REDUNDANCY IN FUNCTIONAL BRAIN CONNECTIVITY FROM EEG RECORDINGS
REDUNDANCY IN FUNCTIONAL BRAIN CONNECTIVITY FROM EEG RECORDINGS
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DOI：
10.1142/s0218127412501581
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发表时间：
2012-07-01
影响因子：
2.2
通讯作者：
Babiloni, Fabio
中科院分区：
文献类型：
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作者：
Fallani, Fabrizio De Vico;Toppi, Jlenia;Babiloni, Fabio
冗余的概念是大脑增强对神经损伤和功能障碍的恢复力的关键资源。在目前的工作中，我们提出了一个基于图的方法来研究大脑网络中的连接冗余。通过考虑节点对之间的所有可能路径，我们考虑了三个互补的指标，在全局水平上表征网络冗余（i），即跨不同路径长度的标量冗余（ii），即节点对之间的向量冗余（iii），即矩阵冗余。我们使用这个程序来调查的功能连接估计从一组健康受试者在无任务的休息状态下的高密度EEG信号的数据集。与随机网络的基准数据集的统计比较，具有相同数量的节点和EEG网络的链接，揭示了所有三个指标的显著差异（p < 0.05）。特别是，在EEG网络中的冗余，对于每个频带，出现从根本上高于随机图，从而揭示了大脑的自然趋势，呈现不同的专业领域之间的多个并行的相互作用。值得注意的是，矩阵冗余显示了在EEG振荡的Alpha范围（7.5 - 12.5Hz）中顶枕区域上的头皮传感器之间的高（p < 0.05）冗余，这已知是静息状态条件期间最具响应性的通道。
The concept of redundancy is a critical resource of the brain enhancing the resilience to neural damages and dysfunctions. In the present work, we propose a graph-based methodology to investigate the connectivity redundancy in brain networks. By taking into account all the possible paths between pairs of nodes, we considered three complementary indexes, characterizing the network redundancy (i) at the global level, i.e. the scalar redundancy (ii) across different path lengths, i.e. the vectorial redundancy (iii) between node pairs, i.e. the matricial redundancy. We used this procedure to investigate the functional connectivity estimated from a dataset of high-density EEG signals in a group of healthy subjects during a no-task resting state. The statistical comparison with a benchmark dataset of random networks, having the same number of nodes and links of the EEG nets, revealed a significant (p < 0.05) difference for all the three indexes. In particular, the redundancy in the EEG networks, for each frequency band, appears radically higher than random graphs, thus revealing a natural tendency of the brain to present multiple parallel interactions between different specialized areas. Notably, the matricial redundancy showed a high (p < 0.05) redundancy between the scalp sensors over the parieto-occipital areas in the Alpha range of EEG oscillations (7.5 12.5 Hz), which is known to be the most responsive channel during resting state conditions.