Linking Network Damage to Post-Stroke Depression: A Neurotransmitter-Informed Connectome Analysis

Post-stroke depressive symptoms (PSDS) are a frequent and disabling consequence of stroke. While lesion-network studies implicate disruption of large-scale affective circuits in PSDS, the neurobiological factors determining why certain network disruptions confer vulnerability to PSDS remain insufficiently understood. We analyzed data from two independent stroke cohorts (total n = 435). Acute lesion masks were embedded within normative structural connectomes, weighted by positron-emission tomography-derived maps of 19 neurotransmitter receptors and transporters, to quantify neurotransmitter (NT)-informed network damage. Partial least squares regression with variable importance measures was used to identify NT-specific damage scores that were informative for PSDS, as quantified by the Hospital Anxiety and Depression Scale at follow-up. Informative NT-systems were subsequently evaluated in multivariable logistic regression models adjusted for age, sex, lesion volume, and neurological deficit. Across cohorts, multivariate analyses converged on a neurochemical signature involving serotonergic, cholinergic, dopaminergic, and GABAergic networks. Damage to networks related to the serotonin transporter (5-HTT) and the vesicular acetylcholine transporter (VAChT) was independently associated with increased odds of PSDS in covariate-adjusted models and improved model fit beyond clinical and lesion-based predictors. In contrast, associations with other NT systems, including dopaminergic networks, were not consistently implicated across cohorts. These findings identify the serotonergic and cholinergic network architecture as a key neurochemical substrate that modulates vulnerability to PSDS. By integrating structural disconnection mapping with NT-informed connectomics, this study provides a mechanistic framework that links stroke-induced network disruption to PSDS and highlights serotonergic and cholinergic systems as central pathways for hypothesis-driven risk stratification and future multimodal investigations.