Prestel Marcel, Steinfath Tim Paul, Tremmel Michael, Stark Rudolf, Ott Ulrich
Bender Institute of Neuroimaging, Justus Liebig University Giessen, Giessen, Germany.
Front Hum Neurosci. 2018 Nov 27;12:478. doi: 10.3389/fnhum.2018.00478. eCollection 2018.
: We aimed to identify electroencephalographic (EEG) signal fluctuations within independent components (ICs) that correlate to spontaneous blood oxygenation level dependent (BOLD) activity in regions of the default mode network (DMN) during eyes-closed resting state. : We analyzed simultaneously acquired EEG and functional magnetic resonance imaging (fMRI) eyes-closed resting state data in a convenience sample of 30 participants. IC analysis (ICA) was used to decompose the EEG time-series and common ICs were identified using data-driven IC clustering across subjects. The IC time courses were filtered into seven frequency bands, convolved with a hemeodynamic response function (HRF) and used to model spontaneous fMRI signal fluctuations across the brain. In parallel, group ICA analysis was used to decompose the fMRI signal into ICs from which the DMN was identified. Frequency and IC cluster associated hemeodynamic correlation maps obtained from the regression analysis were spatially correlated with the DMN. To investigate the reliability of our findings, the analyses were repeated with data collected from the same subjects 1 year later. : Our results indicate a relationship between power fluctuations in the delta, theta, beta and gamma frequency range and the DMN in different EEG ICs in our sample as shown by small to moderate spatial correlations at the first measurement (0.234 < < 0.346, < 0.0001). Furthermore, activity within an EEG component commonly identified as eye movements correlates with BOLD activity within regions of the DMN. In addition, we demonstrate that correlations between EEG ICs and the BOLD signal during rest are in part stable across time. : We show that ICA source separated EEG signals can be used to investigate electrophysiological correlates of the DMN. The relationship between the eye movement component and the DMN points to a behavioral association between DMN activity and the level of eye movement or the presence of neuronal activity in this component. Previous findings of an association between frontal midline theta activity and the DMN were replicated.
我们旨在识别闭眼静息状态下,与默认模式网络(DMN)区域中自发的血氧水平依赖(BOLD)活动相关的独立成分(IC)内的脑电图(EEG)信号波动。我们分析了30名参与者便利样本中同时采集的闭眼静息状态下的EEG和功能磁共振成像(fMRI)数据。独立成分分析(ICA)用于分解EEG时间序列,并通过跨受试者的数据驱动IC聚类识别共同的IC。IC时间进程被过滤到七个频带,与血液动力学响应函数(HRF)进行卷积,并用于模拟全脑自发的fMRI信号波动。同时,使用组ICA分析将fMRI信号分解为IC,从中识别出DMN。从回归分析中获得的频率和IC聚类相关的血液动力学相关图在空间上与DMN相关。为了研究我们结果的可靠性,一年后用从同一受试者收集的数据重复了分析。我们的结果表明,在我们的样本中,不同EEG IC中δ、θ、β和γ频率范围内的功率波动与DMN之间存在关系,首次测量时的空间相关性为小到中等(0.234 < < 0.346,< 0.0001)。此外,通常被识别为眼动的EEG成分内的活动与DMN区域内的BOLD活动相关。此外,我们证明静息期间EEG IC与BOLD信号之间的相关性在一定程度上随时间稳定。我们表明,ICA源分离的EEG信号可用于研究DMN的电生理相关性。眼动成分与DMN之间的关系表明DMN活动与眼动水平或该成分中神经元活动的存在之间存在行为关联。先前关于额中线θ活动与DMN之间关联的发现得到了重复。