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@ARTICLE{Wu:892070,
      author       = {Wu, Jianxiao and Eickhoff, Simon B and Hoffstaedter, Felix
                      and Patil, Kaustubh R and Schwender, Holger and Yeo, B T
                      Thomas and Genon, Sarah},
      title        = {{A} {C}onnectivity-{B}ased {P}sychometric {P}rediction
                      {F}ramework for {B}rain–{B}ehavior {R}elationship
                      {S}tudies},
      journal      = {Cerebral cortex},
      volume       = {31},
      number       = {8},
      issn         = {1460-2199},
      address      = {Oxford},
      publisher    = {Oxford Univ. Press},
      reportid     = {FZJ-2021-01921},
      pages        = {3732–3751},
      year         = {2021},
      abstract     = {The recent availability of population-based studies with
                      neuroimaging and behavioral measurements opens promising
                      perspectives to investigate the relationships between
                      interindividual variability in brain regions' connectivity
                      and behavioral phenotypes. However, the multivariate nature
                      of connectivity-based prediction model severely limits the
                      insight into brain-behavior patterns for neuroscience. To
                      address this issue, we propose a connectivity-based
                      psychometric prediction framework based on individual
                      regions' connectivity profiles. We first illustrate two main
                      applications: 1) single brain region's predictive power for
                      a range of psychometric variables and 2) single psychometric
                      variable's predictive power variation across brain region.
                      We compare the patterns of brain-behavior provided by these
                      approaches to the brain-behavior relationships from
                      activation approaches. Then, capitalizing on the increased
                      transparency of our approach, we demonstrate how the
                      influence of various data processing and analyses can
                      directly influence the patterns of brain-behavior
                      relationships, as well as the unique insight into
                      brain-behavior relationships offered by this approach.},
      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       = {33884421},
      UT           = {WOS:000741348300001},
      doi          = {10.1093/cercor/bhab044},
      url          = {https://juser.fz-juelich.de/record/892070},
}