Xavier Marta, Esteves Inês, Jorge João, Abreu Rodolfo, Giraud Anne-Lise, Sadaghiani Sepideh, Wirsich Jonathan, Figueiredo Patrícia
ISR-Lisboa and Department of Bioengineering, Instituto Superior Técnico, Universidade de Lisboa, Lisbon, Portugal.
CSEM-Swiss Center for Electronics and Microtechnology, Bern, Switzerland.
Imaging Neurosci (Camb). 2025 Jun 18;3. doi: 10.1162/IMAG.a.37. eCollection 2025.
Several simultaneous electroencephalography (EEG)-functional magnetic resonance imaging (fMRI) studies have aimed to identify the relationship between EEG band power and fMRI resting-state networks (RSNs) to elucidate their neurobiological significance. Although common patterns have emerged, inconsistent results have also been reported. This study aims to explore the consistency of these correlations across subjects and to understand how factors such as the hemodynamic response delay and the use of different EEG data spaces (source/scalp) influence them. Using three distinct EEG-fMRI datasets, acquired independently on 1.5T, 3T, and 7T MRI scanners (comprising 42 subjects in total), we evaluate the generalizability of our findings across different acquisition conditions. We found consistent correlations between fMRI RSN and EEG band power time series across subjects in the three datasets studied, with systematic variations with RSN, EEG frequency band, and hemodynamic response function (HRF) delay, but not with EEG space. Several of these correlations were consistent across the three datasets, despite important differences in field strength and resting-state conditions. These included spatially widespread patterns observed across HRF delays from 2 to 10 s, such as positive delta correlations with the visual and somatomotor networks, negative delta correlations with the default mode network, positive theta correlations with the somatomotor network, negative alpha correlations with both the visual and dorsal attention networks, positive alpha correlations with the default mode network, and negative beta correlations with the somatomotor network. Our findings support consistent correlations across specific fMRI RSNs and EEG bands and highlight the importance of methodological considerations in interpreting them that may explain conflicting reports in the existing literature.
多项同步脑电图(EEG)-功能磁共振成像(fMRI)研究旨在确定EEG频段功率与fMRI静息态网络(RSN)之间的关系,以阐明其神经生物学意义。尽管已经出现了一些共同模式,但也有不一致的结果报道。本研究旨在探讨这些相关性在不同受试者之间的一致性,并了解诸如血液动力学反应延迟和使用不同EEG数据空间(源/头皮)等因素如何影响它们。我们使用在1.5T、3T和7T MRI扫描仪上独立采集的三个不同的EEG-fMRI数据集(总共包括42名受试者),评估我们的研究结果在不同采集条件下的普遍性。我们发现在所研究的三个数据集中,不同受试者的fMRI RSN与EEG频段功率时间序列之间存在一致的相关性,且随着RSN、EEG频段和血液动力学反应函数(HRF)延迟存在系统性变化,但与EEG空间无关。尽管场强和静息态条件存在重要差异,但其中一些相关性在三个数据集中是一致的。这些包括在2至10秒的HRF延迟范围内观察到的空间广泛模式,例如与视觉和躯体运动网络呈正δ相关性,与默认模式网络呈负δ相关性,与躯体运动网络呈正θ相关性,与视觉和背侧注意网络呈负α相关性,与默认模式网络呈正α相关性,以及与躯体运动网络呈负β相关性。我们的研究结果支持特定fMRI RSN与EEG频段之间的一致相关性,并强调在解释这些相关性时方法学考虑的重要性,这可能解释了现有文献中相互矛盾的报道。