Surrogate-assisted analysis of weighted functional brain networks
Surrogate-assisted analysis of weighted functional brain networks
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DOI：
10.1016/j.jneumeth.2012.05.008
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发表时间：
2012-07-15
影响因子：
3
通讯作者：
Lehnertz, Klaus
中科院分区：
文献类型：
--
作者：
Ansmann, Gerrit;Lehnertz, Klaus
复杂脑网络的图论分析是一个快速发展的领域，对神经科学和相关临床研究具有强大的影响。然而，由于许多令人困惑的变量，对特定功能的大脑网络进行可靠和有意义的表征是一个重大挑战。针对这一问题，我们提出了一种加权网络的分析方法，该方法利用保留边权重或顶点强度的代理网络。我们首先研究加权网络的特征是否受边权重或顶点强度(例如，它们的标准差)的微不足道的性质的影响。如果是，则利用相应网络特性的适当代理归一化来有效地分离这些影响。我们通过以时间分辨的方式重新检查癫痫患者和对照受试者在不同行为状态下的同时EEG/MEG记录得到的加权功能脑网络来展示这种方法。我们表明，这种替代辅助分析方法揭示了关于这些网络的补充信息，可以帮助解释它们，从而可以防止得出不适当的结论。(C)2012爱思唯尔B.V.保留所有权利。
Graph-theoretical analyses of complex brain networks is a rapidly evolving field with a strong impact for neuroscientific and related clinical research. Due to a number of confounding variables, however, a reliable and meaningful characterization of particularly functional brain networks is a major challenge. Addressing this problem, we present an analysis approach for weighted networks that makes use of surrogate networks with preserved edge weights or vertex strengths. We first investigate whether characteristics of weighted networks are influenced by trivial properties of the edge weights or vertex strengths (e.g., their standard deviations). If so, these influences are then effectively segregated with an appropriate surrogate normalization of the respective network characteristic. We demonstrate this approach by re-examining, in a time-resolved manner, weighted functional brain networks of epilepsy patients and control subjects derived from simultaneous EEG/MEG recordings during different behavioral states. We show that this surrogate-assisted analysis approach reveals complementary information about these networks, can aid with their interpretation, and thus can prevent deriving inappropriate conclusions. (C) 2012 Elsevier B.V. All rights reserved.