Gray matter brain age predicts cognitive outcome one year after ischemic stroke

Abstract

Background

Predicting post-stroke cognitive impairment (PSCI) remains challenging.

Objective

This study validated two brain age metrics—Gray Matter Brain Age (GMBA) and Predicted Age Difference (PAD)—as independent predictors of 12-month cognitive decline following ischemic stroke.

Methods

We analyzed 39 patients with ischemic stroke. Brain age was estimated using a machine learning model trained on healthy controls (n = 362). Baseline assessments, including demographics, clinical severity, and thick-sliced (7 mm) 2D T1- and T2-weighted MRI, were performed during acute hospitalization (median 5 days post-stroke). Change in Clinical Dementia Rating-Sum of Boxes (ΔCDR-SB) was measured between baseline and 12 months. Multiple linear regression (MLR) and receiver operating characteristic (ROC) analysis identified predictors of ΔCDR-SB.

Results

MLR identified two significant models. In Model 1, PAD (β = 0.109, p = 0.035) and chronological age (β = 0.082, p = 0.038) were independent predictors (Adjusted R² = 0.183). In Model 2, GMBA emerged as the sole robust predictor (β = 0.092, p = 0.002; Adjusted R² = 0.201). ROC analysis showed GMBA possessed the highest discriminative ability (AUC = 0.717, p = 0.026), followed by PAD (AUC = 0.691, p = 0.050), while chronological age was not significant (AUC = 0.629, p = 0.188).

Conclusions

Both GMBA and PAD are independent predictors of PSCI. Notably, these metrics maintained high predictive utility even when derived from real-world, thick-sliced MRI protocols. Integrating brain age estimation into routine clinical workflows can potentially facilitate early identification of patients at high risk for PSCI.