Spatio-Spectral EEG Patterns in the Source-Reconstructed Space and Relation to Resting-State Networks: An EEG-fMRI Study

0560856 - ÚI 2023 CZ eng V - Research Report
Jiříček, Stanislav - Koudelka, V. - Mantini, D. - Mareček, R. - Hlinka, Jaroslav
Spatio-Spectral EEG Patterns in the Source-Reconstructed Space and Relation to Resting-State Networks: An EEG-fMRI Study.
Prague: ICS CAS, 2022. 23 s. Technical Report, V-1288.
R&D Projects: GA ČR(CZ) GA21-32608S
Institutional support: RVO:67985807
Keywords : EEG-fMRI Integration * EEG-informed fMRI * Spatio-spectral Decomposition * Electrical Source Imaging * Independent Component Analysis * Resting State Networks

In this work, we present and evaluate a novel EEG-fMRI integration approach combining a spatio-spectral decomposition method and a reliable source localization technique. On the large 72 subjects resting- state hdEEG-fMRI data set we tested the stability of the proposed method in terms of both extracted spatio-spectral patterns(SSPs) as well as their correspondence to the BOLD signal. We also compared the proposed method with the spatio-spectral decomposition in the electrode space as well as well-known occipital alpha correlate in terms of the explained variance of BOLD signal. We showed that the proposed method is stable in terms of extracted patterns and where they correlate with the BOLD signal. Furthermore, we show that the proposed method explains a very similar level of the BOLD signal with the other methods and that the BOLD signal in areas of typical BOLD functional networks is explained significantly more than by a chance. Nevertheless, we didn’t observe a significant relation between our source-space SSPs and the BOLD ICs when spatio-temporally comparing them. Finally, we report several the most stable source space EEG-fMRI patterns together with their interpretation and comparison to the electrode space patterns.
Permanent Link: https://hdl.handle.net/11104/0333634