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@ARTICLE{Chen:865283,
      author       = {Chen, Ji and Patil, Kaustubh R. and Weis, Susanne and Sim,
                      Kang and Nickl-Jockschat, Thomas and Zhou, Juan and Aleman,
                      André and Sommer, Iris E. and Liemburg, Edith J. and
                      Hoffstaedter, Felix and Habel, Ute and Derntl, Birgit and
                      Liu, Xiaojin and Kogler, Lydia and Regenbogen, Christina and
                      Diwadkar, Vaibhav A. and Stanley, Jeffrey A. and Riedl,
                      Valentin and Jardri, Renaud and Gruber, Oliver and Sotiras,
                      Aristeidis and Davatzikos, Christos and Eickhoff, Simon},
      title        = {{N}eurobiological divergence of the positive and negative
                      schizophrenia subtypes identified upon a new
                      factor-structure of psychopathology using non-negative
                      factorization: {A}n international machine-learning study},
      journal      = {Biological psychiatry},
      volume       = {83},
      number       = {3},
      issn         = {0006-3223},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2019-04803},
      pages        = {282-293},
      year         = {2020},
      note         = {This study was supported by the Deutsche
                      Forschungsgemeinschaft (DFG, EI 816/4-1), the National
                      Institute of Mental Health (R01-MH074457), the Helmholtz
                      Portfolio Theme "Supercomputing and Modeling for the Human
                      Brain", and the European Union’s Horizon 2020 Research and
                      Innovation Programme under Grant Agreement No. 720270 (HBP
                      SGA1) and 785907 (HBP SGA2). Ji Chen has received a Ph.D
                      fellowship from the Chinese Scholarship Council. Also,
                      acknowledgment goes to Asadur Chowdury, PhD (Brain Imaging
                      Research Division, Wayne State University School of
                      Medicine, Detroit, Michigan), who contributed to the early
                      arrangement and communication of the Wayne-State dataset.},
      abstract     = {ObjectiveDisentangling psychopathological heterogeneity in
                      schizophrenia is challenging and previous results remain
                      inconclusive. We employed advanced machine-learning to
                      identify a stable and generalizable factorization of the
                      “Positive and Negative Syndrome Scale (PANSS)”, and used
                      it to identify psychopathological subtypes as well as their
                      neurobiological differentiations.MethodsPANSS data from the
                      Pharmacotherapy Monitoring and Outcome Survey cohort (1545
                      patients, 586 followed up after 1.35±0.70 years) were used
                      for learning the factor-structure by an orthonormal
                      projective non-negative factorization. An international
                      sample, pooled from nine medical centers across Europe, USA,
                      and Asia (490 patients), was used for validation. Patients
                      were clustered into psychopathological subtypes based on the
                      identified factor-structure, and the neurobiological
                      divergence between the subtypes was assessed by
                      classification analysis on functional MRI connectivity
                      patterns.ResultsA four-factor structure representing
                      negative, positive, affective, and cognitive symptoms was
                      identified as the most stable and generalizable
                      representation of psychopathology. It showed higher internal
                      consistency than the original PANSS subscales and previously
                      proposed factor-models. Based on this representation, the
                      positive-negative dichotomy was confirmed as the (only)
                      robust psychopathological subtypes, and these subtypes were
                      longitudinally stable in about $80\%$ of the repeatedly
                      assessed patients. Finally, the individual subtype could be
                      predicted with good accuracy from functional connectivity
                      profiles of the ventro-medial frontal cortex,
                      temporoparietal junction, and
                      precuneus.ConclusionsMachine-learning applied to multi-site
                      data with cross-validation yielded a factorization
                      generalizable across populations and medical systems.
                      Together with subtyping and the demonstrated ability to
                      predict subtype membership from neuroimaging data, this work
                      further disentangles the heterogeneity in schizophrenia.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {572 - (Dys-)function and Plasticity (POF3-572) / SMHB -
                      Supercomputing and Modelling for the Human Brain
                      (HGF-SMHB-2013-2017) / HBP SGA2 - Human Brain Project
                      Specific Grant Agreement 2 (785907)},
      pid          = {G:(DE-HGF)POF3-572 / G:(DE-Juel1)HGF-SMHB-2013-2017 /
                      G:(EU-Grant)785907},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:31748126},
      UT           = {WOS:000505773200013},
      doi          = {10.1016/j.biopsych.2019.08.031},
      url          = {https://juser.fz-juelich.de/record/865283},
}