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By pairing the results of neuroimaging studies with their relevant meta\u2010data, BrainMap facilitates coordinate\u2010based meta\u2010analysis (CBMA) of the neuroimaging literature <jats:italic>en masse<\/jats:italic> or at the level of experimental paradigm, clinical disease, or anatomic location. Initially dedicated to the functional, task\u2010activation literature, BrainMap is now expanding to include voxel\u2010based morphometry (VBM) studies in a separate sector, titled: BrainMap VBM. VBM is a whole\u2010brain, voxel\u2010wise method that measures significant structural differences between or within groups which are reported as standardized, peak <jats:italic>x<\/jats:italic>\u2013<jats:italic>y<\/jats:italic>\u2013<jats:italic>z<\/jats:italic> coordinates. Here we describe BrainMap VBM, including the meta\u2010data structure, current data volume, and automated reverse inference functions (region\u2010to\u2010disease profile) of this new community resource. CBMA offers a robust methodology for retaining true\u2010positive and excluding false\u2010positive findings across studies in the VBM literature. As with BrainMap's functional database, BrainMap VBM may be synthesized <jats:italic>en masse<\/jats:italic> or at the level of clinical disease or anatomic location. As a use\u2010case scenario for BrainMap VBM, we illustrate a trans\u2010diagnostic data\u2010mining 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\u2010redundancy effects inherent to any database, we demonstrate two data\u2010filtering approaches that proved helpful to ICA. Finally, we apply hierarchical clustering analysis (HCA) to measure network\u2010 and disease\u2010specificity. This procedure distinguished psychiatric from neurological diseases. We invite the neuroscientific community to further exploit BrainMap VBM with other modeling approaches.<\/jats:p>","DOI":"10.1002\/hbm.24078","type":"journal-article","created":{"date-parts":[[2018,5,2]],"date-time":"2018-05-02T07:03:26Z","timestamp":1525244606000},"page":"3308-3325","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":88,"title":["BrainMap VBM: An environment for structural meta\u2010analysis"],"prefix":"10.1002","volume":"39","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4672-0049","authenticated-orcid":false,"given":"Thomas J.","family":"Vanasse","sequence":"first","affiliation":[{"name":"Research Imaging Institute, University of Texas Health Science Center at San Antonio San Antonio Texas"},{"name":"Department of Radiology University of Texas Health Science Center at San Antonio San Antonio Texas"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4997-0003","authenticated-orcid":false,"given":"P. 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