Mechanisms underlying the spontaneous reorganization of depression network after stroke.

Abstract

Exploring the causal relationship between focal brain lesions and post-stroke depression (PSD) can provide therapeutic insights. However, a gap exists between causal and therapeutic information. Exploring post-stroke brain repair processes post-stroke could bridge this gap. We defined a depression network using the normative connectome and investigated the predictive capacity of lesion-induced network damage on depressive symptoms in discovery cohort of 96 patients, at baseline and six months post-stroke. Stepwise functional connectivity (SFC) was used to examine topological changes in the depression network over time to identify patterns of network reorganization. The predictive value of reorganization information was evaluated for follow-up symptoms in discovery and validation cohort 1 (22 worsening PSD patients) as well as for treatment responsiveness in validation cohort 2 (23 antidepressant-treated patients). We evaluated the consistency of significant reorganization areas with neuromodulation targets. Spatial correlations of network reorganization patterns with gene expression and neurotransmitter maps were analyzed. The predictive power of network damage for symptoms diminished at follow-up compared to baseline (Δadjusted R2=-0.070, p<0.001). Reorganization information effectively predicted symptoms at follow-up in the discovery cohort (adjust R2=0.217, 95%CI: 0.010 to 0.431), as well as symptom exacerbation (r=0.421, p=0.033) and treatment responsiveness (r=0.587, p=0.012) in the validation cohorts. Regions undergoing significant reorganization overlapped with neuromodulatory targets known to be effective in treating depression. The reorganization of the depression network was associated with immune-inflammatory responses gene expressions and gamma-aminobutyric acid. Our findings may yield important insights into the repair mechanisms of PSD and provide a critical context for developing post-stroke treatment strategies.