Sleep Medicine and Psychophysiology (수면정신생리)

KOREAN ACADEMY OF SLEEP MEDICINE (대한수면의학회)
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The Feasibility for Whole-Night Sleep Brain Network Research Using Synchronous EEG-fMRI

수면 뇌파-기능자기공명영상 동기화 측정과 신호처리 기법을 통한 수면 단계별 뇌연결망 연구

  • Kim, Joong Il (Future Medicine Division, Korea Institute of Oriental Medicine) ;
  • Park, Bumhee (Department of Biomedical Informatics, Ajou University School of Medicine) ;
  • Youn, Tak (Department of Psychiatry, Dongguk University Ilsan Hospital) ;
  • Park, Hae-Jeong (BK21 PLUS Project for Medical Science, Yonsei University College of Medicine)
  • 김중일 (한국한의학연구원 미래의학부) ;
  • 박범희 (아주대학교 의과대학 의료정보학과) ;
  • 윤탁 (동국대학교 의과대학 일산병원 정신건강의학과) ;
  • 박해정 (연세대학교 의과대학 BK21 PLUS 의과학과)
  • Received : 2018.11.21
  • Accepted : 2018.12.10
  • Published : 2018.12.31
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Abstract

Objectives: Synchronous electroencephalogram (EEG) and functional magnetic resonance imaging (fMRI) has been used to explore sleep stage dependent functional brain networks. Despite a growing number of sleep studies using EEG-fMRI, few studies have conducted network analysis on whole night sleep due to difficulty in data acquisition, artifacts, and sleep management within the MRI scanner. Methods: In order to perform network analysis for whole night sleep, we proposed experimental procedures and data processing techniques for EEG-fMRI. We acquired 6-7 hours of EEG-fMRI data per participant and conducted signal processing to reduce artifacts in both EEG and fMRI. We then generated a functional brain atlas with 68 brain regions using independent component analysis of sleep fMRI data. Using this functional atlas, we constructed sleep level dependent functional brain networks. Results: When we evaluated functional connectivity distribution, sleep showed significantly reduced functional connectivity for the whole brain compared to that during wakefulness. REM sleep showed statistically different connectivity patterns compared to non-REM sleep in sleep-related subcortical brain circuits. Conclusion: This study suggests the feasibility of exploring functional brain networks using sleep EEG-fMRI for whole night sleep via appropriate experimental procedures and signal processing techniques for fMRI and EEG.

목 적 : 본 연구는 전 수면 주기 동안 수면단계에 따른 전체 뇌 영역과 수면 관련 뇌 영역들의 뇌기능 연결망의 변화를 살펴보기 위해 동기화된 뇌파(EEG)-자기기능공명영상(fMRI)를 전 수면 주기 동안 측정하고 신호처리 기법을 사용함으로 수면 단계에 따른 뇌 연결망의 탐구가 가능함을 살펴 보기 위해 수행되었다. 방 법 : 정상 성인 피험자 5인을 대상으로 6~7시간의 수면동안 MRI 기계 안에서 안전도, 심전도, 근전도와 EEG-fMRI를 측정하였고 EEG에 발생한 MRI 자장 변화 잡음과 심박관련 잡음을 제거하였다. fMRI에서는 피험자의 움직임에 의해 발생하는 영상 왜곡을 보정하는 부분볼륨활용기법을 제안하여 사용하였다. 잡음이 제거된 수면중 fMRI에 독립성분분석기법을 적용하여 뇌 전체를 68 영역으로 구획하여 수면 연구에 적합한 뇌 구획 지도를 만들고 이를 바탕으로 각 구획들간의 연결성을 계산하였다. 수면관련 뇌심부 영역을 선택하여 연결망 분석을 수행하였다. 결 과 : 뇌파를 비롯한 수면 생리적 신호들은 잡음 제거의 방법을 이용하게 되면 수면단계설정에 문제가 없으며 수면 단계별 뇌 연결망 연구가 가능함을 보여 주었다. 뇌연결망 분석에서 수면 관련 뇌심부 연결망은 렘과 비렘수면에 따라 다른 특성이 나타나는데 비렘수면에서 전반적으로 높은 연결성을 보였다. 대뇌를 포함한 전체 뇌 연결망의 경우 각성에 비해서 수면 중에 뇌 연결성이 떨어지는 양상을 보였다(Kolmogorov-Smirnov 검정 ; p < 0.05, Bonferroni corrected). 결 론 : 본 연구를 통해서 장시간 수면 EEG-fMRI 측정과 수면단계설정이 가능하고 신호처리 기법을 통해서 보정하게 되면 뇌기능 연결망을 이용한 전체 수면 뇌 연구가 가능함을 시사한다.

Keywords

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Figure 1. The EEG preprocessing steps for EEG-fMRI data.

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Figure 2. Two examples of hypnogram for EEG-fMRI sleep study.

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Figure 3. Sleep stage dependent connectivity matrices.

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Figure 4. Connectivity difference between Wake and Sleep and between REM and NREM among sleep related subcortical regions.

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