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@ARTICLE{Chen:911029,
      author       = {Chen, Ji and Patil, Kaustubh R. and Yeo, B. T. Thomas and
                      Eickhoff, Simon B.},
      title        = {{L}everaging {M}achine {L}earning for {G}aining
                      {N}eurobiological and {N}osological {I}nsights in
                      {P}sychiatric {R}esearch},
      journal      = {Biological psychiatry},
      volume       = {9},
      number       = {1},
      issn         = {0006-3223},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2022-04355},
      pages        = {18-28},
      year         = {2022},
      abstract     = {Much attention is currently devoted to developing
                      diagnostic classifiers for mental disorders. Complementing
                      these efforts, we highlight the potential of machine
                      learning to gain biological insights into the
                      psychopathology and nosology of mental disorders. Studies to
                      this end have mainly used brain imaging data, which can be
                      obtained noninvasively from large cohorts and have
                      repeatedly been argued to reveal potentially intermediate
                      phenotypes. This may become particularly relevant in light
                      of recent efforts to identify magnetic resonance
                      imaging–derived biomarkers that yield insight into
                      pathophysiological processes as well as to refine the
                      taxonomy of mental illness. In particular, the accuracy of
                      machine learning models may be used as dependent variables
                      to identify features relevant to pathophysiology. Moreover,
                      such approaches may help disentangle the dimensional (within
                      diagnosis) and often overlapping (across diagnoses)
                      symptomatology of psychiatric illness. We also point out a
                      multiview perspective that combines data from different
                      sources, bridging molecular and system-level information.
                      Finally, we summarize recent efforts toward a data-driven
                      definition of subtypes or disease entities through
                      unsupervised and semisupervised approaches. The latter,
                      blending unsupervised and supervised concepts, may represent
                      a particularly promising avenue toward dissecting
                      heterogeneous categories. Finally, we raise several
                      technical and conceptual aspects related to the reviewed
                      approaches. In particular, we discuss common pitfalls
                      pertaining to flawed input data or analytic procedures that
                      would likely lead to unreliable outputs.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {36307328},
      UT           = {WOS:000911845300006},
      doi          = {10.1016/j.biopsych.2022.07.025},
      url          = {https://juser.fz-juelich.de/record/911029},
}