Autism spectrum disorders (ASD) represent a formidable challenge for
psychiatry and neuroscience because of their high prevalence, life-long nature,
complexity and substantial heterogeneity. Facing these obstacles requires
large-scale multidisciplinary efforts. While the field of genetics has pioneered
data sharing for these reasons, neuroimaging had not kept pace. In response, we
introduce the Autism Brain Imaging Data Exchange (ABIDE) – a grassroots
consortium aggregating and openly sharing 1112 existing resting-state functional
magnetic resonance imaging (R-fMRI) datasets with corresponding structural MRI
and phenotypic information from 539 individuals with ASD and 573 age-matched
typical controls (TC; 7–64 years) (http://fcon_1000.projects.nitrc.org/indi/abide/). Here, we
present this resource and demonstrate its suitability for advancing knowledge of
ASD neurobiology based on analyses of 360 males with ASD and 403 male
age-matched TC. We focused on whole-brain intrinsic functional connectivity and
also survey a range of voxel-wise measures of intrinsic functional brain
architecture. Whole-brain analyses reconciled seemingly disparate themes of both
hypo and hyperconnectivity in the ASD literature; both were detected, though
hypoconnectivity dominated, particularly for cortico-cortical and
interhemispheric functional connectivity. Exploratory analyses using an array of
regional metrics of intrinsic brain function converged on common loci of
dysfunction in ASD (mid and posterior insula, posterior cingulate cortex), and
highlighted less commonly explored regions such as thalamus. The survey of the
ABIDE R-fMRI datasets provides unprecedented demonstrations of both replication
and novel discovery. By pooling multiple international datasets, ABIDE is
expected to accelerate the pace of discovery setting the stage for the next
generation of ASD studies.
Resting state fMRI; Intrinsic functional connectivity; Data sharing; Large-scale networks; Default network; Interhemispheric connectivity; Thalamus
Sensory over-responsivity (SOR), defined as a negative response to or avoidance of sensory stimuli, is both highly prevalent and extremely impairing in youth with autism spectrum disorders (ASD), yet little is known about the neurological bases of SOR. This study aimed to examine the functional neural correlates of SOR by comparing brain responses to sensory stimuli in youth with and without ASD.
Twenty-five high-functioning youth with ASD and 25 age- and IQ-equivalent typically developing (TD) youth were presented with mildly aversive auditory and visual stimuli during a functional magnetic resonance imaging (fMRI) scan. Parents provided ratings of children's SOR and anxiety symptom severity.
Compared to TD participants, ASD participants displayed greater activation in primary sensory cortical areas as well as amygdala, hippocampus, and orbital-frontal cortex. In both groups, the level of activity in these areas was positively correlated with level of SOR severity as rated by parents, over and above behavioral ratings of anxiety.
This study demonstrates that youth with ASD show neural hyper-responsivity to sensory stimuli, and that behavioral symptoms of SOR may be related to both heightened responsivity in primary sensory regions as well as areas related to emotion processing, and regulation.
amygdala; anxiety; autism spectrum disorders; functional magnetic resonance; imaging (fMRI); sensory over-responsivity
Recently, carriers of a common variant in the autism risk gene, CNTNAP2, were found to have altered functional brain connectivity using functional MRI. Here we scanned 328 young adults with high-field (4-Tesla) diffusion imaging, to test the hypothesis that carriers of this gene variant would have altered structural brain connectivity. All participants (209 females, 119 males, age: 23.4 +/−2.17 SD years) were scanned with 105-gradient high angular diffusion imaging (HARDI) at 4 Tesla. After performing a whole-brain fiber tractography using the full angular resolution of the diffusion scans, 70 cortical surface-based regions of interest were created from each individual’s co-registered anatomical data to compute graph metrics for all pairs of cortical regions. In graph theory analyses, subjects homozygous for the risk allele (CC) had lower characteristic path length, greater small-worldness and global efficiency in whole brain analyses, as well as greater eccentricity (maximum path length) in 60 of 70 nodes in regional analyses. These results were not reducible to differences in more commonly studied traits such as fiber density or fractional anisotropy. This is the first study to link graph theory metrics of brain structural connectivity to a common genetic variant linked with autism and will help us understand the neurobiology of circuits implicated in risk for autism.
structural connectivity; HARDI; autism; CNTNAP2; graph theory; twins
Individuals with ASD show consistent impairment in processing pragmatic language when attention to multiple social cues (e.g., facial expression, tone of voice) is often needed to navigate social interactions. Building upon prior fMRI work examining how facial affect and prosodic cues are used to infer a speaker's communicative intent, the authors examined whether children and adolescents with ASD differ from typically developing (TD) controls in their processing of sincere versus ironic remarks. At the behavioral level, children and adolescents with ASD and matched TD controls were able to determine whether a speaker's remark was sincere or ironic equally well, with both groups showing longer response times for ironic remarks. At the neural level, for both sincere and ironic scenarios, an extended cortical network—including canonical language areas in the left hemisphere and their right hemisphere counterparts—was activated in both groups, albeit to a lesser degree in the ASD sample. Despite overall similar patterns of activity observed for the two conditions in both groups, significant modulation of activity was detected when directly comparing sincere and ironic scenarios within and between groups. While both TD and ASD groups showed significantly greater activity in several nodes of this extended network when processing ironic versus sincere remarks, increased activity was largely confined to left language areas in TD controls, whereas the ASD sample showed a more bilateral activation profile which included both language and “theory of mind” areas (i.e., ventromedial prefrontal cortex). These findings suggest that, for high-functioning individuals with ASD, increased activity in right hemisphere homologues of language areas in the left hemisphere, as well as regions involved in social cognition, may reflect compensatory mechanisms supporting normative behavioral task performance.
Although the amygdala and ventrolateral prefrontal cortex have been implicated in the pathophysiology of bipolar I disorder, the neural mechanisms underlying bipolar II disorder remain unknown. The authors examined neural activity in response to negative emotional faces during an emotion perception task that reliably activates emotion regulatory regions.
Twenty-one nonmedicated depressed bipolar II patients and 21 healthy comparison subjects underwent functional MRI (fMRI) while performing an emotional face-matching task. Within- and between-group whole-brain fMRI activation and seed-based connectivity analyses were conducted.
In depressed bipolar II patients, random-effects between-group fMRI analyses revealed a significant reduction in activation in several regions, including the left and right ventrolateral prefrontal cortices (Brodmann's area [BA] 47) and the right amygdala, a priori regions of interest. Additionally, bipolar patients exhibited significantly reduced negative functional connectivity between the right amygdala and the right orbitofrontal cortex (BA 10) as well as the right dorsolateral prefrontal cortex (BA 46) relative to healthy comparison subjects.
These findings suggest that bipolar II depression is characterized by reduced regional orbitofrontal and limbic activation and altered connectivity in a fron-to-temporal circuit implicated in working memory and emotional learning. While the amygdala hypoactivation observed in bipolar II depression is opposite to the direction seen in bipolar I mania and may therefore be state dependent, the observed orbitofrontal cortex hypoactivation is consistent with findings in bipolar I depression, mania, and euthymia, suggesting a physiologic trait marker of the disorder.
A growing body of evidence suggests that autism spectrum disorders (ASDs) are related to altered communication between brain regions. Here, we present findings showing that ASD is characterized by a pattern of reduced functional integration as well as reduced segregation of large-scale brain networks. Twenty-three children with ASD and 25 typically developing matched controls underwent functional magnetic resonance imaging while passively viewing emotional face expressions. We examined whole-brain functional connectivity of two brain structures previously implicated in emotional face processing in autism: the amygdala bilaterally and the right pars opercularis of the inferior frontal gyrus (rIFGpo). In the ASD group, we observed reduced functional integration (i.e., less long-range connectivity) between amygdala and secondary visual areas, as well as reduced segregation between amygdala and dorsolateral prefrontal cortex. For the rIFGpo seed, we observed reduced functional integration with parietal cortex and increased integration with right frontal cortex as well as right nucleus accumbens. Finally, we observed reduced segregation between rIFGpo and the ventromedial prefrontal cortex. We propose that a systems-level approach—whereby the integration and segregation of large-scale brain networks in ASD is examined in relation to typical development—may provide a more detailed characterization of the neural basis of ASD.
amygdala; connectivity; default mode network; face processing; mirror neuron system
Recently, carriers of a common variant in the autism risk gene, CNTNAP2, were found to have altered functional brain connectivity using functional MRI. Here, we scanned 328 young adults with high-field (4-Tesla) diffusion imaging, to test the hypothesis that carriers of this gene variant would have altered structural brain connectivity. All participants (209 women, 119 men, age: 23.4±2.17 SD years) were scanned with 105-gradient high-angular-resolution diffusion imaging (HARDI) at 4 Tesla. After performing a whole-brain fiber tractography using the full angular resolution of the diffusion scans, 70 cortical surface-based regions of interest were created from each individual's co-registered anatomical data to compute graph metrics for all pairs of cortical regions. In graph theory analyses, subjects homozygous for the risk allele (CC) had lower characteristic path length, greater small-worldness and global efficiency in whole-brain analyses, and lower eccentricity (maximum path length) in 60 of the 70 nodes in regional analyses. These results were not reducible to differences in more commonly studied traits such as fiber density or fractional anisotropy. This is the first study that links graph theory metrics of brain structural connectivity to a common genetic variant linked with autism and will help us understand the neurobiology of the circuits implicated in the risk for autism.
autism; CNTNAP2; graph theory; HARDI; structural connectivity; twins
Peer rejection is particularly pervasive among adolescents with autism spectrum disorders (ASD). However, how adolescents with ASD differ from typically developing adolescents in their responses to peer rejection is poorly understood. The goal of the current investigation was to examine neural responses to peer exclusion among adolescents with ASD compared to typically developing adolescents. Nineteen adolescents with ASD and 17 typically developing controls underwent fMRI as they were ostensibly excluded by peers during an online game called Cyberball. Afterwards, participants reported their distress about the exclusion. Compared to typically developing adolescents, those with ASD displayed less activity in regions previously linked with the distressing aspect of peer exclusion, including the subgenual anterior cingulate and anterior insula, as well as less activity in regions previously linked with the regulation of distress responses during peer exclusion, including the ventrolateral prefrontal cortex and ventral striatum. Interestingly, however, both groups self-reported equivalent levels of distress. This suggests that adolescents with ASD may engage in differential processing of social experiences at the neural level, but be equally aware of, and concerned about, peer rejection. Overall, these findings contribute new insights about how this population may differentially experience negative social events in their daily lives.
Autism spectrum disorders; Peer rejection; Social exclusion; Adolescence; Functional magnetic resonance imaging
Attention deficit hyperactivity disorder (ADHD) currently is diagnosed in children by clinicians via subjective ADHD-specific behavioral instruments and by reports from the parents and teachers. Considering its high prevalence and large economic and societal costs, a quantitative tool that aids in diagnosis by characterizing underlying neurobiology would be extremely valuable. This provided motivation for the ADHD-200 machine learning (ML) competition, a multisite collaborative effort to investigate imaging classifiers for ADHD. Here we present our ML approach, which used structural and functional magnetic resonance imaging data, combined with demographic information, to predict diagnostic status of individuals with ADHD from typically developing (TD) children across eight different research sites. Structural features included quantitative metrics from 113 cortical and non-cortical regions. Functional features included Pearson correlation functional connectivity matrices, nodal and global graph theoretical measures, nodal power spectra, voxelwise global connectivity, and voxelwise regional homogeneity. We performed feature ranking for each site and modality using the multiple support vector machine recursive feature elimination (SVM-RFE) algorithm, and feature subset selection by optimizing the expected generalization performance of a radial basis function kernel SVM (RBF-SVM) trained across a range of the top features. Site-specific RBF-SVMs using these optimal feature sets from each imaging modality were used to predict the class labels of an independent hold-out test set. A voting approach was used to combine these multiple predictions and assign final class labels. With this methodology we were able to predict diagnosis of ADHD with 55% accuracy (versus a 39% chance level in this sample), 33% sensitivity, and 80% specificity. This approach also allowed us to evaluate predictive structural and functional features giving insight into abnormal brain circuitry in ADHD.
attention deficit hyperactivity disorder; ADHD-200; machine learning; classification; feature selection; fMRI; graph theory
Brain connectomics research has rapidly expanded using functional MRI (fMRI) and diffusion-weighted MRI (dwMRI). A common product of these varied analyses is a connectivity matrix (CM). A CM stores the connection strength between any two regions (“nodes”) in a brain network. This format is useful for several reasons: (1) it is highly distilled, with minimal data size and complexity, (2) graph theory can be applied to characterize the network's topology, and (3) it retains sufficient information to capture individual differences such as age, gender, intelligence quotient (IQ), or disease state. Here we introduce the UCLA Multimodal Connectivity Database (http://umcd.humanconnectomeproject.org), an openly available website for brain network analysis and data sharing. The site is a repository for researchers to publicly share CMs derived from their data. The site also allows users to select any CM shared by another user, compute graph theoretical metrics on the site, visualize a report of results, or download the raw CM. To date, users have contributed over 2000 individual CMs, spanning different imaging modalities (fMRI, dwMRI) and disorders (Alzheimer's, autism, Attention Deficit Hyperactive Disorder). To demonstrate the site's functionality, whole brain functional and structural connectivity matrices are derived from 60 subjects' (ages 26–45) resting state fMRI (rs-fMRI) and dwMRI data and uploaded to the site. The site is utilized to derive graph theory global and regional measures for the rs-fMRI and dwMRI networks. Global and nodal graph theoretical measures between functional and structural networks exhibit low correspondence. This example demonstrates how this tool can enhance the comparability of brain networks from different imaging modalities and studies. The existence of this connectivity-based repository should foster broader data sharing and enable larger-scale meta-analyses comparing networks across imaging modality, age group, and disease state.
graph theory; data sharing; functional connectivity; structural connectivity; resting-state fMRI; diffusion-weighted MRI
Genetic studies are rapidly identifying variants that shape risk for disorders of human cognition, but the question of how such variants predispose to neuropsychiatric disease remains. Noninvasive human brain imaging allows assessment of the brain in vivo, and the combination of genetics and imaging phenotypes remains one of the only ways to explore functional genotype-phenotype associations in human brain. Common variants in contactin-associated protein-like 2 (CNTNAP2), a neurexin superfamily member, have been associated with several allied neurodevelopmental disorders, including autism and specific language impairment, and CNTNAP2 is highly expressed in frontal lobe circuits in the developing human brain. Using functional neuroimaging, we have demonstrated a relationship between frontal lobar connectivity and common genetic variants in CNTNAP2. These data provide a mechanistic link between specific genetic risk for neurodevelopmental disorders and empirical data implicating dysfunction of long-range connections within the frontal lobe in autism. The convergence between genetic findings and cognitive-behavioral models of autism provides evidence that genetic variation at CNTNAP2 predisposes to diseases such asautism in part through modulation of frontal lobe connectivity.