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 underlying biological mechanisms of 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, and 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 feeds back to disrupt circuit function. Modeling these domains in isolation limits our understanding. To determine where and why disease emerges, how it spreads, and how it might be altered, we must develop integrated frameworks that capture feedback between neuronal dynamics and disease biology. In this review, we survey the two modeling approaches and highlight efforts to unify them. We argue that such integration is necessary to address key questions in neurodegeneration and to inform interventions, from targeted stimulation to control-theoretic strategies that slow progression and restore function.
Alexandersen et al. (Fri,) studied this question.