Investigation of True High Frequency Electrical Substrates of fMRI-Based Resting State Networks Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data.

Kyathanahally, Sreenath Pruthviraj; Wang, Yun; Calhoun, Vince D; Deshpande, Gopikrishna (2017). Investigation of True High Frequency Electrical Substrates of fMRI-Based Resting State Networks Using Parallel Independent Component Analysis of Simultaneous EEG/fMRI Data. Frontiers in neuroinformatics, 11, p. 74. Frontiers Research Foundation 10.3389/fninf.2017.00074

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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.

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology > DCR Magnetic Resonance Spectroscopy and Methodology (AMSM)
04 Faculty of Medicine > Pre-clinic Human Medicine > BioMedical Research (DBMR) > Forschungsbereich Pavillon 52 > Abt. Magnetresonanz-Spektroskopie und Methodologie, AMSM

Graduate School:

Graduate School for Cellular and Biomedical Sciences (GCB)

UniBE Contributor:

Kyathanahally, Sreenath Pruthviraj

ISSN:

1662-5196

Publisher:

Frontiers Research Foundation

Language:

English

Submitter:

Roland Kreis

Date Deposited:

14 Mar 2018 17:28

Last Modified:

28 Oct 2019 02:34

Publisher DOI:

10.3389/fninf.2017.00074

PubMed ID:

29311887

Uncontrolled Keywords:

default mode network neurophysiological basis of DMN parallel independent component analysis primary visual cortex resting state brain networks simultaneous EEG-fMRI

BORIS DOI:

10.7892/boris.109984

URI:

https://boris.unibe.ch/id/eprint/109984

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