EEG spatiospectral patterns and their link to fMRI BOLD signal
via variable hemodynamic response functions

J 2019

EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions

LABOUNEK, R., D.A. BRIDWELL, Radek MAREČEK, Martin LAMOŠ, Michal MIKL et. al.

Basic information

Original name

EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions

Authors

LABOUNEK, R. (203 Czech Republic), D.A. BRIDWELL (840 United States of America), Radek MAREČEK (203 Czech Republic, belonging to the institution), Martin LAMOŠ (203 Czech Republic, belonging to the institution), Michal MIKL (203 Czech Republic, belonging to the institution), P. BEDNARIK (203 Czech Republic), J. BASTINEC (203 Czech Republic), Tomáš SLAVÍČEK (203 Czech Republic, belonging to the institution), P. HLUSTIK (203 Czech Republic), Milan BRÁZDIL (203 Czech Republic, guarantor, belonging to the institution) and J. JAN (203 Czech Republic)

Edition

Journal of Neuroscience Methods, AMSTERDAM, ELSEVIER SCIENCE BV, 2019, 0165-0270

Other information

Language

English

Type of outcome

Článek v odborném periodiku

Field of Study

30103 Neurosciences

Country of publisher

Netherlands

Confidentiality degree

není předmětem státního či obchodního tajemství

References:

URL

Impact factor

Impact factor: 2.214

RIV identification code

RIV/00216224:14740/19:00112798

Organization unit

Central European Institute of Technology

DOI

http://dx.doi.org/10.1016/j.jneumeth.2019.02.012

UT WoS

000463294200004

Keywords in English

Simultaneous EEG-fMRI; Group-ICA; Spatiospectral patterns; Large scale brain networks; Multi-subject blind source separation; Resting-state; Semantic decision; Visual oddball

Abstract

V originále

Background: Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. New Method: We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during "resting-state", visual oddball and semantic decision paradigms. Results: The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. Comparison with existing method(s): Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. Conclusions: This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.

Links

ED1.1.00/02.0068, research and development project

Name: CEITEC - central european institute of technology

ED3.2.00/08.0144, research and development project

Name: CERIT Scientific Cloud

LM2010005, research and development project

Name: Velká infrastruktura CESNET (Acronym: VI CESNET)

Investor: Ministry of Education, Youth and Sports of the CR

90062, large research infrastructures

Name: Czech-BioImaging

Citovat

LABOUNEK, R., D.A. BRIDWELL, Radek MAREČEK, Martin LAMOŠ, Michal MIKL, P. BEDNARIK, J. BASTINEC, Tomáš SLAVÍČEK, P. HLUSTIK, Milan BRÁZDIL and J. JAN. EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions. Journal of Neuroscience Methods. AMSTERDAM: ELSEVIER SCIENCE BV, 2019, vol. 318, APR, p. 34-46. ISSN 0165-0270. Available from: https://dx.doi.org/10.1016/j.jneumeth.2019.02.012.

@article{1612564,
   author = {Labounek, R. and Bridwell, D.A. and Mareček, Radek and Lamoš, Martin and Mikl, Michal and Bednarik, P. and Bastinec, J. and Slavíček, Tomáš and Hlustik, P. and Brázdil, Milan and Jan, J.},
   article_location = {AMSTERDAM},
   article_number = {APR},
   doi = {http://dx.doi.org/10.1016/j.jneumeth.2019.02.012},
   keywords = {Simultaneous EEG-fMRI; Group-ICA; Spatiospectral patterns; Large scale brain networks; Multi-subject blind source separation; Resting-state; Semantic decision; Visual oddball},
   language = {eng},
   issn = {0165-0270},
   journal = {Journal of Neuroscience Methods},
   title = {EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions},
   url = {https://www.sciencedirect.com/science/article/pii/S0165027019300597?via%3Dihub},
   volume = {318},
   year = {2019}
}

TY  - JOUR
ID  - 1612564
AU  - Labounek, R. - Bridwell, D.A. - Mareček, Radek - Lamoš, Martin - Mikl, Michal - Bednarik, P. - Bastinec, J. - Slavíček, Tomáš - Hlustik, P. - Brázdil, Milan - Jan, J.
PY  - 2019
TI  - EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions
JF  - Journal of Neuroscience Methods
VL  - 318
IS  - APR
SP  - 34-46
EP  - 34-46
PB  - ELSEVIER SCIENCE BV
SN  - 01650270
KW  - Simultaneous EEG-fMRI
KW  - Group-ICA
KW  - Spatiospectral patterns
KW  - Large scale brain networks
KW  - Multi-subject blind source separation
KW  - Resting-state
KW  - Semantic decision
KW  - Visual oddball
UR  - https://www.sciencedirect.com/science/article/pii/S0165027019300597?via%3Dihub
L2  - https://www.sciencedirect.com/science/article/pii/S0165027019300597?via%3Dihub
N2  - Background: Spatial and temporal resolution of brain network activity can be improved by combining different modalities. Functional Magnetic Resonance Imaging (fMRI) provides full brain coverage with limited temporal resolution, while electroencephalography (EEG), estimates cortical activity with high temporal resolution. Combining them may provide improved network characterization. New Method: We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with 1st and 2nd temporal derivatives in voxel-wise general linear models (GLM). HRF shapes were derived from EEG-fMRI time courses during "resting-state", visual oddball and semantic decision paradigms. Results: The resulting GLM F-maps self-organized into several different large-scale brain networks (LSBNs) often with different timing between EEG and fMRI revealed through differences in GLM-derived HRF shapes (e.g., with a lower time to peak than the canonical HRF). We demonstrate that some EEG spatiospectral patterns (related to concurrent fMRI) are weakly task-modulated. Comparison with existing method(s): Previously, we demonstrated 14 independent EEG spatiospectral patterns within this EEG dataset, stable across the resting-state, visual oddball and semantic decision paradigms. Here, we demonstrate that their time courses are significantly correlated with fMRI dynamics organized into LSBN structures. EEG-fMRI derived HRF peak appears earlier than the canonical HRF peak, which suggests limitations when assuming a canonical HRF shape in EEG-fMRI. Conclusions: This is the first study examining EEG-fMRI relationships among independent EEG spatiospectral patterns over different paradigms. The findings highlight the importance of considering different HRF shapes when spatiotemporally characterizing brain networks using EEG and fMRI.
ER  -

LABOUNEK, R., D.A. BRIDWELL, Radek MAREČEK, Martin LAMOŠ, Michal MIKL, P. BEDNARIK, J. BASTINEC, Tomáš SLAVÍČEK, P. HLUSTIK, Milan BRÁZDIL and J. JAN. EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions. \textit{Journal of Neuroscience Methods}. AMSTERDAM: ELSEVIER SCIENCE BV, 2019, vol.~318, APR, p.~34-46. ISSN~0165-0270. Available from: https://dx.doi.org/10.1016/j.jneumeth.2019.02.012.

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