Brain age prediction and early neurodegeneration detection using contrastive learning on brain biomechanics: a retrospective, multicentre study
Year: 2025
Type: article
Study design: Observational Study
Abstract: BACKGROUND: 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. METHODS: 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. FINDINGS: 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. INTERPRETATION: 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. FUNDING: Gates Cambridge Trust; Cambridge Centre for Data-Driven Discovery (Schmidt Sciences); Wellcome Trust; NIH (R01-AG058853, U01-NS112120); UK EPSRC; UK MRC; Alzheimer's Research UK; Michael J. Fox Foundation; Infinitus China Ltd.
Source: EBioMedicine
Language: English
Subfield: Psychiatry and Mental health
Field: Medicine
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $5,780
Funders: Michael J. Fox Foundation for Parkinson's Research, Wellcome Trust, Alan Turing Institute, Alzheimer's Research Trust, Leverhulme Trust, Royal Society, University of Cambridge, Alzheimer’s Research UK, Cambridge Trust, Gates Cambridge Trust, National Institutes of Health, Medical Research Council, Engineering and Physical Sciences Research Council, National Institute on Aging
Awards: Predicting Dementia: Optimising and translating AI to improve prognosis and clinical pathways, All in one cancer imaging optimisation using an integrated mathematical and deep learning approach, G1828984001, G2529265188, Mechanisms that lie behind the pathology of Parkinson's Disease., Robust and Efficient Analysis Approaches of Remote Imagery for Assessing Population and Forest Health in India, MICA: Multi-parametric and super-resolution imaging of amyloidogenic proteins, Liquid droplets and hydrogels: protein phase transition in health and disease, Liquid droplets and hydrogels: protein phase transition in health and disease, The Mechanobiology of Preclinical Alzheimer’s Disease, The Mechanobiology of Preclinical Alzheimer’s Disease, Cambridge Mathematics of Information in Healthcare (CMIH), G6365737904, Mechanical integrity of memory systems in mild cognitive impairment, EPSRC Centre for Mathematical and Statistical Analysis of Multimodal Clinical Imaging, All in one cancer imaging optimisation using an integrated mathematical and deep learning approach, G7293733238, Combining Knowledge And Data Driven Approaches to Inverse Imaging Problems, PET++: Improving Localisation, Diagnosis and Quantification in Clinical and Medical PET Imaging with Randomised Optimisation, G7962661378, Predicting Dementia: Optimising and translating AI to improve prognosis and clinical pathways, G87014449, G9000284103
OpenAlex ID: W4415751844