Effective connectivity analysis of brain networks of mathematically gifted adolescents using transfer entropy

被引:0
作者
Zhu, Yakun [1 ]
Gong, Weiqiang [2 ]
Lu, Xuesong [3 ]
Wang, Haixian [1 ]
机构
[1] Southeast Univ, Key Lab Child Dev & Learning Sci, Sch Biol Sci & Med Engn, Minist Educ, Nanjing, Jiangsu, Peoples R China
[2] Nanjing Informat Syst Technol Co Ltd, Nanjing, Jiangsu, Peoples R China
[3] Southeast Univ, Dept Rehabil, Zhongda Hosp, Nanjing, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
Effective connectivity; EEG; mathematically gifted adolescents; information flow; graph theory; GRAPH-THEORETICAL ANALYSIS; DIRECTED TRANSFER-FUNCTION; NEUROSCIENCE;
D O I
10.3233/JIFS-223819
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Using functional neuroimaging, electrophysiological techniques and neural data processing techniques, neuroscientists have found that mathematically gifted adolescents exhibit unusual neurocognitive features in the activation of task-related brain regions. Hemispheric information interaction, functional reorganization of networks, and utilization of task-related brain regions are beneficial to rapid and efficient task processing. Based on Granger causality channel selection, the transfer entropy (TE) value between effective channels was computed, and the information flow patterns in the directed functional brain networks derived from electroencephalography (EEG) data during deductive reasoning tasks were explored. We evaluated the workspace configuration patterns of the brain network and the global integration characteristics of separated brain regions using node strength, motif, directed clustering coefficient and characteristic path length in the brain networks of mathematically gifted adolescents with effective connectivity. The empirical results demonstrated that a more integrated functional network at the global level and a more efficient clique at the local level support a pattern of workspace configuration in the mathematically gifted brain that is more conducive to task-related information processing.
引用
收藏
页码:9615 / 9626
页数:12
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