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. Diffusion MRI (dMRI) 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), but younger in participants already diagnosed with Alzheimer's disease (AD). Furthermore, dMRI-based brain age may offer advantages over T1w MRI-based brain age in predicting the transition from CN to MCI up to five years before diagnosis.

@article{gao2025_2410.22454,
  title={ Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease },
  author={ Chenyu Gao and Michael E. Kim and Karthik Ramadass and Praitayini Kanakaraj and Aravind R. Krishnan and Adam M. Saunders and Nancy R. Newlin and Ho Hin Lee and Qi Yang and Warren D. Taylor and Brian D. Boyd and Lori L. Beason-Held and Susan M. Resnick and Lisa L. Barnes and David A. Bennett and Katherine D. Van Schaik and Derek B. Archer and Timothy J. Hohman and Angela L. Jefferson and Ivana Išgum and Daniel Moyer and Yuankai Huo and Kurt G. Schilling and Lianrui Zuo and Shunxing Bao and Nazirah Mohd Khairi and Zhiyuan Li and Christos Davatzikos and Bennett A. Landman },
  journal={arXiv preprint arXiv:2410.22454},
  year={ 2025 }
}