Conference Agenda

Neurodegenerative diseases, including Alzheimer’s and Parkinson’s diseases, are characterised by progressive neuronal dysfunction and loss, leading to cognitive and motor impairment and imposing a growing societal burden as populations age. Despite major advances in experimental and clinical neuroscience, the mechanisms governing disease onset, progression, and patient-topatient variability remain only partially understood.

In this talk, I will present a mathematical perspective on neurodegeneration, showing how mathematical modelling, scientific computing, and computational learning can help uncover its dynamics. The proposed models integrate mechanistic descriptions of disease dynamics with advanced computational methods and clinical data across multiple spatial and temporal scales. We will explore the spatiotemporal propagation of misfolded proteins—a major pathological hallmark of many neurodegenerative diseases—the role of cerebral waste-clearance pathways, and the impact of pathological changes on neural dynamics.

In the second part of the lecture, I will discuss how computational learning approaches can be combined with mechanistic modelling to infer latent disease states and predict individual trajectories from sparse, irregularly sampled, and multimodal clinical data. The lecture will highlight how mathematics can serve as a powerful language for understanding neurodegenerative diseases, supporting personalised, quantitative assessment of disease progression.