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Hum Brain Mapp. 2018 Aug;39(8):3308-3325. doi: 10.1002/hbm.24078. Epub 2018 May 2.

BrainMap VBM: An environment for structural meta-analysis.

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Research Imaging Institute, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
Department of Radiology, University of Texas Health Science Center at San Antonio, San Antonio, Texas.
Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut.
Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany.
Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
South Texas Veterans Health Care System, San Antonio, Texas.
Shenzhen Institute of Neuroscience, Shenzhen University, Shenzhen China, People's Republic of China.


The BrainMap database is a community resource that curates peer-reviewed, coordinate-based human neuroimaging literature. By pairing the results of neuroimaging studies with their relevant meta-data, BrainMap facilitates coordinate-based meta-analysis (CBMA) of the neuroimaging literature en masse or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task-activation literature, BrainMap is now expanding to include voxel-based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole-brain, voxel-wise method that measures significant structural differences between or within groups which are reported as standardized, peak x-y-z coordinates. Here we describe BrainMap VBM, including the meta-data structure, current data volume, and automated reverse inference functions (region-to-disease profile) of this new community resource. CBMA offers a robust methodology for retaining true-positive and excluding false-positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized en masse or at the level of clinical disease or anatomic location. As a use-case scenario for BrainMap VBM, we illustrate a trans-diagnostic data-mining procedure wherein we explore the underlying network structure of 2,002 experiments representing over 53,000 subjects through independent components analysis (ICA). To reduce data-redundancy effects inherent to any database, we demonstrate two data-filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network- and disease-specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches.


atrophy; data-mining; independent components analysis; networks; pattern analysis; structural covariance; structural magnetic resonance imaging; transdiagnostic; voxel-based morphometry

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