Mathematical Institute, University of Oxford, Oxford OX2 6GG, United Kingdom.
Living Matter Laboratory, Stanford University, Stanford, California 94305, USA.
Phys Rev Lett. 2020 Sep 18;125(12):128102. doi: 10.1103/PhysRevLett.125.128102.
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.
神经退行性疾病，如阿尔茨海默病或帕金森病，表现出特征性的结构性脑网络退化。这种退化最终导致网络动力学的变化和认知功能的退化。在这里，我们以耦合的物理过程来模拟进展：有毒蛋白的积累，由非线性反应扩散输运过程给出，产生了一个以加权边为特征的进化脑连接组，在该边上面演化着神经元质量模型。通过模拟进化中的脑网络的静息状态活动，可以测试脑功能的进展。我们表明，虽然边缘权重的演变在疾病的整体进展中作用较小，但动态生物标志物可以预测与严重认知能力下降相关的 10 年过渡期。
