Network Models of Neurodegeneration: Bridging Neuronal Dynamics and Disease Progression

Abstract

Neurodegenerative diseases are characterized by the accumulation of misfolded proteins and widespread disruptions in brain function. Computational modeling has advanced our understanding of these processes, but efforts have traditionally focused on either neuronal dynamics or the biological processes underlying disease. One class of models uses neural mass and whole-brain frameworks to simulate changes in oscillations, connectivity, and network stability. A second class focuses on biological processes underlying disease progression, particularly prion-like propagation through the connectome, glial responses and vascular mechanisms. Each modeling tradition has provided important insights, but experimental evidence shows these processes are interconnected: neuronal activity modulates protein release and clearance, while pathological burden disrupts neuronal function. Modeling these domains in isolation limits our understanding, although recent studies have begun to bridge the two by coupling neuronal and pathological processes. To determine where and why disease emerges, how it spreads, and how it might be altered, mathematical models that capture feedback between neuronal dynamics and disease biology are needed. This review surveys the two modeling approaches and highlights efforts to unify them, emphasizing that linking neuronal activity and disease progression is key to identifying strategies that slow, halt, or reverse degeneration and restore neural function.