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@PHDTHESIS{Wu:1007357,
      author       = {Wu, Jianxiao},
      title        = {{B}rain {R}egion-wise {C}onnectivity-based {P}sychometric
                      {P}rediction {F}ramework, {I}nterpretation, {R}eplicability
                      and {G}eneralizability},
      school       = {HHU},
      type         = {Dissertation},
      reportid     = {FZJ-2023-02026},
      pages        = {50p},
      year         = {2022},
      note         = {Dissertation, HHU, 2022},
      abstract     = {The study of brain-behavior relationships is a fundamental
                      aspect of neuroscience.Recently, it has become increasingly
                      popular to investigate brain-behavior relationshipsby
                      relating the interindividual variability in psychometric
                      measure to the interindividualvariability in brain imaging
                      data. In particular, prediction approaches
                      withcross-validation can be useful for identifying
                      generalizable brain-behavior relationshipsin a data-driven
                      manner. Nevertheless, it remains to be ascertained what
                      brain-behaviorrelationships can be interpreted from the
                      prediction models, and how generalizable themodels are to
                      fully new cohorts. In this work, we attempt to fill in the
                      gap ofinterpretability by developing a region-wise
                      connectivity-based psychometric prediction(CBPP) framework.
                      This framework involves a region-wise approach where a
                      predictionmodel is estimated and evaluated for each brain
                      region. The prediction accuracy of eachregion-wise model is
                      a direct indication of that brain region’s association
                      with thepsychometric measure predicted. In study 1, we
                      applied the framework to a range ofpsychometric variables
                      from a large healthy cohort and demonstrated the helpfulness
                      ofthe framework in constructing region-wise psychometric
                      prediction profiles orpsychometric-wise prediction pattern
                      across the brain. In study 2, we demonstrated theusefulness
                      of the framework in assessing cross-cohort replicability and
                      generalizability interms of brain-behavior relationships
                      derived from the prediction models, instead of justbased on
                      prediction accuracies. In study 3, we systematically
                      examined existingpsychometric prediction studies,
                      summarizing the trends in the field, calling for the useof
                      large cohorts and external validation. Overall, our work
                      suggested the importance ofinterpretability and
                      generalizability for psychometric prediction, recommending
                      the useof multiple large cohorts in evaluating the
                      interpretability and generalizability.},
      cin          = {INM-7},
      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)11},
      url          = {https://juser.fz-juelich.de/record/1007357},
}