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@PHDTHESIS{Chen:884904,
      author       = {Chen, Ji},
      title        = {{M}odeling the psychopathology in schizophrenia: symptom
                      dimensions, subtypes, brain connectivity patterns, and
                      molecular architecture},
      school       = {Heinrich-Heine Universität},
      type         = {Dissertation},
      reportid     = {FZJ-2020-03308},
      pages        = {147},
      year         = {2020},
      note         = {Dissertation, Heinrich-Heine Universität, 2020},
      abstract     = {Summary Schizophrenia is a severe, debilitating, and
                      heterogeneous mental disorder. Disentangling the
                      psychopathological heterogeneity in schizophrenia from its
                      underlying dimensions and the related neurobiology remains a
                      challenge. Although ample efforts have been devoted to their
                      study, symptom dimensions and subtypes, as well as
                      neurobiological substrates and differentiations in
                      schizophrenia remain unclear. In my project, I implemented
                      machine-learning frameworks aiming to develop a new method
                      to robustly and reliably conceptualize the psychopathology
                      of schizophrenia at both symptom and brain levels in a
                      data-driven fashion. First, an orthonormal and projective
                      variant of non-negative matrix factorization (OPNMF) was
                      employed to identify the latent dimensions of the
                      well-established Positive and Negative Syndrome Scale
                      (PANSS). This method is capable of learning compact and
                      homogeneous factors which can be readily generalized to
                      novel patients. By evaluating OPNMF-derived factor models
                      within a large, homogeneous schizophrenia dataset and then
                      cross-validating the yielded models with an independent
                      multi-site sample recruited from Europe, Asia, and the
                      United States, a structure with four dimensions representing
                      negative, positive, affective and cognitive symptoms was
                      identified as the most stable and generalizable. This
                      four-dimensional structure showed higher internal
                      consistency than the original PANSS subscales and previously
                      proposed factor models. Based on the identified dimensions,
                      fuzzy-clustering was employed to derive symptomatically
                      well-separated schizophrenia subtypes. Two core subtypes of
                      schizophrenia patients were identified, with one featuring
                      prominent negative and affective symptoms while the other
                      featuring positive symptomatology. This positive-negative
                      dichotomy was longitudinally stable in about $80\%$ of the
                      repeatedly assessed patients. Neurobiological divergence of
                      the identified subtypes was assessed using classification
                      analysis of resting-state functional MRI measurement with
                      cross-validation in a subset of the multi-site sample.
                      Individual subtypes could be well-discriminated using
                      resting-state functional connectivity (rsFC) profiles of the
                      ventromedial frontal cortex, temporoparietal junction, and
                      precuneus, with the highest classification accuracy of
                      $70\%.$ Individual expression of the four symptom dimensions
                      were predicted using relevance vector machine based on rsFC
                      within 17 meta-analytically defined task-activation
                      networks. A strict validation procedure including 10-fold
                      cross-validation, leave-one-site-out experiments, and
                      generalization to independent samples was conducted to
                      derive robust symptom-network associations. Finally, the
                      significant and robust symptom-predictive networks were
                      spatially correlated with whole-brain density maps of nine
                      receptors and transporters from prior molecular imaging in
                      healthy populations to reveal the molecular architecture
                      related to these networks. The theory-of-mind and the
                      extended socio-affective default networks, which are
                      implicated in social cognition and affective processes, were
                      identified as significantly and robustly predictive of the
                      cognitive symptom dimension. Moreover, node importance of
                      these two networks showed a spatial pattern positively
                      co-varying with D1 dopamine receptor and serotonin reuptake
                      transporter densities as well as presynaptic dopamine
                      capacity.The current work provides a systematic modeling
                      framework of schizophrenia from symptomatology to
                      neurobiology. Together the proposed hybrid
                      dimensional-categorical conceptualization of symptomatology
                      and the revealed intrinsic neurobiological processes and
                      molecular architecture further disentangle the heterogeneity
                      in schizophrenia, possibly allowing for the development of
                      more specifically targeted treatments.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {574 - Theory, modelling and simulation (POF3-574)},
      pid          = {G:(DE-HGF)POF3-574},
      typ          = {PUB:(DE-HGF)11},
      url          = {https://juser.fz-juelich.de/record/884904},
}