ArticleTranslational psychiatry2026
Disentangling individual heterogeneity reveals robust network and molecular signatures of major depressive disorder with suicidal ideation.
Article in Translational psychiatry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Dissecting the Ecological Structure of Health and Disease in the Global Gut Microbiome.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Temporal dynamic brain-heart interaction alterations associated with suicidal ideation in major depressive disorder.BMC psychiatry · 2026Article
- Depressive symptoms as a mediator in the association between regular physical activity and irritable bowel syndrome-related symptom severity: a cross-sectional study among college students.Frontiers in psychology · 2026Article
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Authors and funding
11 authors.
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Abstract
The neurobiological mechanisms of major depressive disorder with suicidal ideation (MDDSI) remain unclear, partly due to individual heterogeneity among patients with MDDSI. We developed a multi-level framework to extract individual-shared (IShN) and individual-specific brain networks (ISpN) using personalized principal component analysis (perPCA), construct structure-function coupling (SFC) network via graph embedding, and map network alterations to transcriptomic and neurotransmitter distributions. Structural, functional, and SFC networks were examined in 528 participants and replicated in 123 participants of an independent cohort. After removing individual heterogeneity, patients with MDDSI showed convergent disruptions within the default-mode network and action mode network across structural, functional, and SFC networks. These alterations corresponded to 5-HT2a and to the expression of genes involved in neurotransmitter transport, synaptic signalling, and neurodevelopmental pathways. By disentangling subject-specific components, the ISpN captured symptom-relevant variations that were obscured in the original brain networks, enabling more accurate diagnostic classification. Our findings identify reproducible, cross-modal network abnormalities and their molecular correlates underlying MDDSI, demonstrating the importance of disentangling individual heterogeneity for advancing the neurobiological understanding of MDDSI.
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