Department of Biomedical Engineering, Amirkabir university of Technology, Tehran, Iran.
Neuroimage. 2011 Sep 15;58(2):401-8. doi: 10.1016/j.neuroimage.2011.04.070. Epub 2011 May 7.
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.
脑功能连接（SBFC）结构分析是理解大脑认知和大脑疾病病理的一个基本问题。系统子部分之间的社区分析越来越多地用于社会、生态和其他网络。本文提出了一种基于图论和从脑电图（EEG）获得的脑功能连接图的社区模式分析来研究 SBFC 和理解大脑的新方法。该方法包括三个主要部分：模糊同步似然（FSL）、社区划分和基于分区的决策。作为一个应用实例，该方法应用于注意力缺陷/多动障碍（ADHD）患者大脑的分析以及区分 ADHD EEG 与健康（非 ADHD）EEG 的问题。
