Dissecting biological heterogeneity in major depressive disorder based on neuroimaging subtypes with multi-omics data.

ABSTRACT: The heterogeneity of Major Depressive Disorder (MDD) has been increasingly recognized, challenging traditional symptom-based diagnostics and the development of mechanism-targeted therapies. This study aims to identify neuroimaging-based MDD subtypes and dissect their predominant biological characteristics using multi-omics data. A total of 807 participants were included in this study, comprising 327 individuals with MDD and 480 healthy controls (HC). The amplitude of low-frequency fluctuations (ALFF), a functional neuroimaging feature, was extracted for each participant and used to identify MDD subtypes through machine learning clustering. Multi-omics data, including profiles of genetic, epigenetics, metabolomics, and pro-inflammatory cytokines, were obtained. Comparative analyses of multi

SUBMITTER: Tang L 

PROVIDER: S-EPMC11876359 | biostudies-literature | 2025 Mar

REPOSITORIES: biostudies-literature