Publication detail

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

LABOUNEK, R. BRIDWELL, D. MAREČEK, R. LAMOŠ, M. MIKL, M. BEDNAŘÍK, P. BAŠTINEC, J. SLAVÍČEK, T. HLUŠTÍK, P. BRÁZDIL, M. JAN, J.

Original Title

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

English Title

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

Type

journal article in Web of Science

Language

en

Original Abstract

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. We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 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. 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. 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. 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.

English abstract

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. We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 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. 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. 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. 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.

Keywords

Simultaneous EEG-fMRIGroup-ICASpatiospectral patternsLarge scale brain networksMulti-subject blind source separationResting-stateSemantic decisionVisual oddball

Released

15.04.2019

Publisher

Elsevier

ISBN

0165-0270

Periodical

JOURNAL OF NEUROSCIENCE METHODS

Year of study

2019

Number

318

State

NL

Pages from

34

Pages to

46

Pages count

13

URL

Documents

BibTex


@article{BUT156054,
  author="René {Labounek} and David {Bridwell} and Radek {Mareček} and Martin {Lamoš} and Michal {Mikl} and Petr {Bednařík} and Jaromír {Baštinec} and Tomáš {Slavíček} and Petr {Hluštík} and Milan {Brázdil} and Jiří {Jan}",
  title="EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions",
  annote="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.
We examined relationships between EEG spatiospectral pattern timecourses and concurrent fMRI BOLD signals using canonical hemodynamic response function (HRF) with its 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.
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.
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.
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.",
  address="Elsevier",
  chapter="156054",
  doi="10.1016/j.jneumeth.2019.02.012",
  howpublished="online",
  institution="Elsevier",
  number="318",
  volume="2019",
  year="2019",
  month="april",
  pages="34--46",
  publisher="Elsevier",
  type="journal article in Web of Science"
}