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@ARTICLE{LefortBesnard:840267,
      author       = {Lefort-Besnard, Jérémy and Bassett, Danielle S and
                      Smallwood, Jonathan and Margulies, Daniel S and Derntl,
                      Birgit and Gruber, Oliver and Aleman, Andre and Jardri,
                      Renaud and Varoquaux, Gaël and Thirion, Bertrand and
                      Eickhoff, Simon and Bzdok, Danilo},
      title        = {{D}ifferent shades of default mode disturbance in
                      schizophrenia: {S}ubnodal covariance estimation in structure
                      and function.},
      journal      = {Human brain mapping},
      volume       = {39},
      number       = {2},
      issn         = {1065-9471},
      address      = {New York, NY},
      publisher    = {Wiley-Liss},
      reportid     = {FZJ-2017-07814},
      pages        = {644–661},
      year         = {2018},
      abstract     = {Schizophrenia is a devastating mental disease with an
                      apparent disruption in the highly associative default mode
                      network (DMN). Interplay between this canonical network and
                      others probably contributes to goal-directed behavior so its
                      disturbance is a candidate neural fingerprint underlying
                      schizophrenia psychopathology. Previous research has
                      reported both hyperconnectivity and hypoconnectivity within
                      the DMN, and both increased and decreased DMN coupling with
                      the multimodal saliency network (SN) and dorsal attention
                      network (DAN). This study systematically revisited network
                      disruption in patients with schizophrenia using data-derived
                      network atlases and multivariate pattern-learning algorithms
                      in a multisite dataset (n = 325). Resting-state
                      fluctuations in unconstrained brain states were used to
                      estimate functional connectivity, and local volume
                      differences between individuals were used to estimate
                      structural co-occurrence within and between the DMN, SN, and
                      DAN. In brain structure and function, sparse inverse
                      covariance estimates of network coupling were used to
                      characterize healthy participants and patients with
                      schizophrenia, and to identify statistically significant
                      group differences. Evidence did not confirm that the
                      backbone of the DMN was the primary driver of brain
                      dysfunction in schizophrenia. Instead, functional and
                      structural aberrations were frequently located outside of
                      the DMN core, such as in the anterior temporoparietal
                      junction and precuneus. Additionally, functional covariation
                      analyses highlighted dysfunctional DMN-DAN coupling, while
                      structural covariation results highlighted aberrant DMN-SN
                      coupling. Our findings reframe the role of the DMN core and
                      its relation to canonical networks in schizophrenia. We thus
                      underline the importance of large-scale neural interactions
                      as effective biomarkers and indicators of how to tailor
                      psychiatric care to single patients.},
      cin          = {INM-7},
      ddc          = {610},
      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)16},
      pubmed       = {pmid:29105239},
      UT           = {WOS:000419856200004},
      doi          = {10.1002/hbm.23870},
      url          = {https://juser.fz-juelich.de/record/840267},
}