EEG spatiospectral patterns and their link to fMRI BOLD signal via variable hemodynamic response functions
| Autor: | Radek Mareček, Petr Bednařík, René Labounek, Milan Brázdil, Petr Hluštík, Jaromír Baštinec, David A. Bridwell, Jiří Jan, Michal Mikl, Tomáš Slavíček, Martin Lamoš |
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| Rok vydání: | 2019 |
| Předmět: |
Adult
Male 0301 basic medicine Haemodynamic response Computer science Electroencephalography Young Adult 03 medical and health sciences 0302 clinical medicine medicine Humans Bold fmri Cerebrum Brain network Psycholinguistics medicine.diagnostic_test Resting state fMRI business.industry Functional Neuroimaging General Neuroscience Pattern recognition Magnetic Resonance Imaging Variable (computer science) 030104 developmental biology Temporal resolution Visual Perception Neurovascular Coupling Female Artificial intelligence Nerve Net Functional magnetic resonance imaging business 030217 neurology & neurosurgery |
| Zdroj: | Journal of Neuroscience Methods. 318:34-46 |
| ISSN: | 0165-0270 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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