A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates

B. Hunyadi; M. W. Woolrich; A. J. Quinn; D. Vidaurre; M. De Vos

doi:10.1016/j.neuroimage.2018.09.082

A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates

B. Hunyadi^*, M. W. Woolrich, A. J. Quinn, D. Vidaurre, M. De Vos

^*Corresponding author for this work

Psychology

Research output: Contribution to journal › Article › peer-review

21 Citations (Scopus)

Abstract

Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks.

Original language	English
Pages (from-to)	72-82
Number of pages	11
Journal	NeuroImage
Volume	185
DOIs	https://doi.org/10.1016/j.neuroimage.2018.09.082
Publication status	Published - 15 Jan 2019

Bibliographical note

Funding Information:
We thank Sebastian Puschmann for kindly providing the EEG-fMRI dataset. BH was supported by imec funds 2017, Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO) and by the European Research Council : The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme ( FP7/2007–2013 )/ERC Advanced Grant: BIOTENSORS (n° 339804 ). This paper reflects only the authors' views and the Union is not liable for any use that may be made of the contained information. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust ( 203139/Z/16/Z ). MWW's research is supported by the NIHR Oxford Health Biomedical Research Centre , by the Wellcome Trust ( 106183/Z/14/Z ), and the MRC UK MEG Partnership Grant ( MR/K005464/1 ). MDV's research is supported by the National Institute for Health Research ; Oxford Biomedical Research Centre (BRC) .

Publisher Copyright:
© 2018 Elsevier Inc.

ASJC Scopus subject areas

Neurology
Cognitive Neuroscience

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10.1016/j.neuroimage.2018.09.082

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abstract = "Resting state brain activity has become a significant area of investigation in human neuroimaging. An important approach for understanding the dynamics of neuronal activity in the resting state is to use complementary imaging modalities. Electrophysiological recordings can access fast temporal dynamics, while functional magnetic resonance imaging (fMRI) studies reveal detailed spatial patterns. However, the relationship between these two measures is not fully established. In this study, we used simultaneously recorded electroencephalography (EEG) and fMRI, along with Hidden Markov Modelling, to investigate how network dynamics at fast sub-second time-scales, accessible with EEG, link to the slower time-scales and higher spatial detail of fMRI. We found that the fMRI correlates of fast transient EEG dynamic networks show highly reproducible spatial patterns, and that their spatial organization exhibits strong similarity with traditional fMRI resting state networks maps. This further demonstrates the potential of electrophysiology as a tool for understanding the fast network dynamics that underlie fMRI resting state networks.",

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note = "Funding Information: We thank Sebastian Puschmann for kindly providing the EEG-fMRI dataset. BH was supported by imec funds 2017, Fonds voor Wetenschappelijk Onderzoek-Vlaanderen (FWO) and by the European Research Council : The research leading to these results has received funding from the European Research Council under the European Union's Seventh Framework Programme ( FP7/2007–2013 )/ERC Advanced Grant: BIOTENSORS (n° 339804 ). This paper reflects only the authors' views and the Union is not liable for any use that may be made of the contained information. The Wellcome Centre for Integrative Neuroimaging is supported by core funding from the Wellcome Trust ( 203139/Z/16/Z ). MWW's research is supported by the NIHR Oxford Health Biomedical Research Centre , by the Wellcome Trust ( 106183/Z/14/Z ), and the MRC UK MEG Partnership Grant ( MR/K005464/1 ). MDV's research is supported by the National Institute for Health Research ; Oxford Biomedical Research Centre (BRC) . Publisher Copyright: {\textcopyright} 2018 Elsevier Inc.",

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A dynamic system of brain networks revealed by fast transient EEG fluctuations and their fMRI correlates

Abstract

Bibliographical note

ASJC Scopus subject areas

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