A convolutional neural network estimation of brain age is sensitive to cognitive impairment and decline

Yang, Y.; Sathe, A.; Schilling, K.; Shashikumar, N.; Moore, E.; Dumitrescu, L.; Pechman, K. R.; Landman, B. A.; Gifford, K. A.; Hohman, T. J.; Jefferson, A. L.; Archer, D. B.

doi:10.1101/2023.08.10.552494

bioRxiv · 10.1101/2023.08.10.552494

A convolutional neural network estimation of brain age is sensitive to cognitive impairment and decline

Yang, Y.·Sathe, A.·Schilling, K.·Shashikumar, N.·Moore, E.·Dumitrescu, L.·Pechman, K. R.·Landman, B. A.·Gifford, K. A.·Hohman, T. J.·Jefferson, A. L.·Archer, D. B.

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Abstract

The greatest known risk factor for Alzheimers disease (AD) is age. While both normal aging and AD pathology involve structural changes in the brain, their trajectories of atrophy are not the same. Recent developments in artificial intelligence have encouraged studies to leverage neuroimaging-derived measures and deep learning approaches to predict brain age, which has shown promise as a sensitive biomarker in diagnosing and monitoring AD. However, prior efforts primarily involved structural magnetic resonance imaging and conventional diffusion MRI (dMRI) metrics without accounting for partial volume effects. To address this issue, we post-processed our dMRI scans with an advanced free-water (FW) correction technique to compute distinct FW-corrected fractional anisotropy (FAFWcorr) and FW maps that allow for the separation of tissue from fluid in a scan. We built 3 densely connected neural networks from FW-corrected dMRI, T1-weighted MRI, and combined FW+T1 features, respectively, to predict brain age. We then investigated the relationship of actual age and predicted brain ages with cognition. We found that all models accurately predicted actual age in cognitively unimpaired (CU) controls (FW: r=0.66, p=1.62x10-32; T1: r=0.61, p=1.45x10-26, FW+T1: r=0.77, p=6.48x10-50) and distinguished between CU and mild cognitive impairment participants (FW: p=0.006; T1: p=0.048; FW+T1: p=0.003), with FW+T1-derived age showing best performance. Additionally, all predicted brain age models were significantly associated with cross-sectional cognition (memory, FW: {beta}=-1.094, p=6.32x10-7; T1: {beta}=-1.331, p=6.52x10-7; FW+T1: {beta}=-1.476, p=2.53x10-10; executive function, FW: {beta}=-1.276, p=1.46x10-9; T1: {beta}=-1.337, p=2.52x10-7; FW+T1: {beta}=-1.850, p=3.85x10-17) and longitudinal cognition (memory, FW: {beta}=-0.091, p=4.62x10-11; T1: {beta}=-0.097, p=1.40x10-8; FW+T1: {beta}=-0.101, p=1.35x10-11; executive function, FW: {beta}=-0.125, p=1.20x10-10; T1: {beta}=-0.163, p=4.25x10-12; FW+T1: {beta}=-0.158, p=1.65x10-14). Our findings provide evidence that both T1-weighted MRI and dMRI measures improve brain age prediction and support predicted brain age as a sensitive biomarker of cognition and cognitive decline.

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Yang, Y., Sathe, A., Schilling, K., Shashikumar, N., Moore, E., Dumitrescu, L., Pechman, K. R., Landman, B. A., Gifford, K. A., Hohman, T. J., Jefferson, A. L., Archer, D. B.. 2023-08-14. A convolutional neural network estimation of brain age is sensitive to cognitive impairment and decline. https://doi.org/10.1101/2023.08.10.552494

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