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@PHDTHESIS{Wu:1014711,
      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       = {Heinrich-Heine-Universität Düsseldorf},
      type         = {Dissertation},
      reportid     = {FZJ-2023-03406},
      pages        = {50},
      year         = {2023},
      note         = {Dissertation, Heinrich-Heine-Universität Düsseldorf,
                      2023},
      abstract     = {The study of brain-behavior relationships is a fundamental
                      aspect of neuroscience. Recently, it has become increasingly
                      popular to investigate brain-behavior relationships by
                      relating the interindividual variability in psychometric
                      measure to the interindividual variability in brain imaging
                      data. In particular, prediction approaches with
                      cross-validation can be useful for identifying generalizable
                      brain-behavior relationships in a data-driven manner.
                      Nevertheless, it remains to be ascertained what
                      brain-behavior relationships can be interpreted from the
                      prediction models, and how generalizable the models are to
                      fully new cohorts. In this work, we attempt to fill in the
                      gap of interpretability by developing a region-wise
                      $\acrfull{cbpp}$ framework. This framework involves a
                      region-wise approach where a prediction model is estimated
                      and evaluated for each brain region. The prediction accuracy
                      of each region-wise model is a direct indication of that
                      brain region's association with the psychometric measure
                      predicted. In study 1, we applied the framework to a range
                      of psychometric variables from a large healthy cohort and
                      demonstrated the helpfulness of the framework in
                      constructing region-wise psychometric prediction profiles or
                      psychometric-wise prediction pattern across the brain. In
                      study 2, we demonstrated the usefulness of the framework in
                      assessing cross-cohort replicability and generalizability in
                      terms of brain-behavior relationships derived from the
                      prediction models, instead of just based on prediction
                      accuracies. In study 3, we systematically examined existing
                      psychometric prediction studies, summarizing the trends in
                      the field, calling for the use of large cohorts and external
                      validation. Overall, our work suggested the importance of
                      interpretability and generalizability for psychometric
                      prediction, recommending the use of multiple large cohorts
                      in evaluating the interpretability and generalizability.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5253 - Neuroimaging (POF4-525) / 5251 - Multilevel Brain
                      Organization and Variability (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5253 / G:(DE-HGF)POF4-5251},
      typ          = {PUB:(DE-HGF)11},
      url          = {https://juser.fz-juelich.de/record/1014711},
}