Predicting outcomes of cross-sex hormone therapy in gender dysphoria based on pre-therapy resting-state brain connectivity

Abstract

AbstractIndividuals with gender dysphoria experience serious distress due to incongruence between their gender identity and birth-assigned sex. Sociological, cultural, interpersonal, and biological factors are likely contributory, and for some individuals medical treatment such as cross-hormone therapy and gender affirming surgery can be helpful. Cross-hormone therapy can be effective for reducing body incongruence, but responses vary, and there is no reliable way to predict therapeutic outcomes. We used clinical and MRI data before cross-sex hormone therapy as features to train a machine learning model to predict individuals’ post-therapy body congruence (the degree to which photos of their bodies match their self-identities). Twenty-five trans women and trans men with gender dysphoria participated. The model significantly predicted post-therapy body congruence, with the highest predictive features coming from the fronto-parietal and cingulo-opercular networks. This study provides evidence that hormone therapy efficacy can be predicted from information collected before therapy and that patterns of functional brain connectivity may provide insights into body-brain effects of hormones, affecting one’s sense of body congruence. Results could help identify the need for personalized therapies in individuals predicted to have low body-self congruence after standard therapy.