Statistical network analysis for epilepsy MEG data

IF 0.5 Q4 STATISTICS & PROBABILITY

Communications for Statistical Applications and Methods Pub Date : 2023-11-30 DOI:10.29220/csam.2023.30.6.561

Haeji Lee, Chun Kee Chung, Jaehee Kim

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引用次数: 0

Abstract

Brain network analysis has attracted the interest of neuroscience researchers in studying brain diseases. Mag-netoencephalography (MEG) is especially proper for analyzing functional connectivity due to high temporal and spatial resolution. The application of graph theory for functional connectivity analysis has been studied widely, but research on network modeling for MEG still needs more. Temporal exponential random graph model (TERGM) considers temporal dependencies of networks. We performed the brain network analysis, including static / temporal network statistics, on two groups of epilepsy patients who removed the left (LT) or right (RT) part of the brain and healthy controls. We investigate network di ﬀ erences using Multiset canonical correlation analysis (MCCA) and TERGM between epilepsy patients and healthy controls (HC). The brain network of healthy controls had fewer temporal changes than patient groups. As a result of TERGM, on the simulation networks, LT and RT had less stable state than HC in the network connectivity structure. HC had a stable state of the brain network.

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癫痫 MEG 数据的统计网络分析

脑网络分析引起了神经科学研究人员对脑部疾病研究的兴趣。脑磁图（MEG）具有很高的时间和空间分辨率，尤其适合分析功能连接性。图论在功能连通性分析中的应用已被广泛研究，但针对 MEG 的网络建模研究仍有待加强。时间指数随机图模型（TERGM）考虑了网络的时间依赖性。我们对切除左脑（LT）或右脑（RT）部分的两组癫痫患者和健康对照组进行了脑网络分析，包括静态/时间网络统计。我们使用多集典型相关分析（MCCA）和TERGM研究了癫痫患者和健康对照组（HC）之间的网络差异。与患者组相比，健康对照组大脑网络的时间变化较少。TERGM的结果是，在模拟网络中，LT和RT的网络连接结构不如HC稳定。健康对照组的大脑网络处于稳定状态。

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来源期刊

Communications for Statistical Applications and Methods STATISTICS & PROBABILITY-

CiteScore

0.90

自引率

0.00%

发文量

期刊介绍： Communications for Statistical Applications and Methods (Commun. Stat. Appl. Methods, CSAM) is an official journal of the Korean Statistical Society and Korean International Statistical Society. It is an international and Open Access journal dedicated to publishing peer-reviewed, high quality and innovative statistical research. CSAM publishes articles on applied and methodological research in the areas of statistics and probability. It features rapid publication and broad coverage of statistical applications and methods. It welcomes papers on novel applications of statistical methodology in the areas including medicine (pharmaceutical, biotechnology, medical device), business, management, economics, ecology, education, computing, engineering, operational research, biology, sociology and earth science, but papers from other areas are also considered.