The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data

By Paul M. Thompson, Jason L. Stein, S. Medland, D. Hibar, A. Vasquez, M. Rentería, Roberto Toro, Neda Jahanshad, G. Schumann, B. Franke and 279 more. Brain Imaging and Behavior, 2014

790 citations recorded by the source corpus.

The Enhancing NeuroImaging Genetics through Meta-Analysis (ENIGMA) Consortium is a collaborative network of researchers working together on a range of large-scale studies that integrate data from 70 institutions worldwide. Organized into Working Groups that tackle questions in neuroscience, genetics, and medicine, ENIGMA studies have analyzed neuroimaging data from over 12,826 subjects. In addition, data from 12,171 individuals were provided by the CHARGE consortium for replication of findings, in a total of 24,997 subjects. By meta-analyzing results from many sites, ENIGMA has detected factors that affect the brain that no individual site could detect on its own, and that require larger numbers of subjects than any individual neuroimaging study has currently collected. ENIGMA’s first project was a genome-wide association study identifying common variants in the genome associated with hippocampal volume or intracranial volume. Continuing work is exploring genetic associations with subcortical volumes (ENIGMA2) and white matter microstructure (ENIGMA-DTI). Working groups also focus on understanding how schizophrenia, bipolar illness, major depression and attention deficit/hyperactivity disorder (ADHD) affect the brain. We review the current progress of the ENIGMA Consortium, along with challenges and unexpected discoveries made on the way.

Subfields: Cognitive Neuroscience, Genetics, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/0193ad55614bfa9016bee5b93640bd9f5a0b96bb

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Citations in the corpus

Distributed Weight Consolidation: A Brain Segmentation Case Study (2018, methodology): “In the neuroimaging literature, several platforms have been introduced for combining models trained on different datasets, such as ENIGMA ([26], for meta-analyses) and COINSTAC ([21], for distributed training of models).”
Federated learning enables big data for rare cancer boundary detection (2022, methodology): “These speciﬁc 6 out-of-sample sites (Site IDs: 8, 11, 19, 20, 21, 43) were allocated based on their availability, i.e., they have indicated an expected delayed participation rendering them optimal for model generalizability validation.”
The relation between statistical power and inference in fMRI (2017, methodology): “Other large-scale collaborative efforts include mega-analy-ses (such as the ENIGMA consortium [97]) and very promising are the various initiatives for open-data sharing [53] such as HCP used in the current paper and the Functional Connec-tomes Project (FCP/International Neuroimaging Data sharing…”
Cross‐validation failure: Small sample sizes lead to large error bars (2017, background): “In functional imaging, resting-state fMRI acquisition are a promising source of very large data, via post-hoc aggregation (Biswal et al., 2010; Thompson et al., 2014; Di Martino et al., 2014) or large concerted efforts (Miller et al.”
A practical guide to linking brain-wide gene expression and neuroimaging data (2018, background): “The latter has been facilitated by the formation of large consortia, such as ENIGMA (Thompson et al., 2014).”
Recruiting the ABCD sample: Design considerations and procedures (2018, positive): “, 2016) or data pooling efforts such as ENIGMA (Thompson et al., 2014) comes the possibility to apply epidemiological principles to neuroscientific investigations.”
Towards human-guided machine learning (2019, methodology): “Analysis. learning analysis produced by the ENIGMA neuroscience consortium [32].”
Federated Learning in Distributed Medical Databases: Meta-Analysis of Large-Scale Subcortical Brain Data (2018, background): “With a large number of institutions world-wide [3], ENIGMA has become one of the largest networks bringing together multiple groups analyzing neuroimaging data from over 10,000 subjects.”
Cortical abnormalities in bipolar disorder: an MRI analysis of 6503 individuals from the ENIGMA Bipolar Disorder Working Group (2017, positive): “14 ,33 left superior We formed the Bipolar Disorder Working Group within the ENIGMA Consortium 38,39 with the overarching goal of identifying consistent brain alterations associated with BD and elucidating and controlling for moderating factors that may affect the pathophysiology of BD.”