The Dynamic Brain Networks of Motor Imagery: Time-Varying Causality Analysis of Scalp EEG

Abstract

Motor imagery (MI) requires subjects to visualize the requested motor behaviors, which involves a large-scale network that spans multiple brain areas. The corresponding cortical activity reflected on the scalp is characterized by event-related desynchronization (ERD) and then by event-related synchronization (ERS). However, the network mechanisms that account for the dynamic information processing of MI during the ERD and ERS periods remain unknown. Here, we combined ERD/ERS analysis with the dynamic networks in different MI stages (i.e. motor preparation, ERD and ERS) to probe the dynamic processing of MI information. Our results show that specific dynamic network structures correspond to the ERD/ERS evolution patterns. Specifically, ERD mainly shows the contralateral networks, while ERS has the symmetric networks. Moreover, different dynamic network patterns are also revealed between the two types of MIs, in which the left-hand MIs exhibit a relatively less sustained contralateral network, which may be the network mechanism that accounts for the bilateral ERD/ERS observed for the left-hand MIs. Similar to the network topologies, the three MI stages also appear to be characterized by different network properties. The above findings all demonstrate that different MI stages that involve specific brain networks for dynamically processing the MI information.

The Influence of Different EEG References on Scalp EEG Functional Network Analysis During Hand Movement Tasks

“…Although the brain network method has been applied to the study of motor tasks, the problem of selecting an appropriate reference electrode in EEG research has not been solved (Popovych et al, 2016 ; Storti et al, 2016 ; Li et al, 2018b ). Similarly, the problem of the selection of the reference electrode also exists in the motor imagination (MI) that have similar pattern of brain networks with the ME (Zhang et al, 2015 ; Li et al, 2018a , 2019 ). Recently, some studies investigated how different reference choices influence scalp EEG functional connectivity using simulated EEG data (Chella et al, 2016 ; Huang et al, 2017 ).…”

Deriving Electrophysiological Brain Network Connectivity via Tensor Component Analysis During Freely Listening to Music

“…However, we do not find many applications regarding the characterization of temporal and spectral evolution of coupling between brain regions. Such coupling, generally termed functional connectivity, has been demonstrated temporal non-stationarity, spatial inhomogeneities, and spectral structure [38], [68]. It is natural to take into account the measure of time-frequency coupling between all pairs of regions based on wavelet transform, yielding a big data in tensor form with three modes corresponding to temporal course, spectrum, and spatial connectivity topology.…”

The Time-varying Networks of the Wrist Extension in Post-stroke Hemiplegic Patients

“…For each subject, the power spectral density (PSD) of each electrode was first estimated using the pWelch at α (8 -13) and β (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30). When exploring the group differences, the PSD of the PL, PR, and HC groups were statistically compared by using the non-parametric Wilcoxon rank-sum test, whose p-values were then multiply corrected by the false discovery rate (FDR) under a significance level of 0.05 (p < 0.05).…”

“…It facilitates the construction of the time-varying networks on each time point, which thus provides the opportunity to estimate the directed flows among concerned brain regions and to capture the dynamic transition related to the movement effectively. For example, based on the ATDF, Li et al have demonstrated the distinct information processing stages related to the P300 [14] and the motor imagery [15], as well.…”