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

By A. di Martino, Chao-Gan Yan, Qingyang Li, Erin Denio, F. Castellanos, K. Alaerts, Jeffrey S. Anderson, Michal Assaf, Susan Y. Bookheimer, Mirella Dapretto and 32 more. Molecular Psychiatry, 2013

2,689 citations recorded by the source corpus.

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) (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 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.

Subfields: Cognitive Neuroscience, Pediatrics, Perinatology and Child Health

View Paper: https://www.semanticscholar.org/paper/9012a33a1ac8b6388b562e7558b3d808df485ceb

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In Pediatrics, Perinatology and Child Health

Citations in the corpus

Explanations based on the Missing: Towards Contrastive Explanations with Pertinent Negatives (2018, neutral): “The third one is a brain functional MRI (fMRI) imaging dataset from the publicly accessible Autism Brain Imaging Data Exchange (ABIDE) I database [10], which comprises of resting-state fMRI acquisitions of subjects diagnosed with autism spectrum disorder (ASD) and neurotypical individuals.”
An Unsupervised Learning Model for Deformable Medical Image Registration (2018, positive): “We use a large-scale, multi-site, multi-study dataset of 7829 T1weighted brain MRI scans from eight publicly available datasets: ADNI [33], OASIS [29], ABIDE [31], ADHD200 [32], MCIC [19], PPMI [30], HABS [12], and Harvard GSP [20].”
Distributed Weight Consolidation: A Brain Segmentation Case Study (2018, neutral): “In order to provide an independent estimate of how well a given network generalizes to any new site, we also test networks on a completely held-out dataset consisting of 2,529 sMRI volumes collected across several institutions by the ABIDE project [4].”
Disease prediction using graph convolutional networks: Application to Autism Spectrum Disorder and Alzheimer's disease (2018, neutral): “Semi-supervisedClassification The ABIDE database (Di Martino et al., 2014) aggregates data from diﬀerent international acquisition sites and openly shares neuroimaging (functional MRI) and phenotypic data of 1112 subjects 3 .”
A Novel Transfer Learning Approach to Enhance Deep Neural Network Classification of Brain Functional Connectomes (2018, neutral): “At present, most deep learning approaches in previous studies on FC features were developed using only modest sample sizes (Di Martino et al., 2014; Kuang et al., 2014; Kim et al., 2016; Guo et al., 2017; Heinsfeld et al., 2018).”
DPABI: Data Processing & Analysis for (Resting-State) Brain Imaging (2016, positive): “…al., 2012) because of its substantial reliability (Shehzad et al., 2009; Zuo and Xing, 2014), sensitivity (Castellanos et al., 2013; Fox and Greicius, 2010; Greicius, 2008) and comparability across labs (ADHD-200-Consortium, 2012; Biswal et al., 2010; Di Martino et al., 2014; Mennes et al., 2013).”
Brain Biomarker Interpretation in ASD Using Deep Learning and fMRI (2018, neutral): “We also performed experiments on data from the ABIDE I cohort UM site [13,9].”
Recursive Cascaded Networks for Unsupervised Medical Image Registration (2019, neutral): “• ABIDE [21], 1287 scans.”
ICA-based artifact removal diminishes scan site differences in multi-center resting-state fMRI (2015, positive): “…Initiative (GenFI), Rohrer et al., 2013; 1000 Functional Connectome Project, Biswal et al., 2010; ADHD 200 Consortium dataset (The ADHD-200 Consortium, 2012); and Autism Brain Imaging Data Exchange (ABIDE), Di Martino et al., 2014], methods for reducing scan site differences must be developed.”
Deriving reproducible biomarkers from multi‐site resting‐state data: An Autism‐based example (2016, positive): “Decreases in homotopic functional connectivity have been previously reported in R-fMRI studies with moderately [64, 72] or large samples such as ABIDE [25].”