Prog Neuropsychopharmacol Biol Psychiatry. 2025 Sep 11:111497. doi: 10.1016/j.pnpbp.2025.111497. Online ahead of print.
ABSTRACT
BACKGROUND: Functional connectivity (FC) features serve as effective biomarkers to enhance the diagnostic and treatment of major depressive disorder (MDD). While sex hormones play a crucial role in MDD pathogenesis, neuroimaging signatures specifically linked to sex hormone fluctuations remain critically underexplored for MDD identification.
METHODS: A dataset including 7316 participants with sex hormones and depression assessment was used to analyzed the relationships between depression and sex hormones across age and sex groups. Additionally, employing REST-meta-MDD dataset including 753 MDD patients, we established a novel graph classification framework based on multi-convolution network and attention pooling to identify MDD subtypes related to sex hormone dynamics RESULTS: MDD individuals of both sexes showed elevated estrogen levels than healthy controls (HC). Female MDD individuals also had higher testosterone levels than HC. Depressive symptoms differences between young and middle-aged MDD individuals were predominantly observed in females, whereas no significant age-related variations were detected in males. Our novel method achieved over 75 % accuracy in classifying young and middle-aged MDD patients. Discriminative features were mainly in the sensorimotor network for males and the cingulo-opercular network for females CONCLUSION: These findings revealed that sex- and age-specific FCs were critically in identifying MDD subtypes, especially for female patients. The indirect association with lifelong sex hormone fluctuation suggests that future research should investigate sex hormone effects across age-sex dimensions rather than merely comparing imaging differences. Thus, this approach could advance personalized MDD diagnosis and clinical interventions.
PMID:40945817 | DOI:10.1016/j.pnpbp.2025.111497
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