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Original title:
Spatio-Spectral EEG Patterns in the Source-Reconstructed Space and Relation to Resting-State Networks: An EEG-fMRI Study
Authors: Jiříček, Stanislav ; Koudelka, V. ; Mantini, D. ; Mareček, R. ; Hlinka, Jaroslav
Document type: Research reports
Year: 2022
Language: eng
Series: Technical Report, volume: V-1288
Abstract: 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.
Keywords: EEG-fMRI Integration; EEG-informed fMRI; Electrical Source Imaging; Independent Component Analysis; Resting State Networks; Spatio-spectral Decomposition
Project no.: GA21-32608S (CEP)
Funding provider: GA ČR
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: https://hdl.handle.net/11104/0333634
Permalink: http://www.nusl.cz/ntk/nusl-508699
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Reports > Research reports
Authors: Jiříček, Stanislav ; Koudelka, V. ; Mantini, D. ; Mareček, R. ; Hlinka, Jaroslav
Document type: Research reports
Year: 2022
Language: eng
Series: Technical Report, volume: V-1288
Abstract: 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.
Keywords: EEG-fMRI Integration; EEG-informed fMRI; Electrical Source Imaging; Independent Component Analysis; Resting State Networks; Spatio-spectral Decomposition
Project no.: GA21-32608S (CEP)
Funding provider: GA ČR
Institution: Institute of Computer Science AS ČR (web)
Document availability information: Fulltext is available in the digital repository of the Academy of Sciences.
Original record: https://hdl.handle.net/11104/0333634
Permalink: http://www.nusl.cz/ntk/nusl-508699
The record appears in these collections:
Research > Institutes ASCR > Institute of Computer Science
Reports > Research reports
Record created 2022-09-28, last modified 2023-12-06