AI 中文总结
该研究将神经元活性耦合到流行病动力学模型，结合多尺度脑网络分析，证实神经元活性是阿尔茨海默病病理扩散的关键驱动因素，其模型可预测疾病进展的空间模式与范围。
AI 中文摘要
神经退行性疾病可被视为脑网络上的传播过程，其中病理蛋白在解剖学连接的脑区之间传播。数学模型已被用于研究这一过程，但它们通常忽略神经元活性的影响，尽管实验研究表明神经元放电会促进蛋白传递。在此，我们将一般的节点活性过程与易感-感染-易感动力学相结合。在该框架中，流行病阈值决定小型病理种子是否能生长，而主导网络模式决定生长的起始位置。我们推导了近似结果，表明神经元活性如何通过混合结构网络模式来改变该阈值并引导传播方向。对于具有多尺度结构的网络，我们将这些变化分解为区域平均活性和区域内活性变异的贡献，从而能够解释脑成像无法解析的活性异质性。随机模拟在合成网络上验证了理论结果。接下来，我们使用纵向人类正电子发射断层扫描数据测试神经元活性是否能预测病理扩散的位置和范围。区域葡萄糖代谢作为神经元活性的替代指标，而tau蛋白积累则用于衡量疾病进展。将神经元活性加入网络模型后，可捕捉到仅靠结构连接和已确立的疾病标志物无法解释的疾病进展空间模式。在个体层面，预测的流行病阈值也与病理在脑内的扩散范围相关。总之，这些结果将流行病理论与神经退行性疾病联系起来，表明神经元活性是阿尔茨海默病进展的驱动因素，并为开发调节活性的疗法以减缓或预防病理传播提供了依据。
英文摘要
Neurodegenerative diseases can be viewed as spreading processes on brain networks, in which pathological proteins propagate between anatomically connected brain regions. Mathematical models have been used to study this process, but they generally ignore the influence of neuronal activity, even though experimental studies show that neuronal firing promotes protein transmission. Here, we couple a general node-activity process to susceptible--infected--susceptible dynamics. In this framework, an epidemic threshold determines whether small pathological seeds can grow, while a dominant network mode determines where growth begins. We derive approximations showing how neuronal activity shifts this threshold and redirects spreading by mixing structural network modes. For networks with multiscale structure, we decompose these changes into contributions from regional mean activity and within-region activity variation, allowing us to account for activity heterogeneity that is not resolved by brain imaging. Stochastic simulations validate the theoretical results across synthetic networks. We next use longitudinal human positron emission tomography to test whether neuronal activity predicts where and how broadly pathology spreads. Regional glucose metabolism serves as a proxy for neuronal activity, while tau accumulation measures disease progression. Adding neuronal activity to the network model captures spatial patterns of disease progression that are not explained by structural connectivity and established disease markers alone. Across individuals, predicted epidemic thresholds are also associated with how broadly pathology spreads through the brain. Together, these results connect epidemic theory to neurodegeneration, implicate neuronal activity as a driver of Alzheimer's disease progression, and motivate activity-modulating therapies to slow or prevent pathological spread.
