INTRODUCTION: Eye movement abnormalities are common after ischemic stroke, reflecting both localized damage and broader network disruptions. Resting-state fMRI-derived ocular variance (fMRIeye) tracks clinical severity and functional brain deviations after stroke, but its relationship to structural damage remains unclear.

AIM(S): We hypothesized that fMRIeye serves as a non-invasive marker of lesion volume, white-matter (WM)  integrity, and longitudinal structural recovery, with differential effects for contralesional versus ipsilesional eyes.

METHOD(S): We analyzed 138 stroke patients scanned at 2 weeks, 3 months, and 1 year. Structural MRI included DWI, with lesion masks and diffusion metrics (ADC, FA). Ocular variance regressors were derived from eye regions of fMRI using PCA, separately for each eye. Linear mixed-effects models examined longitudinal associations between ocular variance, lesion metrics, and eye–lesion laterality.

RESULTS: fMRIeye was associated with lesion size and WM disconnection, particularly at 2 weeks. WM disease and lacunae were positively related to oculomotor variance bilaterally, with modest trends for stronger effects in the eye ipsilateral to the lesion. These associations persisted at 3 months and 1 year, indicating enduring effects of lesion burden on oculomotor stability. Lesion side, position, and type did not significantly explain variance, suggesting PCA variance primarily reflects global rather than focal structural damage. Microstructural metrics (ADC, FA) showed weak associations at later follow-ups.

CONCLUSIONS: Overall, fMRIeye provides a sensitive, non-invasive marker of structural brain alterations after stroke. It reflects global lesion burden and structural disconnection, tracks longitudinal changes in WM integrity, and highlights enduring effects of stroke injury on oculomotor stability. These findings support the utility of fMRIeye for clinical MRI datasets and underscore the prognostic value of oculomotor information in monitoring stroke progression and recovery.

FINANCIAL SUPPORT: This project was funded by National Science Center, Poland, grant no 2024/55/D/NZ5/02998. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 857533 and from the International Research Agendas Programme of the Foundation for Polish Science No MAB PLUS/2019/13. The project was created within the project of the Minister of Science and Higher Education “Support for the activity of Centers of Excellence established in Poland under Horizon 2020” on the basis of the contract number MEiN/2023/DIR/3796. We acknowledge Polish  high-performance computing infrastructure PLGrid (HPC Center: ACK Cyfronet AGH) for providing computer facilities and supportwithin computational grant no. PLG/2025/01828