Current structural MRI‐based brain age estimates and their difference from chronological age—the brain age gap (BAG)—are limited to late‐stage pathological brain‐tissue changes. The addition of physiological MRI features may detect early‐stage pathological brain alterations and improve brain age prediction. This study investigated the optimal combination of structural and physiological arterial spin labelling (ASL) image features and algorithms. Healthy participants (n = 341, age 59.7 ± 14.8 years) were scanned at baseline and after 1.7 ± 0.5 years follow‐up (n = 248, mean age 62.4 ± 13.3 years). From 3 T MRI, structural (T1w and FLAIR) volumetric ROI and physiological (ASL) cerebral blood flow (CBF) and spatial coefficient of variation ROI features were constructed. Multiple combinations of features and machine learning algorithms were evaluated using the Mean Absolute Error (MAE). From the best model, longitudinal BAG repeatability and feature importance were assessed. The ElasticNetCV algorithm using T1w + FLAIR+ASL performed best (MAE = 5.0 ± 0.3 years), and better compared with using T1w + FLAIR (MAE = 6.0 ± 0.4 years, p < .01). The three most important features were, in descending order, GM CBF, GM/ICV, and WM CBF. Average baseline and follow‐up BAGs were similar (−1.5 ± 6.3 and − 1.1 ± 6.4 years respectively, ICC = 0.85, 95% CI: 0.8–0.9, p = .16). The addition of ASL features to structural brain age, combined with the ElasticNetCV algorithm, improved brain age prediction the most, and performed best in a cross‐sectional and repeatability comparison. These findings encourage future studies to explore the value of ASL in brain age in various pathologies.

The structural brain-age makes predictions based on changes in tissue integrity. Adding cerebrovascular MRI biomarkers may add sensitivity to physiological and metabolic changes, hence complementing structural brain-age, and possibly improving its early pathology sensitivity. Baseline and follow-up T1w, FLAIR, and ASL data of 233 healthy participants and combinations of features and algorithms were used to predict &lsquo;Cerebrovascular brain-age&rsquo;. The ExtraTrees algorithm utilising T1w, ASL, and FLAIR features performed best and showed good longitudinal reproducibility.