Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity
Individuals with gender incongruence (GI) 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-sex hormone...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-01-01
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| Series: | NeuroImage: Clinical |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2213158220303545 |
| _version_ | 1818844802463039488 |
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| author | Teena D Moody Jamie D. Feusner Nicco Reggente Jonathan Vanhoecke Mats Holmberg Amirhossein Manzouri Behzad Sorouri Khorashad Ivanka Savic |
| author_facet | Teena D Moody Jamie D. Feusner Nicco Reggente Jonathan Vanhoecke Mats Holmberg Amirhossein Manzouri Behzad Sorouri Khorashad Ivanka Savic |
| author_sort | Teena D Moody |
| collection | DOAJ |
| description | Individuals with gender incongruence (GI) 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-sex hormone therapy and gender-affirming surgery can be helpful. Cross-sex 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 incongruence participated. The model significantly predicted post-therapy body congruence, with the highest predictive features coming from the cingulo-opercular (R2 = 0.41) and fronto-parietal (R2 = 0.30) 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. |
| first_indexed | 2024-12-19T05:19:33Z |
| format | Article |
| id | doaj.art-35a2372906404233a67b8fb351a20157 |
| institution | Directory Open Access Journal |
| issn | 2213-1582 |
| language | English |
| last_indexed | 2024-12-19T05:19:33Z |
| publishDate | 2021-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage: Clinical |
| spelling | doaj.art-35a2372906404233a67b8fb351a201572022-12-21T20:34:33ZengElsevierNeuroImage: Clinical2213-15822021-01-0129102517Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivityTeena D Moody0Jamie D. Feusner1Nicco Reggente2Jonathan Vanhoecke3Mats Holmberg4Amirhossein Manzouri5Behzad Sorouri Khorashad6Ivanka Savic7Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA; Corresponding author at: University of California, Los Angeles, Department of Psychiatry and Biobehavioral Sciences, Los Angeles, CA 90254, USA.Department of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USATiny Blue Dot Foundation, Santa Monica, CA USA; Institute for Advanced Consciousness Studies, Santa Monica, CA, USADepartment of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine at UCLA, Los Angeles, CA, USAKarolinska Institutet, Stockholm, Sweden; ANOVA, Karolinska University Hospital, Stockholm, SwedenKarolinska Institutet, Stockholm, SwedenKarolinska Institutet, Stockholm, SwedenKarolinska Institutet, Stockholm, SwedenIndividuals with gender incongruence (GI) 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-sex hormone therapy and gender-affirming surgery can be helpful. Cross-sex 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 incongruence participated. The model significantly predicted post-therapy body congruence, with the highest predictive features coming from the cingulo-opercular (R2 = 0.41) and fronto-parietal (R2 = 0.30) 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.http://www.sciencedirect.com/science/article/pii/S2213158220303545Gender incongruenceCross-sex hormone therapyMachine learningLASSOTransgenderGender dysphoria |
| spellingShingle | Teena D Moody Jamie D. Feusner Nicco Reggente Jonathan Vanhoecke Mats Holmberg Amirhossein Manzouri Behzad Sorouri Khorashad Ivanka Savic Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity NeuroImage: Clinical Gender incongruence Cross-sex hormone therapy Machine learning LASSO Transgender Gender dysphoria |
| title | Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity |
| title_full | Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity |
| title_fullStr | Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity |
| title_full_unstemmed | Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity |
| title_short | Predicting outcomes of cross-sex hormone therapy in transgender individuals with gender incongruence based on pre-therapy resting-state brain connectivity |
| title_sort | predicting outcomes of cross sex hormone therapy in transgender individuals with gender incongruence based on pre therapy resting state brain connectivity |
| topic | Gender incongruence Cross-sex hormone therapy Machine learning LASSO Transgender Gender dysphoria |
| url | http://www.sciencedirect.com/science/article/pii/S2213158220303545 |
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