de Munck J C, Gonçalves S I, Mammoliti R, Heethaar R M, Lopes da Silva F H
Brain Imaging Section-Department of Physics and Medical Technology, VU University Medical Centre, De Boelelaan 1117, 1081 HV Amsterdam, The Netherlands.
Neuroimage. 2009 Aug 1;47(1):69-76. doi: 10.1016/j.neuroimage.2009.04.029. Epub 2009 Apr 17.
In EEG/fMRI correlation studies it is common to consider the fMRI BOLD as filtered version of the EEG alpha power. Here the question is addressed whether other EEG frequency components may affect the correlation between alpha and BOLD. This was done comparing the statistical parametric maps (SPMs) of three different filter models wherein either the free or the standard hemodynamic response functions (HRF) were used in combination with the full spectral bandwidth of the EEG. EEG and fMRI were co-registered in a 30 min resting state condition in 15 healthy young subjects. Power variations in the delta, theta, alpha, beta and gamma bands were extracted from the EEG and used as regressors in a general linear model. Statistical parametric maps (SPMs) were computed using three different filter models, wherein either the free or the standard hemodynamic response functions (HRF) were used in combination with the full spectral bandwidth of the EEG. Results show that the SPMs of different EEG frequency bands, when significant, are very similar to that of the alpha rhythm. This is true in particular for the beta band, despite the fact that the alpha harmonics were discarded. It is shown that inclusion of EEG frequency bands as confounder in the fMRI-alpha correlation model has a large effect on the resulting SPM, in particular when for each frequency band the HRF is extracted from the data. We conclude that power fluctuations of different EEG frequency bands are mutually highly correlated, and that a multi frequency model is required to extract the SPM of the frequency of interest from EEG/fMRI data. When no constraints are put on the shapes of the HRFs of the nuisance frequencies, the correlation model looses so much statistical power that no correlations can be detected.
在脑电图/功能磁共振成像相关性研究中，通常将功能磁共振成像的血氧水平依赖信号（BOLD）视为脑电图α波功率的滤波版本。本文探讨了其他脑电图频率成分是否可能影响α波与BOLD之间的相关性。通过比较三种不同滤波模型的统计参数图（SPM）来进行研究，其中自由或标准的血流动力学响应函数（HRF）与脑电图的全频谱带宽相结合。在15名健康年轻受试者处于30分钟静息状态时，对脑电图和功能磁共振成像进行了配准。从脑电图中提取δ、θ、α、β和γ频段的功率变化，并将其用作一般线性模型中的回归变量。使用三种不同的滤波模型计算统计参数图（SPM），其中自由或标准的血流动力学响应函数（HRF）与脑电图的全频谱带宽相结合。结果表明，不同脑电图频段的SPM在显著时与α节律的SPM非常相似。特别是β频段，尽管α谐波已被舍弃，但情况依然如此。结果表明，在功能磁共振成像-α相关性模型中纳入脑电图频段作为混杂因素会对所得的SPM产生很大影响，特别是当从数据中提取每个频段的HRF时。我们得出结论，不同脑电图频段的功率波动相互高度相关，并且需要一个多频率模型来从脑电图/功能磁共振成像数据中提取感兴趣频率的SPM。当对干扰频率的HRF形状不设限制时，相关性模型会损失太多统计功效，以至于无法检测到相关性。
