Abstract
Previous work using simultaneously acquired electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) data has shown that the slow temporal dynamics of resting state brain networks (RSNs), e.g., default mode network (DMN), visual network (VN), obtained from fMRI are correlated with smoothed and down sampled versions of various EEG features such as microstates and band-limited power envelopes. Therefore, even though the down sampled and smoothed envelope of EEG gamma band power is correlated with fMRI fluctuations in the RSNs, it does not mean that the electrical substrates of the RSNs fluctuate with periods <100 ms. Based on the scale free properties of EEG microstates and their correlation with resting state fMRI fluctuations in the RSNs, researchers have speculated that truly high frequency electrical substrates may exist for the RSNs, which would make resting fluctuations obtained from fMRI more meaningful to typically occurring fast neuronal processes in the sub-100 ms time scale. In this study, we test this critical hypothesis using an integrated framework involving simultaneous EEG/fMRI acquisition, fast fMRI sampling (TR = 200 ms) using multiband EPI (MB EPI), and EEG/fMRI fusion using parallel independent component analysis (pICA) which does not require the down sampling of EEG to fMRI temporal resolution. Our results demonstrate that with faster sampling, high frequency electrical substrates (fluctuating with periods <100 ms time scale) of the RSNs can be observed. This provides a sounder neurophysiological basis for the RSNs.
Highlights
Functional magnetic resonance imaging reveals spontaneous low-frequency (
The EEG parallel independent components (pICs) corresponding to the Functional magnetic resonance imaging (fMRI) pIC representing the default mode network (DMN) or visual network (VN) was obtained by maximizing the cross correlation coefficient between their mixing matrices
The parallel ICA method for combining fMRI and EEG enabled us to evaluate relationships between resting state network maps obtained from the former and electrical time courses obtained from the latter. These results demonstrate that: (i) electrical substrates of RSNs (DMN and VN) consist of both low and pICA1 pICA2 pICA3 pICA4 pICA5 pICA6 pICA7 pICA8 pICA9 pICA10 pICA11 pICA12 pICA13 pICA14 pICA15 pICA16 pICA17 pICA18 pICA19 pICA20
Summary
Functional magnetic resonance imaging (fMRI) reveals spontaneous low-frequency (
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