Abstract

Speaker: Jakob Träuble, Department of Chemical Engineering and Biotechnology, University of Cambridge, UK.

Title: Brain age prediction and early neurodegeneration detection using contrastive learning on brain biomechanics

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.

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 brain age gap profiling may have the potential to identify at-risk, cognitively normal individuals, which could facilitate timely intervention trials in the future, pending longitudinal validation.

Bio: Jakob Träuble is a PhD student in Biotechnology at the University of Cambridge, working in the Molecular Neuroscience Group led by Prof. Gabriele Kaminski Schierle. His research uses machine learning to study neuronal activity and brain elasticity, with applications to neurodegenerative diseases. He holds a BSc in Physics from the University of Munich and an MPhil in Biotechnology from Cambridge.

Venue: MRC CBU West Wing Seminar Room and Zoom https://us02web.zoom.us/j/82385113580?pwd=RmxIUmphQW9Ud1JBby9nTDQzR0NRdz09 (Meeting ID: 823 8511 3580; Passcode: 299077)