ArticleCerebral cortex (New York, N.Y. : 1991)2023
Accurate machine learning prediction of sexual orientation based on brain morphology and intrinsic functional connectivity.
Article in Cerebral cortex (New York, N.Y. : 1991), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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Who cites it
1 citing paper in PubMed.
- A brief historic overview of sexual and gender diversity in neuroscience: past, present, and future.Frontiers in human neuroscience · 2024Article
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8 authors.
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Abstract
backgroundSexual orientation in humans represents a multilevel construct that is grounded in both neurobiological and environmental factors.
objectiveHere, 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.
methodsIn both brain assessments, we used penalized logistic regression models and nonparametric permutation.
resultsWe 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.
conclusionsThese 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.
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