Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity.

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.