Network Models of Neurodegeneration: Bridging Neuronal Dynamics and Disease Progression

Area of Science:

• Computational neuroscience
• Neurodegenerative disease research
• Mathematical modeling in biology

Background:

• Neurodegenerative diseases involve protein misfolding and brain function disruption.
• Existing computational models focus separately on neuronal dynamics or disease biology.
• These processes are interconnected, with neuronal activity influencing pathology and vice versa.

Purpose of the Study:

• To review computational modeling approaches in neurodegenerative diseases.
• To highlight efforts in unifying neuronal and disease process models.
• To emphasize the need for integrated models to understand disease emergence and progression.

Main Methods:

• Survey of neural mass and whole-brain modeling frameworks.
• Analysis of models focusing on prion-like propagation, glial responses, and vascular mechanisms.
• Review of studies coupling neuronal activity with pathological processes.

Main Results:

• Two distinct modeling traditions exist: one for neuronal dynamics, another for disease biology.
• Experimental evidence confirms the interplay between neuronal activity and pathological burden.
• Emerging research begins to bridge these modeling domains by coupling neuronal and pathological processes.

Conclusions:

• Isolated modeling approaches limit understanding of neurodegenerative disease mechanisms.
• Integrated mathematical models capturing feedback between neuronal dynamics and disease biology are essential.
• Linking neuronal activity and disease progression is critical for developing effective therapeutic strategies.