Brain age prediction and early neurodegeneration detection using contrastive learning on brain biomechanics: a retrospective, multicentre study

TL;DR

This study uses a new AI method on brain scans to predict brain age and detect early signs of neurodegenerative diseases like Alzheimer's more accurately than traditional methods.

Contribution

A self-supervised contrastive regression framework using MRE data to predict brain age and detect early neurodegeneration with higher accuracy than MRI.

Findings

MRE-based models outperformed MRI in brain age prediction with a 3.51-year MAE.

MRE identified distinct biomechanical signatures for Alzheimer's and MCI.

Some cognitively normal individuals showed biomechanical profiles similar to MCI or AD patients.

Abstract

One of the main reasons why drugs for neurodegenerative diseases often fail is that treatment typically begins only after symptoms have appeared—by which point significant, and possibly irreversible, damage may have already occurred. Non-invasive imaging techniques, such as Magnetic Resonance Imaging (MRI), have previously been explored for presymptomatic diagnosis, but with limited success. More recently, Magnetic Resonance Elastography (MRE)—a technique capable of mapping the brain's biomechanical properties, including stiffness and damping ratio—has shown promise in detecting early changes. However, current studies have been limited by small sample sizes, and a lack of robust algorithms capable of accurately interpreting data under such constraints.

We developed a self-supervised contrastive regression framework trained on 3D MRE-derived stiffness and damping ratio maps from 311 healthy individuals (aged 14–90) and evaluated its performance against structural 3D T1-weighted MRI. Brain age predictions were used to compute brain age gaps (BAGs), quantifying deviations from normative ageing trajectories. We applied the models to Alzheimer's disease (AD, n = 11) and mild cognitive impairment (MCI, n = 20) cohorts, and analysed whole-brain and region-specific predictions using occlusion-based saliency maps and subcortical segmentation.

Self-supervised models using MRE achieved a mean absolute error (MAE) of 3.51 years in brain age prediction—significantly outperforming MRI (MAE: 4.79 years, p < 0.05) under matched conditions. The greater age sensitivity of MRE translated into improved differentiation of Alzheimer's disease (AD) and mild cognitive impairment (MCI) from healthy individuals. Stiffness was the dominant ageing biomarker in AD (BAG increase: +9.2 years, p < 0.05), whereas damping ratio revealed early MCI-related changes (BAG increase: +6.3 years, p < 0.05). Region-wise analysis identified the caudate (stiffness) and thalamus (damping ratio) as key markers for AD and MCI, respectively. Notably, some cognitively normal individuals exhibited biomechanical profiles resembling patients with MCI or AD, suggesting that these individuals may share some biomechanical characteristics with clinical populations.

In our controlled experimental setting, MRE combined with contrastive learning provides a sensitive, non-invasive biomarker of brain ageing and neurodegeneration, outperforming MRI and differentiating disease stage–specific biomechanical signatures. Regional BAG profiling may have the potential to identify at-risk, cognitively normal individuals, which could facilitate timely intervention trials in the future, pending longitudinal validation.

10.13039/501100005370Gates Cambridge Trust; Cambridge Centre for Data-Driven Discovery (Schmidt Sciences); 10.13039/100010269Wellcome Trust; 10.13039/100000002NIH (R01-AG058853, U01-NS112120); UK 10.13039/501100000266EPSRC; UK MRC; Alzheimer’s Research UK; 10.13039/100000864Michael J. Fox Foundation; Infinitus China Ltd.

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