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@ARTICLE{Wu:1014929,
      author       = {Wu, Jianxiao and Li, Jingwei and Eickhoff, Simon B. and
                      Scheinost, Dustin and Genon, Sarah},
      title        = {{T}he challenges and prospects of brain-based prediction of
                      behaviour},
      journal      = {Nature human behaviour},
      volume       = {7},
      number       = {8},
      issn         = {2397-3374},
      address      = {London},
      publisher    = {Nature Research},
      reportid     = {FZJ-2023-03484},
      pages        = {1255 - 1264},
      year         = {2023},
      note         = {This work was supported by the Deutsche
                      Forschungsgemeinschaft (GE 2835/2–1, EI 816/ 4–1), the
                      Helmholtz Portfolio Theme ‘Supercomputing and Modelling
                      for the Human Brain’ and the European Union’s Horizon
                      2020 Research and Innovation Programme under grant agreement
                      no. 720270 (HBP SGA1) and grant agreement no. 785907 (HBP
                      SGA2).},
      abstract     = {Relating individual brain patterns to behaviour is
                      fundamental in systemneuroscience. Recently, the predictive
                      modelling approach has becomeincreasingly popular, largely
                      due to the recent availability of large opendatasets and
                      access to computational resources. This means that we can
                      usemachine learning models and interindividual differences
                      at the brain levelrepresented by neuroimaging features to
                      predict interindividual differencesin behavioural measures.
                      By doing so, we could identify biomarkers andneural
                      correlates in a data-driven fashion. Nevertheless, this
                      budding fieldof neuroimaging-based predictive modelling is
                      facing issues that may limitits potential applications. Here
                      we review these existing challenges, as wellas those that we
                      anticipate as the field develops. We focus on the impactsof
                      these challenges on brain-based predictions. We suggest
                      potentialsolutions to address the resolvable challenges,
                      while keeping in mind thatsome general and conceptual
                      limitations may also underlie the predictivemodelling
                      approach.},
      cin          = {INM-7},
      ddc          = {150},
      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)16},
      pubmed       = {37524932},
      UT           = {WOS:001040224100003},
      doi          = {10.1038/s41562-023-01670-1},
      url          = {https://juser.fz-juelich.de/record/1014929},
}