Dynamics of task-related electrophysiological networks: a benchmarking study

• Published in: NeuroImage (Orlando, Fla.) Vol. 231; p. 117829
• Main Authors: Tabbal, Judie, Kabbara, Aya, Khalil, Mohamad, Benquet, Pascal, Hassan, Mahmoud
• Format: Journal Article
• Language: English
• Published: United States Elsevier Inc 01.05.2021; Elsevier Limited; Elsevier
• Subjects: Algorithms; Bioengineering; Brain research; Cognitive ability; Datasets; Dimensionality reduction; Dynamic functional connectivity; Electrophysiological brain networks; Information processing; Life Sciences; Magneto-encephalography (MEG); Magnetoencephalography; Memory; Methods; Nervous system; Neurons and Cognition; Performance evaluation; Principal components analysis; Source separation; Dimensionality reduction; Electrophysiological brain networks; Source separation; Dynamic functional connectivity; Magneto-encephalography (MEG)
• ISSN: 1053-8119; 1095-9572; 1095-9572
• DOI: 10.1016/j.neuroimage.2021.117829

•We explore the fast reconfiguration of electrophysiological brain networks during motor and working memory tasks.•We provide a quantitative evaluation of the performance of nine source separation methods applied on dynamic functional connectivity.•Results show variability between the methods at both group and subject levels in terms of space/time accuracy.•Independent Component Analysis (ICA) methods based on high order statistics provide promising results, while SOBI and Kmeans exhibit a fragility related to data complexity and timescale resolution. 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 great variety of dimensionality reduction methods used at the network-level 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 seek to help researchers in choosing objectively the appropriate SS method when tracking fast reconfiguration of functional brain networks, due to its enormous benefits in cognitive and clinical neuroscience.