TY  - JOUR
AU  - Wiersch, Lisa
AU  - Hamdan, Sami
AU  - Hoffstaedter, Felix
AU  - Votinov, Mikhail
AU  - Habel, Ute
AU  - Clemens, Benjamin
AU  - Derntl, Birgit
AU  - Eickhoff, Simon B.
AU  - Patil, Kaustubh R.
AU  - Weis, Susanne
TI  - Accurate sex prediction of cisgender and transgender individuals without brain size bias
JO  - Scientific reports
VL  - 13
IS  - 1
SN  - 2045-2322
CY  - [London]
PB  - Macmillan Publishers Limited, part of Springer Nature
M1  - FZJ-2023-03988
SP  - 13868
PY  - 2023
AB  - 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.
LB  - PUB:(DE-HGF)16
C6  - 37620339
UR  - <Go to ISI:>//WOS:001113423900004
DO  - DOI:10.1038/s41598-023-37508-z
UR  - https://juser.fz-juelich.de/record/1017175
ER  -