Parkinson’s disease (PD) is the second most common neurodegenerative disease and is characterized by the progressive degeneration of dopaminergic neurons in the substantia nigra pars compacta, leading to profound alterations in striatum, the main input nucleus of the basal ganglia circuitry and dynamics. Despite extensive experimental and clinical research, the relationship between structural reorganization of striatal networks and the emergence of pathological dynamics remains poorly understood. This project aims to address this challenge through large-scale in silico simulations and advanced network analysis methods, with a particular focus on linking network geometry to dynamics in health and disease. This work builds upon one of my previously published articles (during my PhD): Carannante, Ilaria, et al. "The impact of Parkinson’s disease on striatal network connectivity and corticostriatal drive: An in silico study." Network Neuroscience 8.4 (2024): 1149-1172. The computational demands of this project are substantial. The analysis requires large ensembles of simulations across multiple disease conditions and parameter spaces, combined with high-dimensional clique detection and TDA computations on biologically realistic networks containing thousands of neurons and synaptic connections. These analyses are computationally intensive both in memory usage and processing time, particularly when exploring higher-order structures and their temporal evolution. Access to high-performance computing resources is therefore essential to perform systematic parameter sweeps, parallel simulations, and large-scale topological analyses within a feasible timeframe.