Speaker
Description
Bipolar disorder (BD) is a severe affective disorder with recurrent mood episodes that is associated with large-scale brain network dysconnectivity and molecular vulnerability.
We analysed 126 BD patients and 852 healthy controls using diffusion MRI-based structural connectomics and genome-wide genotyping, testing node-wise connectivity with robust regression for diagnostic effects and bipolar polygenic risk score associations. A planned imaging–transcriptomics extension will integrate connectome metrics with Allen Human Brain Atlas gene expression data, using spatial correlations and enrichment analyses to map network alterations onto molecular pathways.
Node-wise analyses revealed widespread structural connectivity alterations in BD and comparable effects of genetic risk, with reduced nodal connectivity in ventral and medial prefrontal, anterior cingulate, and temporal regions, and relative sparing or increases in posterior cortices.
These findings suggest that polygenic liability and structural dysconnectivity converge on the same vulnerable circuits, supporting a systems-level model with potential relevance for biomarker development and targeted intervention.