{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,22]],"date-time":"2026-10-22T13:12:17Z","timestamp":1792674737692,"version":"4.5.0"},"reference-count":35,"publisher":"American Medical Association (AMA)","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JAMA Netw Open"],"abstract":"Importance<\/jats:title>Despite decades of neuroimaging studies reporting brain structural and functional alterations in depression, discrepancies in findings across studies and limited convergence across meta-analyses have raised questions about the consistency and robustness of the observed brain phenotypes.<\/jats:p><\/jats:sec>Objective<\/jats:title>To investigate the associations between 6 operational criteria of lifetime exposure to depression and functional and structural neuroimaging measures.<\/jats:p><\/jats:sec>Design, Setting, and Participants<\/jats:title>This cross-sectional study analyzed data from a UK Biobank cohort of individuals aged 45 to 80 years who were enrolled between January 1, 2014, and December 31, 2018. Participants included individuals with a lifetime exposure to depression and matched healthy controls without indications of psychosis, mental illness, behavior disorder, and disease of the nervous system. Six operational criteria of lifetime exposure to depression were evaluated: help seeking for depression; self-reported depression; antidepressant use; depression definition by Smith et al; hospital International Statistical Classification of Diseases and Related Health Problems, Tenth Revision<\/jats:italic> (ICD-10<\/jats:italic>) diagnosis codes F32 and F33; and Composite International Diagnostic Interview Short Form score. Six increasingly restrictive depression definitions and groups were defined based on the 6 depression criteria, ranging from meeting only 1 criterion to meeting all 6 criteria. Data were analyzed between January and October 2022.<\/jats:p><\/jats:sec>Main Outcomes and Measures<\/jats:title>Functional measures were calculated using voxel-wise fractional amplitude of low-frequency fluctuation (fALFF), global correlation (GCOR), and local correlation (LCOR). Structural measures were calculated using gray matter volume (GMV).<\/jats:p><\/jats:sec>Results<\/jats:title>The study included 20\u202f484 individuals with lifetime depression (12\u202f645 females [61.7%]; mean [SD] age, 63.91 [7.60] years) and 25\u202f462 healthy controls (14 078 males [55.3%]; mean [SD] age, 65.05 [7.8] years). Across all depression criteria, individuals with lifetime depression displayed regionally consistent decreases in fALFF, LCOR, and GCOR (Cohen d<\/jats:italic> range, \u22120.53 [95% CI, \u22120.88 to \u22120.15] to \u22120.04 [95% CI, \u22120.07 to \u22120.01]) but not in GMV (Cohen d<\/jats:italic> range, \u22120.47 [95 % CI, \u22120.75 to \u22120.12] to 0.26 [95% CI, 0.15-0.37]). Hospital ICD-10<\/jats:italic> diagnosis codes F32 and F33 (median [IQR] difference in effect sizes, \u22120.14 [\u22120.17 to \u22120.11]) and antidepressant use (median [IQR] difference in effect sizes, \u22120.12 [\u22120.16 to \u22120.10]) were criteria associated with the most pronounced alterations.<\/jats:p><\/jats:sec>Conclusions and Relevance<\/jats:title>Results of this cross-sectional study indicate that lifetime exposure to depression was associated with robust functional changes, with a more restrictive depression definition revealing more pronounced alterations. Different inclusion criteria for depression may be associated with the substantial variation in imaging findings reported in the literature.<\/jats:p><\/jats:sec>","DOI":"10.1001\/jamanetworkopen.2023.56787","type":"journal-article","created":{"date-parts":[[2024,2,19]],"date-time":"2024-02-19T16:32:19Z","timestamp":1708360339000},"page":"e2356787","source":"Crossref","is-referenced-by-count":8,"title":["Lifetime Exposure to Depression and Neuroimaging Measures of Brain Structure and Function"],"prefix":"10.1001","volume":"7","author":[{"given":"Xinyi","family":"Wang","sequence":"first","affiliation":[{"name":"School of Biological Sciences and Medical Engineering, Child Development, and Learning Science, Key Laboratory of Ministry of Education, Southeast University, Nanjing, China"},{"name":"Institute of Neuroscience and Medicine, INM-7: Brain and Behaviour, Research Centre J\u00fclich, J\u00fclich, 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