TY - JOUR T1 - Accurate sex prediction of cisgender and transgender individuals without brain size bias JF - bioRxiv DO - 10.1101/2022.07.26.499576 SP - 2022.07.26.499576 AU - Lisa Wiersch AU - Sami Hamdan AU - Felix Hoffstaedter AU - Mikhail Votinov AU - Ute Habel AU - Benjamin Clemens AU - Birgit Derntl AU - Simon B. Eickhoff AU - Kaustubh R. Patil AU - Susanne Weis Y1 - 2022/01/01 UR - http://biorxiv.org/content/early/2022/07/28/2022.07.26.499576.abstract N2 - Brain size differs substantially between human males and females. This difference in total intracranial volume (TIV) can cause bias when employing machine-learning approaches for the investigation of sex differences in brain morphology. TIV-biased models will likely not capture actual 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. Here, TIV bias in sex classification models applied to cis- and transgender individuals was systematically investigated by controlling for brain size either through featurewise confound removal or by matching training samples for TIV. Our results provide evidence that non-TIV-biased models can classify the sex of both cis- and transgender individuals with high accuracy, highlighting the importance of appropriate modelling to avoid bias in automated decision making.Teaser Accurate non-biased structural sex classification in cis- and transgender individuals by matching training samples for TIVCompeting Interest StatementBenjamin Clemens serves as scientific advisor for Dionysus Digital Health, Inc. and holds shares of this company. ER -