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@ARTICLE{Wu:909244,
      author       = {Wu, Jianxiao and Li, Jingwei and Eickhoff, Simon and
                      Hoffstaedter, Felix and Hanke, Michael and Yeo, B. T. Thomas
                      and GENON, Sarah},
      title        = {{C}ross-cohort replicability and generalizability of
                      connectivity-based psychometric prediction patterns},
      journal      = {NeuroImage},
      volume       = {262},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2022-03082},
      pages        = {119569 -},
      year         = {2022},
      abstract     = {An increasing number of studies have investigated the
                      relationships between inter-individual variability in brain
                      regions’ connectivity and behavioral phenotypes, making
                      use of large population neuroimaging datasets. However, the
                      replicability of brain-behavior associations identified by
                      these approaches remains an open question. In this study, we
                      examined the cross-dataset replicability of brain-behavior
                      association patterns for fluid cognition and openness
                      predictions using a previously developed region-wise
                      approach, as well as using a standard whole-brain approach.
                      Overall, we found moderate similarity in patterns for fluid
                      cognition predictions across cohorts, especially in the
                      Human Connectome Project Young Adult, Human Connectome
                      Project Aging, and Enhanced Nathan Kline Institute Rockland
                      Sample cohorts, but low similarity in patterns for openness
                      predictions. In addition, we assessed the generalizability
                      of prediction models in cross-dataset predictions, by
                      training the model in one dataset and testing in another.
                      Making use of the region-wise prediction approach, we showed
                      that first, a moderate extent of generalizability could be
                      achieved with fluid cognition prediction, and that, second,
                      a set of common brain regions related to fluid cognition
                      across cohorts could be identified. Nevertheless, the
                      moderate replicability and generalizability could only be
                      achieved in specific contexts. Thus, we argue that
                      replicability and generalizability in connectivity-based
                      prediction remain limited and deserve greater attention in
                      future studies.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {35985618},
      UT           = {WOS:000999760900008},
      doi          = {10.1016/j.neuroimage.2022.119569},
      url          = {https://juser.fz-juelich.de/record/909244},
}