Brain age identification from diffusion MRI synergistically predicts neurodegenerative disease

2024-10-29

arXiv.org

AI Summary

Conventional diffusion MRI (dMRI)-based brain age estimation is confounded by macrostructural variations, limiting its sensitivity to microstructural alterations underlying neurodegenerative diseases. Method: We propose a purely microstructure-driven brain age model, the first to decouple micro- and macrostructural information via non-rigid registration. The framework integrates deep learning–based regression with rigorous multi-center, cross-cohort validation across 12 datasets (N = 13,398). Contribution/Results: We identify a bidirectional, dynamic pattern in brain age gap: significant positive deviation during the cognitively normal (CN) → mild cognitive impairment (MCI) transition, followed by negative deviation upon progression to Alzheimer’s disease (AD). Our model predicts CN-to-MCI conversion up to five years before clinical onset, outperforming state-of-the-art T1-weighted brain age models. This establishes a novel paradigm for probing microstructural mechanisms of neurodegeneration and enabling preclinical intervention.

Technology Category

• Humans and AI: Brain-Sensing and Analysis
• Machine Learning: Graph-based Machine Learning
• Cognitive Modeling & Cognitive Systems: Neural Spike Coding

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Web Mining and Content Analysis: Models for Web evolution

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. 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.

Problem

• Develop microstructure-specific brain age model
• Mitigate macrostructural information in dMRI
• Predict neurodegenerative disease transition early

Innovation

• Non-rigid image registration for microstructure specificity
• Diffusion MRI-based brain age estimation model
• Comparison with T1-weighted MRI models