Fusing concurrent EEG and fMRI intrinsic networks

David Bridwell, Vince Calhoun

Research output: Chapter in Book/Report/Conference proceedingChapter

Abstract

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.

Original languageEnglish (US)
Title of host publicationMagnetoencephalography
Subtitle of host publicationFrom Signals to Dynamic Cortical Networks
PublisherSpringer-Verlag Berlin Heidelberg
Pages213-235
Number of pages23
Volume9783642330452
ISBN (Electronic)9783642330452
ISBN (Print)3642330444, 9783642330445
DOIs
StatePublished - Jul 1 2014
Externally publishedYes

Keywords

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

ASJC Scopus subject areas

  • Engineering(all)

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  • Cite this

    Bridwell, D., & Calhoun, V. (2014). Fusing concurrent EEG and fMRI intrinsic networks. In Magnetoencephalography: From Signals to Dynamic Cortical Networks (Vol. 9783642330452, pp. 213-235). Springer-Verlag Berlin Heidelberg. https://doi.org/10.1007/978-3-642-33045-2_9