This study uses longitudinal histopathology and network dynamical modeling in a mouse α-synuclein model to show that regional pathology follows rise-and-fall trajectories that collapse onto a one-dimensional vulnerability axis tied to monoaminergic neuron composition and expression of proteostatic…

By linking dynamic propagation patterns to specific cellular and transcriptomic features (proteostasis, metabolism, monoaminergic identity), the work highlights actionable molecular and cellular correlates of regional vulnerability that can guide target prioritization, biomarker development, and…

The spread of misfolded proteins through neuronal circuits is a defining feature of neurodegenerative disease, yet the dynamics underlying this process remain poorly understood. Most studies rely on sparsely sampled datasets that capture spatial patterns of pathology but not their temporal evolution. Here, we analyze longitudinal histopathology measurements of α-synuclein pathology across hundreds of brain regions in a mouse model of Parkinson's disease. Network-based dynamical modeling shows that regional pathology does not simply accumulate but instead follows rise-and-fall trajectories across the brain. The inferred parameter landscape reveals a one-dimensional vulnerability axis along which regions with stronger fall dynamics have greater monoaminergic neuronal composition and higher expression of proteostatic and metabolic genes. This vulnerability axis replicates in an independent histopathological dataset, indicating that its dominant transcriptomic structure is preserved. Together, these results suggest that regional vulnerability collapses onto a low-dimensional molecular and cellular axis defined by rise-and-fall dynamics.