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.

中文翻译：

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不断发展的网络上的神经元振荡：动态，损害，退化，衰退，痴呆和死亡

神经退行性疾病，例如阿尔茨海默氏病或​​帕金森氏病，表现出大脑结构网络的特征性退化。这种降级最终导致网络动态变化和认知功能降级。在这里，我们用耦合的物理过程来模拟进展：有毒蛋白质的积累，由非线性反应-扩散运输过程给出，产生了一个以权重边缘为特征的不断发展的脑连接体，神经元质量模型在该连接体上进化。可以通过模拟不断发展的大脑网络上的静止状态活动来测试大脑功能的进展。我们显示，虽然边缘权重的演变在疾病的总体进展中起着较小的作用，但动态生物标记物预测在10年的时间里与强烈的认知能力下降相关的转变。