Institute of Computer Science AS CR, Pod vodárenskou věží 2, 18207 Prague 8, Czech Republic.
Chaos. 2011 Mar;21(1):013119. doi: 10.1063/1.3553181.
In recent years, there has been an increasing interest in the study of large-scale brain activity interaction structure from the perspective of complex networks, based on functional magnetic resonance imaging (fMRI) measurements. To assess the strength of interaction (functional connectivity, FC) between two brain regions, the linear (Pearson) correlation coefficient of the respective time series is most commonly used. Since a potential use of nonlinear FC measures has recently been discussed in this and other fields, the question arises whether particular nonlinear FC measures would be more informative for the graph analysis than linear ones. We present a comparison of network analysis results obtained from the brain connectivity graphs capturing either full (both linear and nonlinear) or only linear connectivity using 24 sessions of human resting-state fMRI. For each session, a matrix of full connectivity between 90 anatomical parcel time series is computed using mutual information. For comparison, connectivity matrices obtained for multivariate linear Gaussian surrogate data that preserve the correlations, but remove any nonlinearity are generated. Binarizing these matrices using multiple thresholds, we generate graphs corresponding to linear and full nonlinear interaction structures. The effect of neglecting nonlinearity is then assessed by comparing the values of a range of graph-theoretical measures evaluated for both types of graphs. Statistical comparisons suggest a potential effect of nonlinearity on the local measures-clustering coefficient and betweenness centrality. Nevertheless, subsequent quantitative comparison shows that the nonlinearity effect is practically negligible when compared to the intersubject variability of the graph measures. Further, on the group-average graph level, the nonlinearity effect is unnoticeable.
近年来，基于功能磁共振成像（fMRI）测量，从复杂网络的角度研究大规模大脑活动的相互作用结构引起了越来越多的关注。为了评估两个脑区之间的相互作用强度（功能连接，FC），最常用的是各自时间序列的线性（皮尔逊）相关系数。由于最近在这个和其他领域讨论了非线性 FC 测量的潜在用途，因此出现了一个问题，即特定的非线性 FC 测量是否比线性测量更能为图分析提供信息。我们比较了使用 24 个人类静息态 fMRI 会话捕获全连接（线性和非线性）或仅线性连接的脑连接图的网络分析结果。对于每个会话，使用互信息计算 90 个解剖区时间序列之间的全连接矩阵。为了进行比较，生成了保留相关性但去除任何非线性的多元线性高斯替代数据的连接矩阵。使用多个阈值对这些矩阵进行二值化，我们生成了对应于线性和全非线性相互作用结构的图。然后通过比较两种类型的图评估的一系列图论度量的值来评估忽略非线性的影响。统计比较表明，局部度量-聚类系数和中间中心性可能受到非线性的影响。然而，随后的定量比较表明，与图度量的个体间变异性相比，非线性效应实际上可以忽略不计。此外，在组平均图水平上，非线性效应是不可察觉的。
