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@ARTICLE{Karrer:864112,
      author       = {Karrer, Teresa M. and Bassett, Danielle S. and Derntl,
                      Birgit and Gruber, Oliver and Aleman, André and Jardri,
                      Renaud and Laird, Angela R. and Fox, Peter T. and Eickhoff,
                      Simon and Grisel, Olivier and Varoquaux, Gaël and Thirion,
                      Bertrand and Bzdok, Danilo},
      title        = {{B}rain‐based ranking of cognitive domains to predict
                      schizophrenia},
      journal      = {Human brain mapping},
      volume       = {40},
      number       = {15},
      issn         = {1097-0193},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2019-04012},
      pages        = {4487-4507},
      year         = {2019},
      note         = {Deutsche Forschungsgemeinschaft, Grant/Award Numbers:
                      BZ2/2-1, BZ2/3-1, BZ2/4-1;Paul Allen Foundation; John D. and
                      CatherineT. MacArthur Foundation; Alfred P. SloanFoundation;
                      ISI Foundation; ExploratoryResearch Space, Grant/Award
                      Number:OPSF449; START-Program of the Faculty ofMedicine,
                      Grant/Award Number: 126/16;Amazon AWS Research Grant;
                      InternationalResearch Training Group, Grant/AwardNumber:
                      IRTG2150},
      abstract     = {Schizophrenia is a devastating brain disorder that disturbs
                      sensory perception, motoraction, and abstract thought. Its
                      clinical phenotype implies dysfunction of variousmental
                      domains, which has motivated a series of theories regarding
                      the underlyingpathophysiology. Aiming at a predictive
                      benchmark of a catalog of cognitive functions,we developed a
                      data-driven machine-learning strategy and provide a proof
                      ofprinciple in a multisite clinical dataset (n = 324).
                      Existing neuroscientific knowledge ondiverse cognitive
                      domains was first condensed into neurotopographical maps.
                      Wethen examined how the ensuing meta-analytic cognitive
                      priors can distinguishpatients and controls using brain
                      morphology and intrinsic functional connectivity.Some
                      affected cognitive domains supported well-studied directions
                      of research onauditory evaluation and social cognition.
                      However, rarely suspected cognitivedomains also emerged as
                      disease relevant, including self-oriented processing of
                      bodilysensations in gustation and pain. Such algorithmic
                      charting of the cognitive landscapecan be used to make
                      targeted recommendations for future mental health research.},
      cin          = {INM-7},
      ddc          = {610},
      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:31313451},
      UT           = {WOS:000476081600001},
      doi          = {10.1002/hbm.24716},
      url          = {https://juser.fz-juelich.de/record/864112},
}