Neurodegenerative disease – AD progression prediction

Alzheimer’s disease (AD) is a progressive neurodegenerative disease that is characterized at the molecular level by the accumulation of two specific protein-based pathologies within the brain: amyloid plaques composed of extracellular amyloid-β peptide (Aβ), intracellular neurofibrillary tangles (NFTs), and abnormally hyper-phosphorylated tau proteins (τ). Despite these protein pathologies have been identified as primary hallmarks of AD, the process whereby protein misfolding develops and propagates in the human brain is still not completely understood. As such, mathematical and in silico models can help elucidating the spread of the pathology. To address this, we are collaborating with the EuroPOND group (Prof Daniel Alexander, Dr Neil Oxtoby and Dr Peter Wijeratne) in modelling the spreading and propagation of misfolded proteins in the brain through a hybrid approach of machine learning and mechanistic models of nucleation, aggregation, and fragmentation of Aβ, NFTs and τ.

Selected relevant works and links:

Garbarino et al. 2021. NeuroImage, doi: 10.1016/j.neuroimage.2021.117980

Fornari et al. 2021. Journal of Theoretical Biology, doi: 10.1016/j.jtbi.2019.110102

The EuroPOND project:

LEFT to RIGHT: Starting from diffusion and structural MRI data, visualized using 3D Slicer (, of healthy individuals, connectome and tractography data were  generated using CMIC in-house models, and subsequently with MRtrix3 (
Three-dimensional finite element model of the human brain (blue: grey matter; red: white matter) visualized using Paraview (

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