Neurodegenerative diseases have a significant global impact, affecting millions of individuals world wide. Some of them, known as proteinopathies (for example, Alzheimer’s and Parkinson’s diseases), are characterized by the accumulation and propagation of toxic proteins known as prions. Mathematical models of prion dynamics play a crucial role in understanding disease progression. Several models have been proposed to describe the misfolding process with various levels of detail, the simplest but most used in literature is the Fisher-Kolmogorov. Typically, the Fisher-Kolmogorov problem in neurodegenerative problems exhibits a travelling wave solution. The construction of the classical discontinuous Galerkin formulation causes a lack of positivity preservation of the numerical solution, which loses its physical meaning. This talk presents a structure-preserving, high-order, unconditionally stable numerical method for approximating the solution to the Fisher-Kolmogorov equation on polytopic meshes. The model problem is reformulated using an entropy variable to guarantee solution positivity, boundedness, and satisfaction of a discrete entropy-stability inequality at the numerical level [1]. Moreover, we performed simulations of alpha-synuclein propagation in a two-dimensional brain geometry segmented from MRI data, providing a relevant computational framework for modeling synucleopathies.