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@ARTICLE{Larabi:890484,
      author       = {Larabi, Daouia I. and Marsman, Jan-Bernard C. and Aleman,
                      André and Tijms, Betty M. and Opmeer, Esther M. and
                      Pijnenborg, Gerdina H. M. and van der Meer, Lisette and van
                      Tol, Marie-José and Ćurčić-Blake, Branislava},
      title        = {{I}nsight does not come at random: {I}ndividual gray matter
                      networks relate to clinical and cognitive insight in
                      schizophrenia},
      journal      = {Progress in neuro-psychopharmacology $\&$ biological
                      psychiatry},
      volume       = {109},
      issn         = {0278-5846},
      address      = {Amsterdam [u.a.]},
      publisher    = {Elsevier Science},
      reportid     = {FZJ-2021-00992},
      pages        = {110251 -},
      year         = {2021},
      abstract     = {AbstractBackgroundImpaired clinical and cognitive insight
                      are prevalent in schizophrenia and relate to poorer outcome.
                      Good insight has been suggested to depend on social
                      cognitive and metacognitive abilities requiring global
                      integration of brain signals. Impaired insight has been
                      related to numerous focal gray matter (GM) abnormalities
                      distributed across the brain suggesting dysconnectivity at
                      the global level. In this study, we test whether global
                      integration deficiencies reflected in gray matter network
                      connectivity underlie individual variations in
                      insight.MethodsWe used graph theory to examine whether
                      individual GM-network metrics relate to insight in patients
                      with a psychotic disorder (n = 114). Clinical insight was
                      measured with the Schedule for the Assessment of
                      Insight–Expanded and item G12 of the Positive and Negative
                      Syndrome Scale, and cognitive insight with the Beck
                      Cognitive Insight Scale. Individual GM-similarity networks
                      were created from GM-segmentations of T1-weighted MRI-scans.
                      Graph metrics were calculated using the Brain Connectivity
                      Toolbox.ResultsNetworks of schizophrenia patients with
                      poorer clinical insight showed less segregation (i.e.
                      clustering coefficient) into specialized subnetworks at the
                      global level. Schizophrenia patients with poorer cognitive
                      insight showed both less segregation and higher
                      connectedness (i.e. lower path length) of their brain
                      networks, making their network topology more
                      “random”.ConclusionsOur findings suggest less segregated
                      processing of information in patients with poorer cognitive
                      and clinical insight, in addition to higher connectedness in
                      patients with poorer cognitive insight. The ability to take
                      a critical perspective on one's symptoms (clinical insight)
                      or views (cognitive insight) might depend especially on
                      segregated specialized processing within distinct
                      subnetworks.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {525 - Decoding Brain Organization and Dysfunction
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-525},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {33493651},
      UT           = {WOS:000653445500006},
      doi          = {10.1016/j.pnpbp.2021.110251},
      url          = {https://juser.fz-juelich.de/record/890484},
}