The Autism Brain Imaging Data Exchange: Towards Large-Scale Evaluation of the Intrinsic Brain Architecture in Autism

Phyllis Green and Randolph Cowen Institute for Pediatric Neuroscience at the NYU Child Study Center, New York University Langone Medical Center, New York, NY, USA.
Molecular Psychiatry (Impact Factor: 14.5). 06/2013; 19(6). DOI: 10.1038/mp.2013.78
Source: PubMed


Autism spectrum disorders (ASDs) represent a formidable challenge for psychiatry and neuroscience because of their high prevalence, lifelong nature, complexity and substantial heterogeneity. Facing these obstacles requires large-scale multidisciplinary efforts. Although 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) data sets with corresponding structural MRI and phenotypic information from 539 individuals with ASDs and 573 age-matched typical controls (TCs; 7-64 years) ( Here, we present this resource and demonstrate its suitability for advancing knowledge of ASD neurobiology based on analyses of 360 male subjects with ASDs and 403 male age-matched TCs. 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, although hypoconnectivity dominated, particularly for corticocortical and interhemispheric functional connectivity. Exploratory analyses using an array of regional metrics of intrinsic brain function converged on common loci of dysfunction in ASDs (mid- and posterior insula and posterior cingulate cortex), and highlighted less commonly explored regions such as the thalamus. The survey of the ABIDE R-fMRI data sets provides unprecedented demonstrations of both replication and novel discovery. By pooling multiple international data sets, ABIDE is expected to accelerate the pace of discovery setting the stage for the next generation of ASD studies.Molecular Psychiatry advance online publication, 18 June 2013; doi:10.1038/mp.2013.78.

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    • "In a machine learning approach, Nielson and colleagues use a large fMRI autism database, the Autism Brain Imaging Data Exchange (ABIDE) (Di Martino et al., 2013), to classify participants with ASD from TD participants based on functional connectivity features. This study uses resting state functional connectivity data obtained from 964 participants across 16 international sites. "

    Frontiers in Human Neuroscience 06/2014; 8:349. DOI:10.3389/fnhum.2014.00349 · 3.63 Impact Factor
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    • "Data sharing is becoming increasingly common (Biswal et al., 2010; Di Martino et al., 2013), but despite encouragement and facilitation by funding agencies, journals, and some labs and larger research efforts 1 (Hall et al., 2012; Prior et al., 2013), there remain political, financial, social, and technical barriers to sharing data (Poline et al., 2012). Excuses such as " it's too hard " and " it takes too long " are all too common, and there is anxiety about subject protection and control of data (De Schutter, 2010). "
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