4.7 Article

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

Journal

INTERNATIONAL JOURNAL OF NEURAL SYSTEMS
Volume 29, Issue 1, Pages -

Publisher

WORLD SCIENTIFIC PUBL CO PTE LTD
DOI: 10.1142/S0129065718500168

Keywords

ERD/ERS; graph theory; motor imagery; time-varying network

Funding

  1. National Key Research and Development Plan of China [2017 YFB1002501]
  2. National Natural Science Foundation of China [61522105, 61603344, 81401484, 81330032]
  3. Open Foundation of Henan Key Laboratory of Brain Science and Brain-Computer Interface Technology [HNBBL17001]
  4. ChengDu's HuiMin projects of science and technology in 2013

Ask authors/readers for more resources

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.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.7
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

No Data Available
No Data Available