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@ARTICLE{Wu:1048963,
      author       = {Wu, Jianxiao and Li, Jingwei and Jung, Kyesam and Eickhoff,
                      Simon and Yeo, B. T. Thomas},
      editor       = {Genon, Sarah},
      title        = {{M}ultimodal neuroimaging data boosts the prediction of
                      multifaceted cognition},
      reportid     = {FZJ-2025-05060},
      year         = {2025},
      abstract     = {Relating individual brain patterns to behavioural
                      phenotypes through predictive modelling has been
                      increasingly popular. Several recent studies have focused on
                      the fundamental challenge of improving behavioural
                      prediction based on individual brain patterns, by
                      integrating information from multimodal neuroimaging data.
                      However, the benefit of multimodal integration in
                      brain-based behaviour prediction remains debated due to
                      inconsistent findings. This issue raises the need of a
                      systematic and extensive evaluation. Here, we investigated
                      the necessity and benefit of multimodal integration in 3
                      large datasets covering different age ranges, using 25 to 33
                      feature types from different imaging modalities, and 21
                      behavioural measures from different domains. By setting up
                      multiple predictive models corresponding to increasing
                      levels of multimodal integration, we demonstrated that
                      prediction performance saturates after integrating a few
                      types of features. In general, our analyses revealed that
                      multifaceted cognitive scores tend to require higher levels
                      of multimodal integration, while other predictions may
                      depend on single feature types. In most cases, multimodal
                      integration can remain focused on functional features,
                      especially in young adults. However, predictions in aging
                      can also require structural and diffusion features. Along
                      the same line, while model-free rest and task functional
                      connectivity may provide relevant brain phenotype for
                      behavioural prediction in most applications, in aging,
                      effective connectivity appears relevant too. Thus, our study
                      demonstrates that alternatives to model-free functional
                      connectivity and, more generally, to functional imaging
                      features should be considered for predictive modelling of
                      behaviour, especially in aging populations where
                      understanding interindividual variability in remain as a key
                      challenge.},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / 5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)25},
      doi          = {10.21203/rs.3.rs-7721822/v1},
      url          = {https://juser.fz-juelich.de/record/1048963},
}