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发表刊物：Advances in Cognitive Neurodynamics (IV)

关键字：Graph theory • Independent component analysis • MEG • Intelligence • Network efficiency

摘要：Graph theory is recently becoming a popular method to study brain functional networks and evaluate efficiency of brain function. However, only a few studies have focused on children. One of the main reasons is that children’s data are usually contaminated by artifacts. We propose to construct brain graphs after applying independent component analysis (ICA) to remove artifacts in the magnetoencephalography (MEG) data of children. The clustering coefficient and the harmonic average path length are calculated from artifact-free MEG data. Analysis showed that certain values of the clustering coefficient and harmonic average path length denoted more intelligent performance, as assessed by the Kaufman Assessment Battery for Children, which may indicate better network efficiency.

论文类型：会议论文

是否译文：否

发表时间：2013-09-04

DOI码：10.1007/978-94-017-9548-7

合写作者：Hiroyuki Tsubomi,Yuko Yoshimura,Mitsuru Kikuchi,Yoshio Minabe,Kastumi Watanabe,Kazuyuki Aihara

通讯作者：段放