Brain Aging in Patients With Cardiovascular Disease From the UK Biobank

Abstract

The brain undergoes complex but normal structural changes during the aging process in healthy adults, whereas deviations from the normal aging patterns of the brain can be indicative of various conditions as well as an increased risk for the development of diseases. The brain age gap (BAG), which is defined as the difference between the chronological age and the machine learning-predicted biological age of an individual, is a promising biomarker for determining whether an individual deviates from normal brain aging patterns. While the BAG has shown promise for various neurological diseases and cardiovascular risk factors, its utility to quantify brain changes associated with diagnosed cardiovascular diseases has not been investigated to date, which is the aim of this study. T1-weighted MRI scans from healthy participants in the UK Biobank were used to train a convolutional neural network (CNN) model for biological brain age prediction. The trained model was then used to quantify and compare the BAGs for all participants in the UK Biobank with known cardiovascular diseases, as well as healthy controls and patients with known neurological diseases for benchmark comparisons. Saliency maps were computed for each individual to investigate whether brain regions used for biological brain age prediction by the CNN differ between groups. The analyses revealed significant differences in BAG distributions for 10 of the 42 sex-specific cardiovascular disease groups investigated compared to healthy participants, indicating disease-specific variations in brain aging. However, no significant differences were found regarding the brain regions used for brain age prediction as determined by saliency maps, indicating that the model mostly relied on healthy brain aging patterns, even in the presence of cardiovascular diseases. Overall, the findings of this work demonstrate that the BAG is a sensitive imaging biomarker to detect differences in brain aging associated with specific cardiovascular diseases. This further supports the theory of the heart-brain axis by exemplifying that many cardiovascular diseases are associated with atypical brain aging.

FIGURE 1

CNN model used in this work for biological brain age prediction using T1w MRI. BatchNorm = batch normalization, Conv3D = 3D convolutional layer, MaxPool = max pooling, ReLU = rectified linear unit.

FIGURE 2

Biological vs. chronological age for the healthy test sets before and after bias correction with the line of best fit (thick, blue) and y = x line (thin, black), M (male), and F (female).

FIGURE 3

Histogram for brain age gaps in healthy test sets.

FIGURE 4

Histograms of the brain age gaps for the noncerebral aneurysm and multiple sclerosis data sets.

FIGURE 5

Saliency maps for various groups; color is the normalized importance value for the voxel, with the outline of the brain regions shown as lines.