Resting-State Functional MRI Connectivity Disruption Predicts Post-Stroke Epileptogenesis: A Prospective Longitudinal Cohort Study

Abstract

Introduction: Post-stroke epilepsy (PSE) complicates roughly 5–10% of ischaemic strokes, yet clinical and electroencephalographic markers predict unprovoked late seizures only modestly. Resting-state functional MRI (rs-fMRI) with graph theory can non-invasively quantify brain-network architecture. We tested whether subacute functional-connectivity disruption predicts PSE.

Methods: In a prospective longitudinal cohort at a tertiary hospital in Palembang, Indonesia, 150 adults with first-ever supratentorial ischaemic stroke underwent 3.0-T rs-fMRI on day 7–14 and were followed for 24 months (reported per STARD 2015 and TRIPOD). Automated Anatomical Labelling 90-region graph metrics were derived (CONN/SPM12). The reference standard was an International League Against Epilepsy-defined unprovoked late seizure, adjudicated blind to imaging. A penalised support-vector-machine model was internally validated (nested cross-validation, optimism correction, calibration) and compared with a clinical model using DeLong, decision-curve and competing-risks analyses.

Results: PSE occurred in 30 of 150 patients (cumulative incidence 19.2%). PSE patients showed thalamic degree-centrality overload (62.4±8.1 vs 45.2±6.8; p<0.001) and small-world collapse (σ 1.08±0.12 vs 1.25±0.11; p=0.008). The rs-fMRI model achieved sensitivity 86.7% (95% CI 70.3–94.7), specificity 88.3% (81.4–92.9), AUC 0.92 (0.85–0.99), LR+ 7.43 and LR− 0.15, versus clinical AUC 0.74 (DeLong p<0.001); inter-reader kappa was 0.84.

Conclusion: Subacute rs-fMRI connectomic disruption is a strong, independent, internally validated predictor of PSE that outperforms clinical variables. External multicentre validation is warranted before clinical adoption.