Neuronal Oscillations on Evolving Networks: Dynamics, Damage, Degradation, Decline, Dementia, and Death

TL;DR

This paper models neurodegenerative disease progression as an evolving brain network influenced by toxic protein accumulation, showing that dynamic biomarkers can predict cognitive decline over a decade.

Contribution

It introduces a coupled physical process model combining reaction-diffusion transport and neuronal-mass dynamics on evolving brain networks, providing insights into disease progression.

Findings

Edge weight evolution has minor impact on disease progression

Dynamic biomarkers can predict cognitive decline over 10 years

Simulation of resting-state activity reflects disease stages

Abstract

Neurodegenerative diseases, such as Alzheimer's or Parkinson's disease, show characteristic degradation of structural brain networks. This degradation eventually leads to changes in the network dynamics and degradation of cognitive functions. Here, we model the progression in terms of coupled physical processes: The accumulation of toxic proteins, given by a nonlinear reaction-diffusion transport process, yields an evolving brain connectome characterized by weighted edges on which a neuronal-mass model evolves. The progression of the brain functions can be tested by simulating the resting-state activity on the evolving brain network. We show that while the evolution of edge weights plays a minor role in the overall progression of the disease, dynamic biomarkers predict a transition over a period of 10 years associated with strong cognitive decline.