed]
  • Pierce K, Haist F, Sedaghat F, Courchesne E. The brain response to personally familiar faces in autism: findings of fusiform activity and beyond. Brain. 2004;127:2703–2716. [PubMed]
  • Pinto D, Pagnamenta AT, Klei L, Anney R, Merico D, Regan R, Conroy J, Magalhaes TR, Correia C, et al. Functional impact of global rare copy number variation in autism spectrum disorders. Nature. 2010 [PMC free article] [PubMed]
  • Qiu S, Anderson CT, Levitt P, Shepherd GM. Circuit-specific intracortical hyperconnectivity in mice with deletion of the autism-associated met receptor tyrosine kinase. J Neurosci. 2011;31:5855–5864. [PMC free article] [PubMed]
  • Raichle ME, MacLeod AM, Snyder AZ, Powers WJ, Gusnard DA, Shulman GL. A default mode of brain function. Proc Natl Acad Sci U S A. 2001;98:676–682. [PubMed]
  • Rosenberg RE, Law JK, Yenokyan G, McGready J, Kaufmann WE, Law PA. Characteristics and concordance of autism spectrum disorders among 277 twin pairs. Arch Pediatr Adolesc Med. 2009;163:907–914. [PubMed]
  • Rudie JD, Shehzad Z, Hernandez LM, Colich NL, Bookheimer SY, Iacoboni M, Dapretto M. Reduced Functional Integration and Segregation of Distributed Neural Systems Underlying Social and Emotional Information Processing in Autism Spectrum Disorders. Cereb Cortex. 2011;22:1025–1037. [PMC free article] [PubMed]
  • Sanders SJ, Murtha MT, Gupta AR, Murdoch JD, Raubeson MJ, Willsey AJ, Ercan-Sencicek AG, Dilullo NM, Parikshak, et al. De novo mutations revealed by whole-exome sequencing are strongly associated with autism. Nature. 2012 [PMC free article] [PubMed]
  • Schipul SE, Keller TA, Just MA. Inter-regional brain communication and its disturbance in autism. Front Syst Neurosci. 2011;5:10. [PMC free article] [PubMed]
  • Schultz RT, Gauthier I, Klin A, Fulbright RK, Anderson AW, Volkmar F, Skudlarski P, Lacadie C, Cohen DJ, Gore JC. Abnormal ventral temporal cortical activity during face discrimination among individuals with autism and Asperger syndrome. Arch Gen Psychiatry. 2000;57:331–340. [PubMed]
  • Scott-Van Zeeland AA, Abrahams BS, Alvarez-Retuerto AI, Sonnenblick LI, Rudie JD, Ghahremani D, Mumford JA, Poldrack RA, Dapretto M, Geschwind DH, Bookheimer SY. Altered Functional Connectivity in Frontal Lobe Circuits Is Associated with Variation in the Autism Risk Gene CNTNAP2. Sci Transl Med. 2010;2 56ra80. [PMC free article] [PubMed]
  • Shukla DK, Keehn B, Smylie DM, Müller RA. Microstructural abnormalities of short-distance white matter fiber tracts in autism spectrum disorder. Neuropsychologia. 2011 [PMC free article] [PubMed]
  • Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TE. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. Neuroimage. 2006;31:1487–1505. [PubMed]
  • Spencer MD, Holt RJ, Chura LR, Suckling J, Calder AJ, Bullmore ET, Baron-Cohen S. A novel functional brain imaging endophenotype of autism: the neural response to facial expression of emotion. Translational Psychiatry. 2011;1:e19. [PMC free article] [PubMed]
  • State MW, Levitt P. The conundrums of understanding genetic risks for autism spectrum disorders. Nat Neurosci. 2011 [PMC free article] [PubMed]
  • Sundaram SK, Kumar A, Makki MI, Behen ME, Chugani HT, Chugani DC. Diffusion tensor imaging of frontal lobe in autism spectrum disorder. Cereb Cortex. 2008;18:2659–2665. [PubMed]
  • Thanseem I, Nakamura K, Miyachi T, Toyota T, Yamada S, Tsujii M, Tsuchiya KJ, Anitha A, Iwayama Y, Yamada K, et al. Further evidence for the role of MET in autism susceptibility. Neurosci Res. 2010;68:137–141. [PubMed]
  • Vernes SC, Newbury DF, Abrahams BS, Winchester L, Nicod J, Groszer M, Alarcón M, Oliver PL, Davies KE, Geschwind DH, et al. A functional genetic link between distinct developmental language disorders. N Engl J Med. 2008;359:2337–2345. [PMC free article] [PubMed]
  • Villalobos ME, Mizuno A, Dahl BC, Kemmotsu N, Müller RA. Reduced functional connectivity between V1 and inferior frontal cortex associated with visuomotor performance in autism. Neuroimage. 2005;25:916–925. [PMC free article] [PubMed]
  • Vissers ME, Cohen MX, Geurts HM. Brain connectivity and high functioning autism: a promising path of research that needs refined models, methodological convergence, and stronger behavioral links. Neurosci Biobehav Rev. 2012;36:604–625. [PubMed]
  • Voineagu I, Wang X, Johnston P, Lowe JK, Tian Y, Horvath S, Mill J, Cantor RM, Blencowe BJ, Geschwind DH. Transcriptomic analysis of autistic brain reveals convergent molecular pathology. Nature. 2011 [PMC free article] [PubMed]
  • Wang K, Zhang H, Ma D, Bucan M, Glessner JT, Abrahams BS, Salyakina D, Imielinski M, Bradfield JP, Sleiman PM, et al. Common genetic variants on 5p14.1 associate with autism spectrum disorders. Nature. 2009 [PMC free article] [PubMed]
  • Wechsler D. Wechsler Abbreviated Scale of Intelligence. San Antonio, TX: Psychological Corporation; 1999.
  • Wechsler D. Wechsler intelligence scale for children. third edition. San Antonio, TX: The Psychological Corporation; 1991.
  • Weiss LA, Arking DE, Daly MJ, Chakravarti A. Gene Discovery Project of Johns Hopkins & the Autism Consortium. A genome-wide linkage and association scan reveals novel loci for autism. Nature. 2009;461:802–808. [PMC free article] [PubMed]
  • Weng SJ, Wiggins JL, Peltier SJ, Carrasco M, Risi S, Lord C, Monk CS. Alterations of resting state functional connectivity in the default network in adolescents with autism spectrum disorders. Brain Res. 2010;1313:202–214. [PMC free article] [PubMed]
  • Xu L, Li J, Huang Y, Zhao M, Tang X, Wei L. AutismKB: an evidence-based knowledgebase of autism genetics. Nucleic Acids Res. 2012;40:D1016. [PMC free article] [PubMed]