Brain age trajectories and cognition after stroke in two longitudinal cohorts

Brain age is a promising neuroimaging biomarker, reflecting biological aging, but long-term trajectories and predictive value for cognitive outcomes post-stroke remains unclear. This study aimed to characterize brain aging trajectories over 8 years following a first-ever stroke and to evaluate the predictive value of brain age estimates for long-term cognitive outcomes. We analysed data from working-age (<65 years) ischaemic stroke patients with small- and medium-sized strokes (lesion volumes <70 ml), using two longitudinal stroke cohorts. T1-weighted MRI was acquired in the acute phase and at multiple time points up to 8 years post-stroke. Montreal cognitive assessment (MoCA) was assessed at follow-up sessions. Brain age was estimated using a state-of-the-art deep learning model. Brain-predicted age difference (Brain-PAD) was calculated as estimated brain age minus chronological age and corrected by regressing on age, age² and sex. Linear mixed-effects models examined Brain-PAD over multiple time points (whole-brain, ipsilesional and contralesional). Normalized brain volume was derived from FreeSurfer and included in the whole-brain analysis. Linear regression models evaluated whether brain age was associated with cognitive performance (MoCA) at long-term follow-up. We included 120 patients [n = 50 (42%) female, mean ± SD age at discharge was 54.9 ± 9 and National Institutes of Health Stroke Scale was 3.7 ± 6.4], with a mean follow-up of 3.4 ± 2.5 years. The mean MoCA score at follow-up was 24.7 ± 3.7. Brain-PAD increased significantly over time in the whole-brain analysis (β = 0.6/year, P < 0.01), indicating 60% acceleration in brain aging after stroke, with the association remaining significant after adjusting for normalized brain volume (β = 0.5/year, P < 0.01). Accelerated brain aging was observed in the ipsilesional hemisphere (β = 0.7/year, P < 0.01), but not the contralesional hemisphere (β = 0.3/year, P = 0.12). Higher brain age in the acute phase of stroke predicted lower MoCA scores at follow-up (β = -0.12, P < 0.05), whereas chronological age was not a significant predictor (P = 0.12). The association between brain age estimations and cognitive performance remained significant after adjusting for age, sex and education (β = -0.42, P < 0.01). In this longitudinal study, we found accelerated brain aging following stroke. Furthermore, brain age was associated with cognitive outcomes several years later, highlighting its potential as an early biomarker for long-term cognitive prognosis.