pp 213-235


Fusing Concurrent EEG and fMRI Intrinsic Networks

  • David BridwellAffiliated withMind Research Network Email author 
  • , Vince CalhounAffiliated withElectrical and Computer Engineering, University of New Mexico

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Different imaging modalities are sensitive to different aspects of brain activity, and integrating information from multiple modalities can provide an improved picture of brain dynamics. Electroencephalography (EEG) and functional Magnetic Resonance Imaging (fMRI) are often integrated since they make up for each other’s limitations. FMRI can reveal localized intrinsic networks whose BOLD signals have periods from 100 s to about 10 s. EEG recordings, in contrast, reflect cortical electrical fluctuations with periods up to 20 ms or higher. The following chapter surveys the physiological differences between EEG and fMRI recordings and the implications and results of their integration. EEG-fMRI findings are reviewed in cases where individuals do not participate in an explicit task (e.g. during “rest”). The results are discussed in the context of different methodological approaches to EEG-fMRI integration, including correlation and GLM-based analysis, and ICA decomposition of group EEG-fMRI datasets. The resulting EEG-fMRI networks capture a broader range of brain dynamics compared to EEG or fMRI alone, and can serve as a reference for studies integrating MEG and fMRI.


BOLD fMRI EEG ERP Networks Oscillations Intrinsic connectivity Spatiotemporal dynamics Data fusion Source separation