Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies to slow disease progression and onset. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that mitigates the model's use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information mitigated, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two recent, popular, openly available T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI) (p-value = 0.023), but younger in participants already diagnosed with Alzheimer's disease (AD) (p-value < 0.001). Classifiers using T1w MRI-based brain ages generally outperform those using dMRI-based brain age in classifying CN versus AD participants. Conversely, dMRI-based brain age may offer advantages over T1w MRI-based brain age in predicting the transition from CN to MCI.

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Publication Details

Journal
Imaging Neuroscience
Published
2025-01-01
DOI
https://doi.org/10.1162/imag_a_00552
Citations
3
Primary Topic
Advanced Neuroimaging Techniques and Applications
Type
article
Field-Weighted Citation Impact
4.08

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article

Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

Susan M. Resnick, Lianrui Zuo, Warren D. Taylor, Nancy R. Newlin et al.
3 citations
Imaging Neuroscience
Advanced Neuroimaging Techniques and Applications
4.08
article

Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

Susan M. Resnick, Lianrui Zuo, Warren D. Taylor, Nancy R. Newlin, Timothy J. Hohman, Karthik Ramadass, Chenyu Gao, Lisa L. Barnes, Derek B. Archer, Christos A. Davatzikos, Lori L. Beason‐Held, Praitayini Kanakaraj, Adam M. Saunders, David Bennett, Shunxing Bao, Ho Hin Lee, Kurt G. Schilling, Katherine D. Van Schaik, Qi Yang, Michael E. Kim, Angela Lee Jefferson, Yuankai Huo, Brian D. Boyd, Daniel Moyer, Bennett A. Landman, Aravind R. Krishnan, Ivana Išgum, Nazirah Mohd Khairi, Z. Li, Marilyn S. Albert
article en
3 citations

Abstract

Estimated brain age from magnetic resonance image (MRI) and its deviation from chronological age can provide early insights into potential neurodegenerative diseases, supporting early detection and implementation of prevention strategies to slow disease progression and onset. Diffusion MRI (dMRI), a widely used modality for brain age estimation, presents an opportunity to build an earlier biomarker for neurodegenerative disease prediction because it captures subtle microstructural changes that precede more perceptible macrostructural changes. However, the coexistence of macro- and micro-structural information in dMRI raises the question of whether current dMRI-based brain age estimation models are leveraging the intended microstructural information or if they inadvertently rely on the macrostructural information. To develop a microstructure-specific brain age, we propose a method for brain age identification from dMRI that mitigates the model's use of macrostructural information by non-rigidly registering all images to a standard template. Imaging data from 13,398 participants across 12 datasets were used for the training and evaluation. We compare our brain age models, trained with and without macrostructural information mitigated, with an architecturally similar T1-weighted (T1w) MRI-based brain age model and two recent, popular, openly available T1w MRI-based brain age models that primarily use macrostructural information. We observe difference between our dMRI-based brain age and T1w MRI-based brain age across stages of neurodegeneration, with dMRI-based brain age being older than T1w MRI-based brain age in participants transitioning from cognitively normal (CN) to mild cognitive impairment (MCI) (p-value = 0.023), but younger in participants already diagnosed with Alzheimer's disease (AD) (p-value < 0.001). Classifiers using T1w MRI-based brain ages generally outperform those using dMRI-based brain age in classifying CN versus AD participants. Conversely, dMRI-based brain age may offer advantages over T1w MRI-based brain age in predicting the transition from CN to MCI.

Imaging NeuroscienceVol. 3
Rush University Medical Center (US), National Institutes of Health (US), University City Science Center (US), Johns Hopkins University (US), Vanderbilt University (US), Johns Hopkins Medicine (US), Valley Health System (US), VA Tennessee Valley Healthcare System (US), Geriatric Research Education and Clinical Center (US), National Institute on Aging (US), University of Pennsylvania (US), University of Amsterdam (NL), Vanderbilt University Medical Center (US)
U.S. Department of Defense, Alzheimer's Association, Bristol-Myers Squibb, Eli Lilly and Company, Pfizer, Biogen, University of Southern California, Vanderbilt University, Meso Scale Diagnostics, Vanderbilt Institute for Clinical and Translational Research, Alzheimer's Disease Neuroimaging Initiative, BioClinica, Novartis Pharmaceuticals Corporation, Northern California Institute for Research and Education, Georgia Clinical and Translational Science Alliance, Eisai, Servier, H. Lundbeck A/S, IXICO, National Institutes of Health, Genentech, Canadian Institutes of Health Research, National Institute on Aging, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Institute of Dental and Craniofacial Research, National Center for Advancing Translational Sciences
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Openalex Percentile: Top 7%
Advanced Neuroimaging Techniques and Applications
4.08
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