Multimodal brain-age prediction and cardiovascular risk: The Whitehall II MRI sub-study

Abstract

Brain age is becoming a widely applied imaging-based biomarker of neural aging and potential proxy for brain integrity and health. We estimated multimodal and modality-specific brain age in the Whitehall II (WHII) MRI cohort using machine learning and imaging-derived measures of gray matter (GM) morphology, white matter microstructure (WM), and resting state functional connectivity (FC). The results showed that the prediction accuracy improved when multiple imaging modalities were included in the model (R² = 0.30, 95% CI [0.24, 0.36]). The modality-specific GM and WM models showed similar performance (R² = 0.22 [0.16, 0.27] and R² = 0.24 [0.18, 0.30], respectively), while the FC model showed the lowest prediction accuracy (R² = 0.002 [-0.005, 0.008]), indicating that the FC features were less related to chronological age compared to structural measures. Follow-up analyses showed that FC predictions were similarly low in a matched sub-sample from UK Biobank, and although FC predictions were consistently lower than GM predictions, the accuracy improved with increasing sample size and age range. Cardiovascular risk factors, including high blood pressure, alcohol intake, and stroke risk score, were each associated with brain aging in the WHII cohort. Blood pressure showed a stronger association with white matter compared to gray matter, while no differences in the associations of alcohol intake and stroke risk with these modalities were observed. In conclusion, machine-learning based brain age prediction can reduce the dimensionality of neuroimaging data to provide meaningful biomarkers of individual brain aging. However, model performance depends on study-specific characteristics including sample size and age range, which may cause discrepancies in findings across studies.

Keywords: Brain age prediction; Cardiovascular risk; Machine learning; Multimodal MRI.

Fig. 1

The correlations (Pearson’s r) between brain age deltas of the multimodal model (MM), the gray matter (GM), white matter (WM), and functional connectivity (FC) models, and the external gray matter model (Ext. GM) based on a separate training sample, indicating the amount of shared variance explained by the models. The delta values were first corrected for age-bias as described in Section 2.3.5, and the corrected deltas were used in the correlation analysis.

Fig. 2

Prediction accuracy (y-axis) for functional connectivity (left plot) and gray matter (right plot) for two different sample sizes (N) across five different age ranges. The sample of 610 (red) resembles the size of the WHII dataset, while the sample of 1782 represents the maximum number of participants available with the smallest age range, keeping N stable across age ranges.

Fig. 3

The associations (β ±  standard error) between standardized measures of brain age delta and blood pressure, alcohol intake, and Framingham stroke risk score for each of the brain-age models. The analyses included age as a covariate. The vertical gray line indicates β = 0. MM = multimodal, GM = gray matter, WM = white matter, FC = functional connectivity, Ext. GM = external gray matter model (gray matter predictions based on the external UK Biobank training set (Section 2.3.5).