Parkinson’s disease (PD) is a prion-like neurodegenerative disorder driven by α-synuclein misfolding, spreading pathology across the brain, especially in cases with GBA mutations. Advances in MRI processing, such as graph analysis and connectomics, help map brain-wide functional connectivity and clarify neurodegenerative mechanisms. Network analysis reveals how diseases alter brain organization and hypothesizes abnormal protein spread. The Aggregation Network Diffusion (AND) model predicts future pathology based on baseline data, simulating misfolded protein spread and aggregation into neurotoxic forms. To account for individual variability in disease progression, AND must adapt to subject-specific dynamics for accurate atrophy modelling. Thirteen GBA-positive, 39 GBA-negative PD patients and 60 age- and sex-matched controls underwent clinical evaluation, 3DT1-weighted and resting-state functional MRI (rs-fMRI) at baseline and, only PD patients, over a 7-year period.