Portnova Galina V, Tetereva Alina, Balaev Vladislav, Atanov Mikhail, Skiteva Lyudmila, Ushakov Vadim, Ivanitsky Alexey, Martynova Olga
Institute of Higher Nervous Activity and Neurophysiology, Russian Academy of Sciences, Moscow, Russia.
Federal State Budgetary Educational Institution of Higher Education, Pushkin State Russian Language Institute, Moscow, Russia.
Front Hum Neurosci. 2018 Jan 9;11:654. doi: 10.3389/fnhum.2017.00654. eCollection 2017.
Concurrent EEG and fMRI acquisitions in resting state showed a correlation between EEG power in various bands and spontaneous BOLD fluctuations. However, there is a lack of data on how changes in the complexity of brain dynamics derived from EEG reflect variations in the BOLD signal. The purpose of our study was to correlate both spectral patterns, as linear features of EEG rhythms, and nonlinear EEG dynamic complexity with neuronal activity obtained by fMRI. We examined the relationships between EEG patterns and brain activation obtained by simultaneous EEG-fMRI during the resting state condition in 25 healthy right-handed adult volunteers. Using EEG-derived regressors, we demonstrated a substantial correlation of BOLD signal changes with linear and nonlinear features of EEG. We found the most significant positive correlation of fMRI signal with delta spectral power. Beta and alpha spectral features had no reliable effect on BOLD fluctuation. However, dynamic changes of alpha peak frequency exhibited a significant association with BOLD signal increase in right-hemisphere areas. Additionally, EEG dynamic complexity as measured by the HFD of the 2-20 Hz EEG frequency range significantly correlated with the activation of cortical and subcortical limbic system areas. Our results indicate that both spectral features of EEG frequency bands and nonlinear dynamic properties of spontaneous EEG are strongly associated with fluctuations of the BOLD signal during the resting state condition.
静息状态下同步进行脑电图（EEG）和功能磁共振成像（fMRI）采集显示，不同频段的EEG功率与自发血氧水平依赖（BOLD）波动之间存在相关性。然而，关于源自EEG的脑动力学复杂性变化如何反映BOLD信号变化的数据却很缺乏。我们研究的目的是将作为EEG节律线性特征的频谱模式以及非线性EEG动态复杂性与通过fMRI获得的神经元活动相关联。我们在25名健康右利手成年志愿者的静息状态下，检查了同步EEG - fMRI期间EEG模式与脑激活之间的关系。使用源自EEG的回归变量，我们证明了BOLD信号变化与EEG的线性和非线性特征之间存在显著相关性。我们发现fMRI信号与δ频谱功率具有最显著的正相关。β和α频谱特征对BOLD波动没有可靠影响。然而，α峰值频率的动态变化与右半球区域的BOLD信号增加表现出显著关联。此外，通过2 - 20Hz EEG频率范围的高频分解（HFD）测量的EEG动态复杂性与皮质和皮质下边缘系统区域的激活显著相关。我们的结果表明，EEG频段的频谱特征和自发EEG的非线性动态特性在静息状态下都与BOLD信号的波动密切相关。
