A Brain Age Residual Biomarker (BARB): Leveraging MRI-Based Models to Detect Latent Health Conditions in U.S. Veterans

TL;DR

This study develops a CNN-based brain age prediction model using MRI scans of U.S. veterans, demonstrating that residuals between predicted and actual age can serve as biomarkers for latent health conditions like hypertension and diabetes, especially in older adults.

Contribution

The paper introduces a novel MRI-based brain age model with residual analysis as a biomarker for multiple health conditions in veterans, highlighting its potential for early detection.

Findings

Residuals differ significantly across health conditions ($p=0.002$).

Negative residuals correlate with multiple ICD-coded conditions in older adults.

Model achieves an $R^{2}$ of 0.816 on test data.

Abstract

Age prediction using brain imaging, such as MRIs, has achieved promising results, with several studies identifying the model's residual as a potential biomarker for chronic disease states. In this study, we developed a brain age predictive model using a dataset of 1,220 U.S. veterans (18--80 years) and convolutional neural networks (CNNs) trained on two-dimensional slices of axial T2-weighted fast spin-echo and T2-weighted fluid attenuated inversion recovery MRI images. The model, incorporating a degree-3 polynomial ensemble, achieved an $R^{2}$ of 0.816 on the testing set. Images were acquired at the level of the anterior commissure and the frontal horns of the lateral ventricles. Residual analysis was performed to assess its potential as a biomarker for five ICD-coded conditions: hypertension (HTN), diabetes mellitus (DM), mild traumatic brain injury (mTBI), illicit substance abuse/dependence (SAD), and alcohol abuse/dependence (AAD). Residuals grouped by the number of ICD-coded conditions demonstrated different trends that were statistically significant ($p = 0.002$), suggesting a relationship between disease states and predicted brain age. This association was particularly pronounced in patients over 49 years, where negative residuals (indicating advanced brain aging) correlated with the presence of multiple ICD codes. These findings support the potential of residuals as biomarkers for detecting latent health conditions.