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Updated: Aug 28, 2025
Modeling the Functional Network for Spatial Navigation in the Human Brain
Published on: October 13, 2023
Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional
Benjamin Clemens1,2, Jeremy Lefort-Besnard3, Christoph Ritter4
1Department of Psychiatry, Psychotherapy and Psychosomatics, Faculty of Medicine, RWTH Aachen, Pauwelsstr. 30, 52074 Aachen, Germany.
Background:
Sexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors.
Objective:
Here, we bring to bear a machine learning approach to predict sexual orientation from gray matter volumes (GMVs) or resting-state functional connectivity (RSFC) in a cohort of 45 heterosexual and 41 homosexual participants.
Methods:
In both brain assessments, we used penalized logistic regression models and nonparametric permutation.
Results:
We found an average accuracy of 62% (±6.72) for predicting sexual orientation based on GMV and an average predictive accuracy of 92% (±9.89) using RSFC. Regions in the precentral gyrus, precuneus and the prefrontal cortex were significantly informative for distinguishing heterosexual from homosexual participants in both the GMV and RSFC settings.
Conclusions:
These results indicate that, aside from self-reports, RSFC offers neurobiological information valuable for highly accurate prediction of sexual orientation. We demonstrate for the first time that sexual orientation is reflected in specific patterns of RSFC, which enable personalized, brain-based predictions of this highly complex human trait. While these results are preliminary, our neurobiologically based prediction framework illustrates the great value and potential of RSFC for revealing biologically meaningful and generalizable predictive patterns in the human brain.
