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Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model?
Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models util...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8107237/ https://www.ncbi.nlm.nih.gov/pubmed/33981283 http://dx.doi.org/10.3389/fneur.2021.644874 |
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author | Labounek, René Wu, Zhuolin Bridwell, David A. Brázdil, Milan Jan, Jiří Nestrašil, Igor |
author_facet | Labounek, René Wu, Zhuolin Bridwell, David A. Brázdil, Milan Jan, Jiří Nestrašil, Igor |
author_sort | Labounek, René |
collection | PubMed |
description | Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ(4) band and low β(1) band) demonstrated significant negative linear relationship (p(FWE) < 0.05) to the frequent stimulus and three patterns (two low δ(2) and δ(3) bands, and narrow θ(1) band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ(4) model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ(4) model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β(1) patterns visualized less significant and distinct suprathreshold spatial associations. Each θ(1) model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ(1) model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ(4), β(1), and θ(1) bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM. |
format | Online Article Text |
id | pubmed-8107237 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-81072372021-05-11 Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? Labounek, René Wu, Zhuolin Bridwell, David A. Brázdil, Milan Jan, Jiří Nestrašil, Igor Front Neurol Neurology Various disease conditions can alter EEG event-related responses and fMRI-BOLD signals. We hypothesized that event-related responses and their clinical alterations are imprinted in the EEG spectral domain as event-related (spatio)spectral patterns (ERSPat). We tested four EEG-fMRI fusion models utilizing EEG power spectra fluctuations (i.e., absolute spectral model - ASM; relative spectral model - RSM; absolute spatiospectral model - ASSM; and relative spatiospectral model - RSSM) for fully automated and blind visualization of task-related neural networks. Two (spatio)spectral patterns (high δ(4) band and low β(1) band) demonstrated significant negative linear relationship (p(FWE) < 0.05) to the frequent stimulus and three patterns (two low δ(2) and δ(3) bands, and narrow θ(1) band) demonstrated significant positive relationship (p < 0.05) to the target stimulus. These patterns were identified as ERSPats. EEG-fMRI F-map of each δ(4) model showed strong engagement of insula, cuneus, precuneus, basal ganglia, sensory-motor, motor and dorsal part of fronto-parietal control (FPCN) networks with fast HRF peak and noticeable trough. ASM and RSSM emphasized spatial statistics, and the relative power amplified the relationship to the frequent stimulus. For the δ(4) model, we detected a reduced HRF peak amplitude and a magnified HRF trough amplitude in the frontal part of the FPCN, default mode network (DMN) and in the frontal white matter. The frequent-related β(1) patterns visualized less significant and distinct suprathreshold spatial associations. Each θ(1) model showed strong involvement of lateralized left-sided sensory-motor and motor networks with simultaneous basal ganglia co-activations and reduced HRF peak and amplified HRF trough in the frontal part of the FPCN and DMN. The ASM θ(1) model preserved target-related EEG-fMRI associations in the dorsal part of the FPCN. For δ(4), β(1), and θ(1) bands, all models provided high local F-statistics in expected regions. The most robust EEG-fMRI associations were observed for ASM and RSSM. Frontiers Media S.A. 2021-04-26 /pmc/articles/PMC8107237/ /pubmed/33981283 http://dx.doi.org/10.3389/fneur.2021.644874 Text en Copyright © 2021 Labounek, Wu, Bridwell, Brázdil, Jan and Nestrašil. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Neurology Labounek, René Wu, Zhuolin Bridwell, David A. Brázdil, Milan Jan, Jiří Nestrašil, Igor Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? |
title | Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? |
title_full | Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? |
title_fullStr | Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? |
title_full_unstemmed | Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? |
title_short | Blind Visualization of Task-Related Networks From Visual Oddball Simultaneous EEG-fMRI Data: Spectral or Spatiospectral Model? |
title_sort | blind visualization of task-related networks from visual oddball simultaneous eeg-fmri data: spectral or spatiospectral model? |
topic | Neurology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8107237/ https://www.ncbi.nlm.nih.gov/pubmed/33981283 http://dx.doi.org/10.3389/fneur.2021.644874 |
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