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@ARTICLE{Vanasse:845582,
      author       = {Vanasse, Thomas J. and Fox, P. Mickle and Barron, Daniel S.
                      and Robertson, Michaela and Eickhoff, Simon and Lancaster,
                      Jack L. and Fox, Peter T.},
      title        = {{B}rain{M}ap {VBM}: {A}n environment for structural
                      meta-analysis},
      journal      = {Human brain mapping},
      volume       = {39},
      number       = {8},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2018-02807},
      pages        = {3308-3325},
      year         = {2018},
      note         = {National Institutes of Health, Grant/AwardNumbers: MH74457,
                      RR024387,MH084812, NS062254, AA019691,EB015314;
                      Congressionally DirectedMedical Research Program,
                      Grant/AwardNumbers: W81XWH0820112,W81XWH1410316; Department
                      ofDefense, Grant/Award Number:W81XWH1320065},
      abstract     = {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.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:29717540},
      UT           = {WOS:000438666800016},
      doi          = {10.1002/hbm.24078},
      url          = {https://juser.fz-juelich.de/record/845582},
}