응용통계연구 (The Korean Journal of Applied Statistics)

  • 제36권5호
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  • Pages.361-380
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  • 2023
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  • 1225-066X(pISSN)
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  • 2383-5818(eISSN)
한국통계학회 (The Korean Statistical Society)
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MEG 복잡계 네트워크 분석에 대한 통계적 고찰

Review of complex network analysis for MEG

  • 신선한 (덕성여자대학교, 정보통계학과) ;
  • 김재희 (덕성여자대학교, 정보통계학과)
  • Sunhan Shin (Department of Statistics, Duksung Women's University) ;
  • Jaehee Kim (Department of Statistics, Duksung Women's University)
  • 투고 : 2022.12.09
  • 심사 : 2023.01.02
  • 발행 : 2023.10.31
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⟨ 이전 논문 다음 논문 ⟩

초록

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.

키워드

과제정보

이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 연구 기초연구실 (No. 2021R1A4A5028907) 지원과 기본연구 (No. 2021R1F1A1054968) 지원을 받아 수행한 연구 과제입니다.

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