PT  - JOURNAL ARTICLE
AU  - Judie Tabbal
AU  - Aya Kabbara
AU  - Mohamad Khalil
AU  - Pascal Benquet
AU  - Mahmoud Hassan
TI  - Dynamics of task-related electrophysiological networks: a benchmarking study
AID  - 10.1101/2020.08.02.232702
DP  - 2020 Jan 01
TA  - bioRxiv
PG  - 2020.08.02.232702
4099  - http://biorxiv.org/content/early/2020/08/04/2020.08.02.232702.short
4100  - http://biorxiv.org/content/early/2020/08/04/2020.08.02.232702.full
AB  - 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 StatementThe authors have declared no competing interest.