{"doi":"10.1101/2020.07.11.20147538","title":"Predicting outcomes of cross-sex hormone therapy in gender dysphoria based on pre-therapy resting-state brain connectivity","abstract":"Abstract Individuals 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.","journal":"medRxiv","year":2020,"id":130351,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":0,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9477,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":243231,"name":"Jamie D. Feusner","orcid":"0000-0002-0391-345X","position":1,"is_corresponding":false},{"id":582693,"name":"Nicco Reggente","orcid":"0000-0002-0511-9962","position":2,"is_corresponding":false},{"id":582694,"name":"Jonathan Vanhoecke","orcid":"0000-0002-9857-1519","position":3,"is_corresponding":false},{"id":582695,"name":"Mats Holmberg","orcid":"0000-0003-2884-9981","position":4,"is_corresponding":false},{"id":582696,"name":"Amirhossein Manzouri","orcid":"0000-0001-5127-9855","position":5,"is_corresponding":false},{"id":582697,"name":"Behzad Sorouri Khorashad","orcid":"0000-0002-9077-1022","position":6,"is_corresponding":false},{"id":582698,"name":"Ivanka Savic","orcid":"0000-0003-4275-0689","position":7,"is_corresponding":false},{"id":484289,"name":"Teena D. Moody","orcid":"0000-0001-7067-9512","position":0,"is_corresponding":true}],"reference_count":45,"raw_metadata":null,"created_at":"2026-07-18T23:15:53.196774Z","pmid":null,"pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}