CONSTRUCTING BRAIN FUNCTIONAL NETWORKS FROM EEG: PARTIAL AND UNPARTIAL CORRELATIONS
CONSTRUCTING BRAIN FUNCTIONAL NETWORKS FROM EEG: PARTIAL AND UNPARTIAL CORRELATIONS
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DOI：
10.1142/s0219635211002725
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发表时间：
2011-06-01
影响因子：
1.8
通讯作者：
Knyazeva, Maria G.
中科院分区：
文献类型：
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作者：
Jalili, Mahdi;Knyazeva, Maria G.
我们考虑健康个体的脑电图（EEG），并比较通过两种方法发现的大脑功能网络的特性：非偏相关和偏相关。通过偏相关获得的网络在图度量方面与通过非偏相关构建的网络有着根本的不同。特别是，它们具有完全不同的连接效率、聚类系数、相配性、程度变异性和同步特性。非偏相关计算简单，可以轻松应用于大规模系统，但不能阻止非直接边缘的预测。相反，偏相关的计算成本通常很高，会减少对此类边缘的预测。我们建议结合这些替代方法，以获得有关大脑功能网络的补充信息。
We consider electroencephalograms (EEGs) of healthy individuals and compare the properties of the brain functional networks found through two methods: unpartialized and partialized cross-correlations. The networks obtained by partial correlations are fundamentally different from those constructed through unpartial correlations in terms of graph metrics. In particular, they have completely different connection efficiency, clustering coefficient, assortativity, degree variability, and synchronization properties. Unpartial correlations are simple to compute and they can be easily applied to large-scale systems, yet they cannot prevent the prediction of non-direct edges. In contrast, partial correlations, which are often expensive to compute, reduce predicting such edges. We suggest combining these alternative methods in order to have complementary information on brain functional networks.