{"doi":"10.1002/hbm.25983","title":"Sex differences in predictors and regional patterns of brain age gap estimates","abstract":"The brain-age-gap estimate (brainAGE) quantifies the difference between chronological age and age predicted by applying machine-learning models to neuroimaging data and is considered a biomarker of brain health. Understanding sex differences in brainAGE is a significant step toward precision medicine. Global and local brainAGE (G-brainAGE and L-brainAGE, respectively) were computed by applying machine learning algorithms to brain structural magnetic resonance imaging data from 1113 healthy young adults (54.45% females; age range: 22-37 years) participating in the Human Connectome Project. Sex differences were determined in G-brainAGE and L-brainAGE. Random forest regression was used to determine sex-specific associations between G-brainAGE and non-imaging measures pertaining to sociodemographic characteristics and mental, physical, and cognitive functions. L-brainAGE showed sex-specific differences; in females, compared to males, L-brainAGE was higher in the cerebellum and brainstem and lower in the prefrontal cortex and insula. Although sex differences in G-brainAGE were minimal, associations between G-brainAGE and non-imaging measures differed between sexes with the exception of poor sleep quality, which was common to both. While univariate relationships were small, the most important predictor of higher G-brainAGE was self-identification as non-white in males and systolic blood pressure in females. The results demonstrate the value of applying sex-specific analyses and machine learning methods to advance our understanding of sex-related differences in factors that influence the rate of brain aging and provide a foundation for targeted interventions.","journal":"Human Brain Mapping","year":2022,"id":239782,"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":48,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.8522,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2022-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":866140,"name":"Ruiyang Ge","orcid":"0000-0002-5547-3935","position":1,"is_corresponding":false},{"id":662900,"name":"Mathilde Antoniades","orcid":"0000-0002-8241-835X","position":2,"is_corresponding":false},{"id":292047,"name":"Amirhossein Modabbernia","orcid":"0000-0003-1564-0441","position":3,"is_corresponding":false},{"id":630078,"name":"Shalaila S. Haas","orcid":"0000-0003-1385-1050","position":4,"is_corresponding":false},{"id":235065,"name":"Heather C. Whalley","orcid":"0000-0002-4505-8869","position":5,"is_corresponding":false},{"id":676705,"name":"Liisa A.M. Galea","orcid":"0000-0003-2874-9972","position":6,"is_corresponding":false},{"id":484120,"name":"Sebastian Popescu","orcid":null,"position":7,"is_corresponding":false},{"id":54146,"name":"James H. Cole","orcid":"0000-0003-1908-5588","position":8,"is_corresponding":false},{"id":246783,"name":"Sophia Frangou","orcid":"0000-0002-3210-6470","position":9,"is_corresponding":false},{"id":866139,"name":"Nicole Sanford","orcid":"0000-0002-4915-2537","position":0,"is_corresponding":true}],"reference_count":65,"raw_metadata":null,"created_at":"2026-07-19T00:22:41.699779Z","pmid":"35790053","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":[]}