加权功能脑网络的代理辅助分析
Surrogate-assisted analysis of weighted functional brain networks
摘要：
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产生了巨大影响。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且有意义的表征是一个重大挑战。针对这个问题，我们提出了一种分析方法，加权网络，利用代理网络保留边的权重或顶点强度。我们首先研究加权网络的特性是否受到边权重或顶点强度的平凡属性的影响（例如，标准偏差）。如果是这样，则这些影响随后通过相应网络特性的适当替代标准化而被有效地分离。我们证明了这种方法，通过重新检查，在时间分辨的方式，加权功能脑网络的癫痫患者和控制对象来自同时EEG/MEG记录在不同的行为状态。我们表明，这种代理辅助分析方法揭示了这些网络的互补信息，可以帮助他们的解释，从而可以防止得出不适当的结论。
英文摘要：
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.
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