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@ARTICLE{Chen:884823,
      author       = {Chen, Ji and Müller, Veronika I. and Dukart, Jürgen and
                      Hoffstaedter, Felix and Baker, Justin T. and Holmes, Avram
                      J. and Vatansever, Deniz and Nickl-Jockschat, Thomas and
                      Liu, Xiaojin and Derntl, Birgit and Kogler, Lydia and
                      Jardri, Renaud and Gruber, Oliver and Aleman, André and
                      Sommer, Iris E. and Eickhoff, Simon B. and Patil, Kaustubh
                      R.},
      title        = {{I}ntrinsic connectivity patterns of task-defined brain
                      networks allow individual prediction of cognitive symptom
                      dimension of schizophrenia and are linked to molecular
                      architecture},
      journal      = {Biological psychiatry},
      volume       = {89},
      number       = {3},
      issn         = {0006-3223},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2020-03278},
      pages        = {308-319},
      year         = {2020},
      abstract     = {Background: Despite the marked inter-individual variability
                      in the clinical presentation of schizophrenia, it remains
                      unclear the extent to which individual dimensions of
                      psychopathology may be reflected in variability across the
                      collective set of functional brain connections. Here, we
                      address this question using network-based predictive
                      modeling of individual psychopathology along four
                      data-driven symptom dimensions. Follow-up analyses assess
                      the molecular underpinnings of predictive networks by
                      relating them to neurotransmitter-receptor distribution
                      patterns. Methods: We investigated resting-state fMRI data
                      from 147 schizophrenia patients recruited at seven sites.
                      Individual expression along negative, positive, affective,
                      and cognitive symptom dimensions was predicted using
                      relevance vector machine based on functional connectivity
                      within 17 meta-analytic task-networks following a repeated
                      10-fold cross-validation and leave-one-site-out analyses.
                      Results were validated in an independent sample. Networks
                      robustly predicting individual symptom dimensions were
                      spatially correlated with density maps of nine
                      receptors/transporters from prior molecular imaging in
                      healthy populations. Results: Ten-fold and
                      leave-one-site-out analyses revealed five predictive
                      network-symptom associations. Connectivity within
                      theory-of-mind, cognitive reappraisal, and mirror neuron
                      networks predicted negative, positive, and affective symptom
                      dimensions, respectively. Cognitive dimension was predicted
                      by theory-of-mind and socio-affective-default networks.
                      Importantly, these predictions generalized to the
                      independent sample. Intriguingly, these two networks were
                      positively associated with D1 dopamine receptor and
                      serotonin reuptake transporter densities as well as
                      dopamine-synthesis-capacity. Conclusions: We revealed a
                      robust association between intrinsic functional connectivity
                      within networks for socio-affective processes and the
                      cognitive dimension of psychopathology. By investigating the
                      molecular architecture, the present work links dopaminergic
                      and serotonergic systems with the functional topography of
                      brain networks underlying cognitive symptoms in
                      schizophrenia.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571)},
      pid          = {G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33357631},
      UT           = {WOS:000603473100016},
      doi          = {10.1016/j.biopsych.2020.09.024},
      url          = {https://juser.fz-juelich.de/record/884823},
}