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.

The input conductance of cells in the cat primary visual cortex (V1) has been shown recently to grow substantially during visual stimulation. Because increasing conductance can have a divisive effect on the synaptic input, theoretical proposals have ascribed to it specific functions. According to the veto model, conductance increases would serve to sharpen orientation tuning by increasing most at off-optimal orientations. According to the normalization model, conductance increases would control the cell's gain, by being independent of stimulus orientation and by growing with stimulus contrast. We set out to test these proposals and to determine the visual properties and possible synaptic origin of the conductance increases. We recorded the membrane potential of cat V1 cells while injecting steady currents and presenting drifting grating patterns of varying contrast and orientation. Input conductance grew with stimulus contrast by 20-300%, generally more in simple cells (40-300%) than in complex cells (20-120%), and in simple cells was strongly modulated in time. Conductance was invariably maximal for stimuli of the preferred orientation. Thus conductance changes contribute to a gain control mechanism, but the strength of this gain control does not depend uniquely on contrast. By assuming that the conductance changes are entirely synaptic, we further derived the excitatory and inhibitory synaptic conductances underlying the visual responses. In simple cells, these conductances were often arranged in push-pull: excitation increased when inhibition decreased and vice versa. Excitation and inhibition had similar preferred orientations and did not appear to differ in tuning width, suggesting that the intracortical synaptic inputs to simple cells of cat V1 originate from cells with similar orientation tuning. This finding is at odds with models where orientation tuning in simple cells is achieved by inhibition at off-optimal orientations or sharpened by inhibition that is more broadly tuned than excitation.

The second iteration of the Autism Brain Imaging Data Exchange (ABIDE II) aims to enhance the scope of brain connectomics research in Autism Spectrum Disorder (ASD). Consistent with the initial ABIDE effort (ABIDE I), that released 1112 datasets in 2012, this new multisite open-data resource is an aggregate of resting state functional magnetic resonance imaging (MRI) and corresponding structural MRI and phenotypic datasets. ABIDE II includes datasets from an additional 487 individuals with ASD and 557 controls previously collected across 16 international institutions. The combination of ABIDE I and ABIDE II provides investigators with 2156 unique cross-sectional datasets allowing selection of samples for discovery and/or replication. This sample size can also facilitate the identification of neurobiological subgroups, as well as preliminary examinations of sex differences in ASD. Additionally, ABIDE II includes a range of psychiatric variables to inform our understanding of the neural correlates of co-occurring psychopathology; 284 diffusion imaging datasets are also included. It is anticipated that these enhancements will contribute to unraveling key sources of ASD heterogeneity.

Feedforward models of visual cortex appear to be inconsistent with a well-known property of cortical cells: contrast invariance of orientation tuning. The models' fixed threshold broadens orientation tuning as contrast increases, whereas in real cells tuning width is invariant with contrast. We have compared the orientation tuning of spike and membrane potential responses in single cells. Both are contrast invariant, yet a threshold-linear relation applied to the membrane potential accurately predicts the orientation tuning of spike responses. The key to this apparent paradox lies in the noisiness of the membrane potential. Responses that are subthreshold on average are still capable of generating spikes on individual trials. Unlike the iceberg effect, contrast invariance remains intact even as threshold narrows orientation selectivity. Noise may, by extension, smooth the average relation between membrane potential and spike rate throughout the brain.

The cortical underconnectivity theory asserts that reduced long-range functional connectivity might contribute to a neural mechanism for autism. We examined resting-state blood oxygen level-dependent interhemispheric correlation in 53 males with high-functioning autism and 39 typically developing males from late childhood through early adulthood. By constructing spatial maps of correlation between homologous voxels in each hemisphere, we found significantly reduced interhemispheric correlation specific to regions with functional relevance to autism: sensorimotor cortex, anterior insula, fusiform gyrus, superior temporal gyrus, and superior parietal lobule. Observed interhemispheric connectivity differences were better explained by diagnosis of autism than by potentially confounding neuropsychological metrics of language, IQ, or handedness. Although both corpus callosal volume and gray matter interhemispheric connectivity were significantly reduced in autism, no direct relationship was observed between them, suggesting that structural and functional metrics measure different aspects of interhemispheric connectivity. In the control but not the autism sample, there was decreasing interhemispheric correlation with subject age. Greater differences in interhemispheric correlation were seen for more lateral regions in the brain. These findings suggest that long-range connectivity abnormalities in autism are spatially heterogeneous and that transcallosal connectivity is decreased most in regions with functions associated with behavioral abnormalities in autism. Autism subjects continue to show developmental differences in interhemispheric connectivity into early adulthood.

The natural history of brain growth in autism spectrum disorders remains unclear. Cross-sectional studies have identified regional abnormalities in brain volume and cortical thickness in autism, although substantial discrepancies have been reported. Preliminary longitudinal studies using two time points and small samples have identified specific regional differences in cortical thickness in the disorder. To clarify age-related trajectories of cortical development, we examined longitudinal changes in cortical thickness within a large mixed cross-sectional and longitudinal sample of autistic subjects and age- and gender-matched typically developing controls. Three hundred and forty-five magnetic resonance imaging scans were examined from 97 males with autism (mean age = 16.8 years; range 3-36 years) and 60 males with typical development (mean age = 18 years; range 4-39 years), with an average interscan interval of 2.6 years. FreeSurfer image analysis software was used to parcellate the cortex into 34 regions of interest per hemisphere and to calculate mean cortical thickness for each region. Longitudinal linear mixed effects models were used to further characterize these findings and identify regions with between-group differences in longitudinal age-related trajectories. Using mean age at time of first scan as a reference (15 years), differences were observed in bilateral inferior frontal gyrus, pars opercularis and pars triangularis, right caudal middle frontal and left rostral middle frontal regions, and left frontal pole. However, group differences in cortical thickness varied by developmental stage, and were influenced by IQ. Differences in age-related trajectories emerged in bilateral parietal and occipital regions (postcentral gyrus, cuneus, lingual gyrus, pericalcarine cortex), left frontal regions (pars opercularis, rostral middle frontal and frontal pole), left supramarginal gyrus, and right transverse temporal gyrus, superior parietal lobule, and paracentral, lateral orbitofrontal, and lateral occipital regions. We suggest that abnormal cortical development in autism spectrum disorders undergoes three distinct phases: accelerated expansion in early childhood, accelerated thinning in later childhood and adolescence, and decelerated thinning in early adulthood. Moreover, cortical thickness abnormalities in autism spectrum disorders are region-specific, vary with age, and may remain dynamic well into adulthood.

Group differences in resting state functional magnetic resonance imaging connectivity between individuals with autism and typically developing controls have been widely replicated for a small number of discrete brain regions, yet the whole-brain distribution of connectivity abnormalities in autism is not well characterized. It is also unclear whether functional connectivity is sufficiently robust to be used as a diagnostic or prognostic metric in individual patients with autism. We obtained pairwise functional connectivity measurements from a lattice of 7266 regions of interest covering the entire grey matter (26.4 million connections) in a well-characterized set of 40 male adolescents and young adults with autism and 40 age-, sex- and IQ-matched typically developing subjects. A single resting state blood oxygen level-dependent scan of 8 min was used for the classification in each subject. A leave-one-out classifier successfully distinguished autism from control subjects with 83% sensitivity and 75% specificity for a total accuracy of 79% (P = 1.1 × 10(-7)). In subjects <20 years of age, the classifier performed at 89% accuracy (P = 5.4 × 10(-7)). In a replication dataset consisting of 21 individuals from six families with both affected and unaffected siblings, the classifier performed at 71% accuracy (91% accuracy for subjects <20 years of age). Classification scores in subjects with autism were significantly correlated with the Social Responsiveness Scale (P = 0.05), verbal IQ (P = 0.02) and the Autism Diagnostic Observation Schedule-Generic's combined social and communication subscores (P = 0.05). An analysis of informative connections demonstrated that region of interest pairs with strongest correlation values were most abnormal in autism. Negatively correlated region of interest pairs showed higher correlation in autism (less anticorrelation), possibly representing weaker inhibitory connections, particularly for long connections (Euclidean distance >10 cm). Brain regions showing greatest differences included regions of the default mode network, superior parietal lobule, fusiform gyrus and anterior insula. Overall, classification accuracy was better for younger subjects, with differences between autism and control subjects diminishing after 19 years of age. Classification scores of unaffected siblings of individuals with autism were more similar to those of the control subjects than to those of the subjects with autism. These findings indicate feasibility of a functional connectivity magnetic resonance imaging diagnostic assay for autism.

Efforts to identify meaningful functional imaging-based biomarkers are limited by the ability to reliably characterize inter-individual differences in human brain function. Although a growing number of connectomics-based measures are reported to have moderate to high test-retest reliability, the variability in data acquisition, experimental designs, and analytic methods precludes the ability to generalize results. The Consortium for Reliability and Reproducibility (CoRR) is working to address this challenge and establish test-retest reliability as a minimum standard for methods development in functional connectomics. Specifically, CoRR has aggregated 1,629 typical individuals’ resting state fMRI (rfMRI) data (5,093 rfMRI scans) from 18 international sites, and is openly sharing them via the International Data-sharing Neuroimaging Initiative (INDI). To allow researchers to generate various estimates of reliability and reproducibility, a variety of data acquisition procedures and experimental designs are included. Similarly, to enable users to assess the impact of commonly encountered artifacts (for example, motion) on characterizations of inter-individual variation, datasets of varying quality are included.