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@ARTICLE{VosdeWael:888032,
      author       = {Vos de Wael, Reinder and Benkarim, Oualid and Paquola,
                      Casey and Lariviere, Sara and Royer, Jessica and Tavakol,
                      Shahin and Xu, Ting and Hong, Seok-Jun and Langs, Georg and
                      Valk, Sofie and Misic, Bratislav and Milham, Michael and
                      Margulies, Daniel and Smallwood, Jonathan and Bernhardt,
                      Boris C.},
      title        = {{B}rain{S}pace: a toolbox for the analysis of macroscale
                      gradients in neuroimaging and connectomics datasets},
      journal      = {Communications biology},
      volume       = {3},
      number       = {1},
      issn         = {2399-3642},
      address      = {London},
      publisher    = {Springer Nature},
      reportid     = {FZJ-2020-04610},
      pages        = {103},
      year         = {2020},
      abstract     = {Understanding how cognitive functions emerge from brain
                      structure depends on quantifying how discrete regions are
                      integrated within the broader cortical landscape. Recent
                      work established that macroscale brain organization and
                      function can be described in a compact manner with
                      multivariate machine learning approaches that identify
                      manifolds often described as cortical gradients. By
                      quantifying topographic principles of macroscale
                      organization, cortical gradients lend an analytical
                      framework to study structural and functional brain
                      organization across species, throughout development and
                      aging, and its perturbations in disease. Here, we present
                      BrainSpace, a Python/Matlab toolbox for (i) the
                      identification of gradients, (ii) their alignment, and (iii)
                      their visualization. Our toolbox furthermore allows for
                      controlled association studies between gradients with other
                      brain-level features, adjusted with respect to null models
                      that account for spatial autocorrelation. Validation
                      experiments demonstrate the usage and consistency of our
                      tools for the analysis of functional and microstructural
                      gradients across different spatial scales.},
      cin          = {INM-7},
      ddc          = {570},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572)},
      pid          = {G:(DE-HGF)POF3-572},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:32139786},
      UT           = {WOS:000519705500007},
      doi          = {10.1038/s42003-020-0794-7},
      url          = {https://juser.fz-juelich.de/record/888032},
}