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Dynamics of task-related electrophysiological networks: a benchmarking study

Judie Tabbal, Aya Kabbara, Mohamad Khalil, Pascal Benquet, View ORCID ProfileMahmoud Hassan
doi: https://doi.org/10.1101/2020.08.02.232702
Judie Tabbal
1Univ Rennes, LTSI - U1099, F-35000 Rennes, France
2Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon
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  • For correspondence: judytabal95@gmail.com
Aya Kabbara
1Univ Rennes, LTSI - U1099, F-35000 Rennes, France
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Mohamad Khalil
2Azm Center for Research in Biotechnology and Its Applications, EDST, Lebanese University, Beirut, Lebanon
3CRSI Lab, Engineering Faculty, Lebanese University, Beirut, Lebanon
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Pascal Benquet
1Univ Rennes, LTSI - U1099, F-35000 Rennes, France
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Mahmoud Hassan
4NeuroKyma, F-35000 Rennes, France
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  • ORCID record for Mahmoud Hassan
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Abstract

Motor, sensory and cognitive functions rely on dynamic reshaping of functional brain networks. Tracking these rapid changes is crucial to understand information processing in the brain, but challenging due to the random selection of methods and the limited evaluation studies. Using Magnetoencephalography (MEG) combined with Source Separation (SS) methods, we present an integrated framework to track fast dynamics of electrophysiological brain networks. We evaluate nine SS methods applied to three independent MEG databases (N=95) during motor and memory tasks. We report differences between these methods at the group and subject level. We show that the independent component analysis (ICA)-based methods and especially those exploring high order statistics are the most efficient, in terms of spatiotemporal accuracy and subject-level analysis. We seek to help researchers in choosing objectively the appropriate methodology when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.

Competing Interest Statement

The authors have declared no competing interest.

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC-ND 4.0 International license.
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Posted August 04, 2020.
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Dynamics of task-related electrophysiological networks: a benchmarking study
Judie Tabbal, Aya Kabbara, Mohamad Khalil, Pascal Benquet, Mahmoud Hassan
bioRxiv 2020.08.02.232702; doi: https://doi.org/10.1101/2020.08.02.232702
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Dynamics of task-related electrophysiological networks: a benchmarking study
Judie Tabbal, Aya Kabbara, Mohamad Khalil, Pascal Benquet, Mahmoud Hassan
bioRxiv 2020.08.02.232702; doi: https://doi.org/10.1101/2020.08.02.232702

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