Identification of informative subgraphs in brain networks

被引：0
|
作者：
Marinazzo, D. [1 ]
Wu, G. [1 ,2 ]
Pellicoro, M. [3 ]
Stramaglia, S. [3 ]
机构：
[1] Univ Ghent, Fac Psychol & Pedag Sci, Dept Data Anal, Ghent, Belgium
[2] Univ Elect Sci & Technol China, Sch Life Sci & Technol, Key Lab NeuroInformat Minist Educ, Chengdu, Peoples R China
[3] Univ Studi Bari, INFN Bari, Dipartimento Fis, Bari, Italy
来源：
PHYSICS, COMPUTATION, AND THE MIND - ADVANCES AND CHALLENGES AT INTERFACES | 2013年 / 1510卷
关键词：
mutual Information; transfer entropy; Granger causality; CAUSALITY; HUMANS; MOTIFS;
D O I：
10.1063/1.4776503
中图分类号：
TP39 [计算机的应用];
学科分类号：
081203 ; 0835 ;
摘要：
Measuring directed interactions in the brain in terms of information flow is a promising approach, mathematically treatable and amenable to encompass several methods. Here we present a formal expansion of the transfer entropy to put in evidence irreducible sets of variables which provide information for the future state of each assigned target. Multiplets characterized by a large contribution to the expansion are associated to informational circuits present in the system, with an informational character (synergetic or redundant) which can be inferred from the sign of the contribution.
引用
收藏
页码：74 / 84
页数：11
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