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@ARTICLE{Li:906797,
      author       = {Li, Jingwei and Bzdok, Danilo and Chen, Jianzhong and Tam,
                      Angela and Ooi, Leon Qi Rong and Holmes, Avram J. and Ge,
                      Tian and Patil, Kaustubh R. and Jabbi, Mbemba and Eickhoff,
                      Simon B. and Yeo, B. T. Thomas and Genon, Sarah},
      title        = {{C}ross-ethnicity/race generalization failure of behavioral
                      prediction from resting-state functional connectivity},
      journal      = {Science advances},
      volume       = {8},
      number       = {11},
      issn         = {2375-2548},
      address      = {Washington, DC [u.a.]},
      publisher    = {Assoc.},
      reportid     = {FZJ-2022-01698},
      pages        = {eabj1812},
      year         = {2022},
      abstract     = {Algorithmic biases that favor majority populations pose a
                      key challenge to the application of machine learning for
                      precision medicine. Here, we assessed such bias in
                      prediction models of behavioral phenotypes from brain
                      functional magnetic resonance imaging. We examined the
                      prediction bias using two independent datasets
                      (preadolescent versus adult) of mixed ethnic/racial
                      composition. When predictive models were trained on data
                      dominated by white Americans (WA), out-of-sample prediction
                      errors were generally higher for African Americans (AA) than
                      for WA. This bias toward WA corresponds to more WA-like
                      brain-behavior association patterns learned by the models.
                      When models were trained on AA only, compared to training
                      only on WA or an equal number of AA and WA participants, AA
                      prediction accuracy improved but stayed below that for WA.
                      Overall, the results point to the need for caution and
                      further research regarding the application of current
                      brain-behavior prediction models in minority populations.},
      cin          = {INM-7},
      ddc          = {500},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5254},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:35294251},
      UT           = {WOS:000770280500003},
      doi          = {10.1126/sciadv.abj1812},
      url          = {https://juser.fz-juelich.de/record/906797},
}