Speaker
Description
Network mapping has identified putative "causal" brain circuits for depression, yet deep phenotyping of these circuits in clinically depressed individuals remains lacking. This study presents a transdiagnostic, multimodal investigation of depression circuits using resting-state fMRI, diffusion-weighted imaging, and structural MRI from the Marburg-Münster Affective Disorders Cohort Study (N=2285; HC: n=1026, MDD: n=740, ANX-MDD: n=243, BD: n=150, SSD: n=126). Four established circuits (Depression, Emotion, PTSD, Psychosis) were parcellated using the Schaefer100-yeo7 atlas and tested for alignment with nodal connectome alterations across functional connectivity, structural connectivity, and cortical thickness. We found a non-random spatial alignment between depression-related connectome alterations and depression circuits for both diagnosis- and symptom-based analyses (spin permutation p<0.05), most pronounced for functional connectivity. However, this alignment was neither diagnosis-specific nor superior to permuted data in subject-based tests, suggesting it may reflect dominant eigenmodes of the connectome rather than a disorder-specific biomarker signature.