# Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

arXiv: https://arxiv.org/abs/2410.22454 · HTML: https://mlsanity.com/paper/2410.22454/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 72 references that are in the map.
- First posted on arXiv 29 Oct 2024.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [3D Whole Brain Segmentation using Spatially Localized Atlas Network Tiles](https://mlsanity.com/paper/1903.12152/) (2019)
- [Brain Age Estimation From MRI Using Cascade Networks with Ranking Loss](https://mlsanity.com/paper/2106.03052/) (2021)
- [Model and predict age and sex in healthy subjects using brain white matter features: A deep learning approach](https://mlsanity.com/paper/2202.03595/) (2022)
- [Predicting Age from White Matter Diffusivity with Residual Learning](https://mlsanity.com/paper/2311.03500/) (2023)

## Built on by (most cited first, 2 of 2)

- [MetaVoxel: Joint Diffusion Modeling of Imaging and Clinical Metadata](https://mlsanity.com/paper/2512.10041/) (2025)
- [Generalizable field of view extension for diffusion MRI via conditional flow matching](https://mlsanity.com/paper/2610.17401/) (2026)

## Abstract (arXiv)

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.