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

ABSTRACT:

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 hete

SUBMITTER: Clemens B 

PROVIDER: S-EPMC10068286 | biostudies-literature | 2023 Mar

REPOSITORIES: biostudies-literature