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The Default Mode Network and EEG Regional Spectral Power: A Simultaneous fMRI-EEG Study

机译:默认模式网络和EEG区域频谱功率:同步fMRI-EEG研究

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摘要

Electroencephalography (EEG) frequencies have been linked to specific functions as an “electrophysiological signature” of a function. A combination of oscillatory rhythms has also been described for specific functions, with or without predominance of one specific frequency-band. In a simultaneous fMRI-EEG study at 3 T we studied the relationship between the default mode network (DMN) and the power of EEG frequency bands. As a methodological approach, we applied Multivariate Exploratory Linear Optimized Decomposition into Independent Components (MELODIC) and dual regression analysis for fMRI resting state data. EEG power for the alpha, beta, delta and theta-bands were extracted from the structures forming the DMN in a region-of-interest approach by applying Low Resolution Electromagnetic Tomography (LORETA). A strong link between the spontaneous BOLD response of the left parahippocampal gyrus and the delta-band extracted from the anterior cingulate cortex was found. A positive correlation between the beta-1 frequency power extracted from the posterior cingulate cortex (PCC) and the spontaneous BOLD response of the right supplementary motor cortex was also established. The beta-2 frequency power extracted from the PCC and the precuneus showed a positive correlation with the BOLD response of the right frontal cortex. Our results support the notion of beta-band activity governing the “status quo” in cognitive and motor setup. The highly significant correlation found between the delta power within the DMN and the parahippocampal gyrus is in line with the association of delta frequencies with memory processes. We assumed “ongoing activity” during “resting state” in bringing events from the past to the mind, in which the parahippocampal gyrus is a relevant structure. Our data demonstrate that spontaneous BOLD fluctuations within the DMN are associated with different EEG-bands and strengthen the conclusion that this network is characterized by a specific electrophysiological signature created by combination of different brain rhythms subserving different putative functions.
机译:脑电图(EEG)频率已与特定功能相关联,作为功能的“电生理特征”。还已经描述了针对特定功能的振荡节奏的组合,具有或不具有一个特定频带的优势。在3 T下同时进行的fMRI-EEG研究中,我们研究了默认模式网络(DMN)与EEG频段功率之间的关系。作为一种方法学方法,我们将多元探索性线性优化分解应用于独立成分(MELODIC)和fMRI静止状态数据的双重回归分析。通过应用低分辨率电磁层析成像(LORETA),在感兴趣区域的方法中,从形成DMN的结构中提取了α,β,δ和θ波段的EEG功率。发现左海马旁回的自发大胆反应和从前扣带回皮层提取的三角带之间有很强的联系。还建立了从后扣带回皮质(PCC)提取的β-1频率功率与右辅助运动皮层的自发BOLD反应之间的正相关关系。从PCC和早突中提取的β-2频率功率与右额叶皮层的BOLD响应呈正相关。我们的研究结果支持控制认​​知和运动设置中“状态”的β波段活动的概念。在DMN和海马旁回的三角洲功率之间发现的高度相关性与三角洲频率与记忆过程的关联一致。我们假设“静息状态”期间的“持续活动”将过去的事件带入大脑,其中海马旁回是相关的结构。我们的数据表明,DMN内的自发BOLD波动与不同的EEG频段相关,并加强了这一网络的特征是该网络具有特定的电生理学特征,该特征是由不同的脑节律组合而成的,这些脑节律具有不同的推定功能。

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