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MEG 복잡계 네트워크 분석에 대한 통계적 고찰
Review of complex network analysis for MEG
Korean J Appl Stat 2023;36(5):361-380
Published online October 31, 2023
© 2023 The Korean Statistical Society.

신선한a, 김재희1,a
Sunhan Shina, Jaehee Kim1,a

a덕성여자대학교, 정보통계학과

aDepartment of Statistics, Duksung Women’s University
1Department of Statistics, Duksung Women’s University, 419 Samyang-ro, 144 Gil 33, Dobong-Gu, Seoul 01369, Korea. E-mail: jaehee@duksung.ac.kr
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (No. 2021R1A4A5028907) and Basic Research (No. 2021R1F1A1054968).
Received December 9, 2022; Revised December 31, 2022; Accepted January 2, 2023.
Abstract
Magnetoencephalography (MEG)는 뉴론 활동에 신경 세포들간 전류 흐름에 의해 유도된 자기장을 측정하는 비침습 뇌영상 기술이다. 기능적 뇌활동은 뇌영역간 또는 뉴런들의 연결로 기능적 연결로 수행된다. MEG 데이터는 상관성, 시공간성을 가지며 다중 다층적 동적 네트워크인 특징을 갖는다. 이러한 복잡성 때문에 MEG 네트워크에 대한 연구는 아직 많지 않은 편이다. 본 연구에서는 MEG 네트워크 모형과 분석법을 소개하고 실제 MEG 데이터 분석에 활용되어 해석된 경우를 요약하고 앞으로 MEG 네트워크 모형 개발 연구의 필요성을 설명하고자 한다. 그러므로 통계적 네트워크 분석이 뇌과학에서 신경학적 질병을 포함하여 뇌기능에 대한 이해에 중요한 역할을 할 수 있음을 알리고자 한다.
Magnetoencephalography (MEG) is a technique to record oscillatory magnetic fields coming from ongoing neuronal activity. Functional brain activities performing cognitive or physiological tasks are performed on structural connections between neurons or brain regions. MEG data can be characterized as highly correlated, spatio-temporal, multidimensional, multilayered dynamic networks. Due to its complex structure, many studies on MEG network have not yet been conducted. In this study, we will explain the concept, necessity, and possible approaches of MEG network analysis. We reviewed the characteristics of MEG data. Network measures and potential network models in MEG and clinical studies are also reviewed.
주요어 : 복잡계 네트워크, MEG, 네트워크 모형, MEG 네트워크 분석
Keywords : complex network, MEG, network model, MEG network analysis
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