The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks
The role of nonlinearity in computing graph-theoretical properties of resting-state functional magnetic resonance imaging brain networks
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DOI：
10.1063/1.3553181
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发表时间：
2011-03-01
期刊：
影响因子：
2.9
通讯作者：
Corbetta, M.
中科院分区：
文献类型：
--
作者：
Hartman, D.;Hlinka, J.;Corbetta, M.
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交互结构的兴趣日益浓厚。为了评估两个大脑区域之间的相互作用（功能连接，FC）的强度，最常用的是各自时间序列的线性（皮尔逊）相关系数。由于最近在该领域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：特定的非线性 FC 测量是否比线性测量更能提供图形分析的信息。我们使用 24 个人类静息态 fMRI 会话，对从捕获完整（线性和非线性）或仅线性连接的大脑连接图获得的网络分析结果进行了比较。对于每个会话，使用互信息计算 90 个解剖地块时间序列之间的完全连接矩阵。为了进行比较，生成了针对多元线性高斯代理数据获得的连通性矩阵，该矩阵保留了相关性，但消除了任何非线性。使用多个阈值对这些矩阵进行二值化，我们生成与线性和完全非线性交互结构相对应的图。然后通过比较针对两种类型图评估的一系列图论测量值来评估忽略非线性的影响。统计比较表明非线性对局部度量聚类系数和介数中心性的潜在影响。然而，随后的定量比较表明，与图表测量的受试者间变异性相比，非线性效应实际上可以忽略不计。此外，在组平均图水平上，非线性效应不明显。 (C) 2011 年美国物理研究所。 [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. (C) 2011 American Institute of Physics. [doi:10.1063/1.3553181]