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@ARTICLE{Kobeleva:912553,
      author       = {Kobeleva, Xenia and Varoquaux, Gaël and Dagher, Alain and
                      Adhikari, Mohit and Grefkes, Christian and Gilson, Matthieu},
      title        = {{A}dvancing brain network models to reconcile functional
                      neuroimaging and clinical research},
      journal      = {NeuroImage: Clinical},
      volume       = {36},
      issn         = {2213-1582},
      address      = {[Amsterdam u.a.]},
      publisher    = {Elsevier},
      reportid     = {FZJ-2022-05726},
      pages        = {103262 -},
      year         = {2022},
      abstract     = {Functional magnetic resonance imaging (fMRI) captures
                      information on brain function beyond the
                      anatomicalalterations that are traditionally visually
                      examined by neuroradiologists. However, the fMRI signals are
                      complexin addition to being noisy, so fMRI still faces
                      limitations for clinical applications. Here we review
                      methods thathave been proposed as potential solutions so
                      far, namely statistical, biophysical and decoding models,
                      with theirstrengths and weaknesses. We especially evaluate
                      the ability of these models to directly predict clinical
                      variablesfrom their parameters (predictability) and to
                      extract clinically relevant information regarding
                      biologicalmechanisms and relevant features for
                      classification and prediction (interpretability). We then
                      provide guidelinesfor useful applications and pitfalls of
                      such fMRI-based models in a clinical research context,
                      looking beyond thecurrent state of the art. In particular,
                      we argue that the clinical relevance of fMRI calls for a new
                      generation ofmodels for fMRI data, which combine the
                      strengths of both biophysical and decoding models. This
                      leads toreliable and biologically meaningful model
                      parameters, which thus fulfills the need for simultaneous
                      inter-pretability and predictability. In our view, this
                      synergy is fundamental for the discovery of new
                      pharmacologicaland interventional targets, as well as the
                      use of models as biomarkers in neurology and psychiatry.},
      cin          = {INM-3},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-3-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36451365},
      UT           = {WOS:000892218700001},
      doi          = {10.1016/j.nicl.2022.103262},
      url          = {https://juser.fz-juelich.de/record/912553},
}