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@ARTICLE{Li:1008666,
      author       = {Li, Xuan and Friedrich, Patrick and Patil, Kaustubh R. and
                      Eickhoff, Simon B. and Weis, Susanne},
      title        = {{A} topography-based predictive framework for naturalistic
                      viewing f{MRI}},
      journal      = {NeuroImage},
      volume       = {277},
      number       = {.},
      issn         = {1053-8119},
      address      = {Orlando, Fla.},
      publisher    = {Academic Press},
      reportid     = {FZJ-2023-02466},
      pages        = {120245 -},
      year         = {2023},
      note         = {This work was supported by the European Union's Horizon
                      2020 Research and Innovation Programme under grant agreement
                      no. 945539 (HBP SGA3), and the Deutsche
                      Forschungsgemeinschaft (491111487).},
      abstract     = {Functional magnetic resonance imaging (fMRI) during
                      naturalistic viewing (NV) provides exciting opportunities
                      for studying brain functions in more ecologically valid
                      settings. Understanding individual differences in brain
                      functions during NV and their behavioural relevance has
                      recently become an important goal. However, methods
                      specifically designed for this purpose remain limited. Here,
                      we propose a topography-based predictive framework (TOPF) to
                      fill this methodological gap. TOPF identifies
                      individual-specific evoked activity topographies in a
                      data-driven manner and examines their behavioural relevance
                      using a machine learning-based predictive framework. We
                      validate TOPF on both NV and task-based fMRI data from
                      multiple conditions. Our results show that TOPF effectively
                      and stably captures individual differences in evoked brain
                      activity and successfully predicts phenotypes across
                      cognition, emotion and personality on unseen subjects from
                      their activity topographies. Moreover, TOPF compares
                      favourably with functional connectivity-based approaches in
                      prediction performance, with the identified predictive brain
                      regions being neurobiologically interpretable. Crucially, we
                      highlight the importance of examining individual evoked
                      brain activity topographies in advancing our understanding
                      of the brain-behaviour relationship. We believe that the
                      TOPF approach provides a simple but powerful tool for
                      understanding brain-behaviour relationships on an individual
                      level with a strong potential for clinical applications.},
      cin          = {INM-7},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5251 - Multilevel Brain Organization and Variability
                      (POF4-525) / HBP SGA3 - Human Brain Project Specific Grant
                      Agreement 3 (945539)},
      pid          = {G:(DE-HGF)POF4-5251 / G:(EU-Grant)945539},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {37353099},
      UT           = {WOS:001038690500001},
      doi          = {10.1016/j.neuroimage.2023.120245},
      url          = {https://juser.fz-juelich.de/record/1008666},
}