Functional community analysis of brain: A new approach for EEG-based investigation of the brain pathology

被引：150

作者：

Ahmadlou, Mehran ^{[2
]}

Adeli, Hojjat ^{[1
,3
,4
,5
,6
,7
]}

机构：

[1] Ohio State Univ, Dept Biomed Engn, Columbus, OH 43210 USA

[2] Amirkabir Univ Technol, Dept Biomed Engn, Tehran, Iran

[3] Ohio State Univ, Dept Biomed Informat, Columbus, OH 43210 USA

[4] Ohio State Univ, Dept Civil & Environm Engn & Geodet Sci, Columbus, OH 43210 USA

[5] Ohio State Univ, Dept Elect & Comp Engn, Columbus, OH 43210 USA

[6] Ohio State Univ, Dept Neurol Surg, Columbus, OH 43210 USA

[7] Ohio State Univ, Dept Neurosci, Columbus, OH 43210 USA

来源：

NEUROIMAGE | 2011年 / 58卷 / 02期

关键词：

SMALL-WORLD NETWORKS; GRAPH-THEORETICAL ANALYSIS; WAVELET-CHAOS METHODOLOGY; PHASE SYNCHRONIZATION; ALZHEIMERS-DISEASE; NEURAL-NETWORK; AUTOMATIC IDENTIFICATION; COMPLEX NETWORKS; CONNECTIVITY; SEIZURE;

D O I：

10.1016/j.neuroimage.2011.04.070

中图分类号：

Q189 [神经科学];

学科分类号：

071006 ;

摘要：

Analysis of structure of the brain functional connectivity (SBFC) is a fundamental issue for understanding of the brain cognition as well as the pathology of brain disorders. Analysis of communities among sub-parts of a system is increasingly used for social, ecological, and other networks. This paper presents a new methodology for investigation of the SBFC and understanding of the brain based on graph theory and community pattern analysis of functional connectivity graph of the brain obtained from encephalograms (EEGs). The methodology consists of three main parts: fuzzy synchronization likelihood (FSL), community partitioning, and decisions based on partitions. As an example application, the methodology is applied to analysis of brain of patients with attention deficit/hyperactivity disorder (ADHD) and the problem of discrimination of ADHD EEGs from healthy (non-ADHD) EEGs. (C) 2011 Elsevier Inc. All rights reserved.

引用

页码：401 / 408

页数：8

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