The spatiospectral characterization of brain networks: fusing concurrent EEG spectra and fMRI maps.
The spatiospectral characterization of brain networks: fusing concurrent EEG spectra and fMRI maps.
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大脑网络的时尚表征：融合并发的EEG光谱和fMRI图。
DOI：
10.1016/j.neuroimage.2012.12.024
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发表时间：
2013-04-01
期刊：
影响因子：
5.7
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Bridwell DA;Wu L;Eichele T;Calhoun VD
不同的成像方式捕捉到大脑活动的不同方面。功能磁共振成像(FMRI)揭示了其大胆信号具有从100秒(0.01赫兹)到约10秒(0.1赫兹)的周期的内在网络。相比之下，脑电(EEG)记录通常反映的是周期长达20毫秒(50赫兹)或更高的皮质电波动。我们检查了固有的功能磁共振成像和静态脑电网络活动之间的对应关系，以便在空间上(使用功能磁共振成像)和频谱上(使用脑电)来表征大脑网络。通过组独立分量分析在总体组水平上分别识别同时记录的fMRI和EEG中的脑网络，并通过去卷积它们的分量时间过程来检验空间fMRI与空间脑电源频率之间的关联。如果估计的脉冲响应函数(IRF)在生物学上看似合理的延迟处显著非零，则认为这两种模式是相关的。我们发现，负关联主要存在于五个阿尔法成分中的两个，这突显了在EEG-fMRI中考虑多个阿尔法来源的重要性。正相关主要存在于较低的频谱区域(例如，δ和theta)和较高的频谱区域(例如，较高的β和较低的伽马)，有时也存在于相同的fMRI成分中。总而言之，这些结果展示了一种在空间和光谱上表征大脑网络的有前景的方法，并揭示了在脑电频谱的部分不同区域内出现正和负关联。
Different imaging modalities capture different aspects of brain activity. Functional Magnetic Resonance Imaging (fMRI) reveals intrinsic networks whose BOLD signals have periods from 100s (0.01 Hz) to about 10s (0.1 Hz). Electroencephalographic (EEG) recordings, in contrast, commonly reflect cortical electrical fluctuations with periods up to 20 ms (50 Hz) or above. We examined the correspondence between intrinsic fMRI and EEG network activity at rest in order to characterize brain networks both spatially (with fMRI) and spectrally (with EEG). Brain networks were separately identified within the concurrently recorded fMRI and EEG at the aggregate group level with group independent component analysis and the association between spatial fMRI and frequency by spatial EEG sources was examined by deconvolving their component time courses. The two modalities are considered linked if the estimated impulse response function (IRF) is significantly non-zero at biologically plausible delays. We found that negative associations were primarily present within two of five alpha components, which highlights the importance of considering multiple alpha sources in EEG-fMRI. Positive associations were primarily present within the lower (e.g. delta and theta) and higher (e.g. upper beta and lower gamma) spectral regions, sometimes within the same fMRI components. Collectively, the results demonstrate a promising approach to characterize brain networks spatially and spectrally, and reveal that positive and negative associations appear within partially distinct regions of the EEG spectrum.
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