Paper Publications
Release time: 2026-10-01Hits:
- Journal:Advances in Cognitive Neurodynamics (IV)
- Key Words:Graph theory • Independent component analysis • MEG • Intelligence • Network efficiency
- Abstract: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.
- Indexed by:会议论文
- Translation or Not:no
- Date of Publication:2013-09-04
- DOI number:10.1007/978-94-017-9548-7
- Co-author:Hiroyuki Tsubomi,Yuko Yoshimura,Mitsuru Kikuchi,Yoshio Minabe,Kastumi Watanabe,Kazuyuki Aihara
- Correspondence Author:Duan, Fang
MOBILE Version