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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00098892%3A_____%2F19%3AN0000153" target="_blank" >RIV/00098892:_____/19:N0000153 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216224:14740/19:00112798 RIV/00216305:26220/19:PU131487

Výsledek na webu

<a href="https://www.sciencedirect.com/science/article/pii/S0165027019300597?via%3Dihub" target="_blank" >https://www.sciencedirect.com/science/article/pii/S0165027019300597?via%3Dihub</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.jneumeth.2019.02.012" target="_blank" >10.1016/j.jneumeth.2019.02.012</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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 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. 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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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 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. 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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30210 - Clinical neurology

Projekt

Výsledek vznikl pri realizaci vícero projektů. Více informací v záložce Projekty.

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2019

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

JOURNAL OF NEUROSCIENCE METHODS

ISSN

0165-0270

e-ISSN

1872-678X

Svazek periodika

318

Číslo periodika v rámci svazku

April 2019

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

13

Strana od-do

34-46

Kód UT WoS článku

000463294200004

EID výsledku v databázi Scopus

2-s2.0-85062413146