Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimers disease
Graph theoretical analysis of magnetoencephalographic functional connectivity in Alzheimers disease
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DOI：
10.1093/brain/awn262
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发表时间：
2009-01-01
期刊：
影响因子：
14.5
通讯作者：
Scheltens, P.
中科院分区：
文献类型：
--
作者：
Stam, C. J.;de Haan, W.;Scheltens, P.
在这项研究中，我们使用图论中的概念，研究了阿尔茨海默病患者与非痴呆对照组患者静息状态脑网络结构的变化。对18例阿尔茨海默病患者和18例非痴呆对照组在无任务、闭眼状态下进行脑磁图(MEG)检查。对于主要频段，使用相位滞后指数(PLI，一种对体积传导不敏感的同步测量)来评估所有对脑磁图通道之间的同步。计算了PLI加权的连通性网络，并用平均聚类系数和路径长度来表征。阿尔茨海默病患者表现出较低α和β频段的平均PLI降低。在低α波段，阿尔茨海默病患者的聚集系数和路径长度均减小。与随机故障模型相比，目标攻击模型更好地解释了较低阿尔法频段的网络变化。因此，阿尔茨海默病患者在较低的α和β频段表现出静息状态功能连接的丧失，即使使用了对体积传导效应不敏感的测量。此外，阿尔茨海默病患者下α带功能网络的大规模结构更具随机性。建模结果表明，高度连接的神经网络中枢可能特别容易患上阿尔茨海默氏症。
In this study we examined changes in the large-scale structure of resting-state brain networks in patients with Alzheimers disease compared with non-demented controls, using concepts from graph theory. Magneto-encephalograms (MEG) were recorded in 18 Alzheimers disease patients and 18 non-demented control subjects in a no-task, eyes-closed condition. For the main frequency bands, synchronization between all pairs of MEG channels was assessed using a phase lag index (PLI, a synchronization measure insensitive to volume conduction). PLI-weighted connectivity networks were calculated, and characterized by a mean clustering coefficient and path length. Alzheimers disease patients showed a decrease of mean PLI in the lower alpha and beta band. In the lower alpha band, the clustering coefficient and path length were both decreased in Alzheimers disease patients. Network changes in the lower alpha band were better explained by a Targeted Attack model than by a Random Failure model. Thus, Alzheimers disease patients display a loss of resting-state functional connectivity in lower alpha and beta bands even when a measure insensitive to volume conduction effects is used. Moreover, the large-scale structure of lower alpha band functional networks in Alzheimers disease is more random. The modelling results suggest that highly connected neural network hubs may be especially at risk in Alzheimers disease.