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@ARTICLE{Wiersch:1017175,
      author       = {Wiersch, Lisa and Hamdan, Sami and Hoffstaedter, Felix and
                      Votinov, Mikhail and Habel, Ute and Clemens, Benjamin and
                      Derntl, Birgit and Eickhoff, Simon B. and Patil, Kaustubh R.
                      and Weis, Susanne},
      title        = {{A}ccurate sex prediction of cisgender and transgender
                      individuals without brain size bias},
      journal      = {Scientific reports},
      volume       = {13},
      number       = {1},
      issn         = {2045-2322},
      address      = {[London]},
      publisher    = {Macmillan Publishers Limited, part of Springer Nature},
      reportid     = {FZJ-2023-03988},
      pages        = {13868},
      year         = {2023},
      abstract     = {he increasing use of machine learning approaches on
                      neuroimaging data comes with the important concern of
                      confounding variables which might lead to biased predictions
                      and in turn spurious conclusions about the relationship
                      between the features and the target. A prominent example is
                      the brain size difference between women and men. This
                      difference in total intracranial volume (TIV) can cause bias
                      when employing machine learning approaches for the
                      investigation of sex differences in brain morphology. A
                      TIV-biased model will not capture qualitative sex
                      differences in brain organization but rather learn to
                      classify an individual’s sex based on brain size
                      differences, thus leading to spurious and misleading
                      conclusions, for example when comparing brain morphology
                      between cisgender- and transgender individuals. In this
                      study, TIV bias in sex classification models applied to cis-
                      and transgender individuals was systematically investigated
                      by controlling for TIV either through featurewise confound
                      removal or by matching the training samples for TIV. Our
                      results provide strong evidence that models not biased by
                      TIV can classify the sex of both cis- and transgender
                      individuals with high accuracy, highlighting the importance
                      of appropriate modeling to avoid bias in automated decision
                      making.},
      cin          = {INM-7},
      ddc          = {600},
      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       = {37620339},
      UT           = {WOS:001113423900004},
      doi          = {10.1038/s41598-023-37508-z},
      url          = {https://juser.fz-juelich.de/record/1017175},
}