Memory and Aging Center, Department of Neurology, University of California, San Francisco, San Francisco, CA 94143, USA.
Neuron. 2012 Mar 22;73(6):1216-27. doi: 10.1016/j.neuron.2012.03.004. Epub 2012 Mar 21.
Neurodegenerative diseases target large-scale neural networks. Four competing mechanistic hypotheses have been proposed to explain network-based disease patterning: nodal stress, transneuronal spread, trophic failure, and shared vulnerability. Here, we used task-free fMRI to derive the healthy intrinsic connectivity patterns seeded by brain regions vulnerable to any of five distinct neurodegenerative diseases. These data enabled us to investigate how intrinsic connectivity in health predicts region-by-region vulnerability to disease. For each illness, specific regions emerged as critical network "epicenters" whose normal connectivity profiles most resembled the disease-associated atrophy pattern. Graph theoretical analyses in healthy subjects revealed that regions with higher total connectional flow and, more consistently, shorter functional paths to the epicenters, showed greater disease-related vulnerability. These findings best fit a transneuronal spread model of network-based vulnerability. Molecular pathological approaches may help clarify what makes each epicenter vulnerable to its targeting disease and how toxic protein species travel between networked brain structures.
神经退行性疾病靶向大规模神经网络。有四个相互竞争的机制假说被提出来解释基于网络的疾病模式：节点应激、神经元间传播、营养失败和共享脆弱性。在这里，我们使用无任务 fMRI 来推导出易受五种不同神经退行性疾病影响的脑区的健康内在连接模式。这些数据使我们能够研究健康内在连接如何预测疾病的区域易感性。对于每种疾病，特定的区域都成为关键的网络“震中”，其正常连接模式最类似于与疾病相关的萎缩模式。在健康受试者中的图论分析表明，具有更高总连接流的区域，更一致地具有到震中的更短功能路径，表现出更大的疾病相关性脆弱性。这些发现最符合基于网络的脆弱性的神经元间传播模型。分子病理方法可能有助于阐明是什么使每个震中易受其靶向疾病的影响，以及毒性蛋白如何在联网的大脑结构之间传播。
