Motor imagery learning modulates functional connectivity of multiple brain systems in resting state.
BACKGROUND
Learning motor skills involves subsequent modulation of resting-state functional connectivity in the sensory-motor system. This idea was mostly derived from the investigations on motor execution learning which mainly recruits the processing of sensory-motor information. Behavioral evidences demonstrated that motor skills in our daily lives could be learned through imagery procedures. However, it remains unclear whether the modulation of resting-state functional connectivity also exists in the sensory-motor system after motor imagery learning.


RESULTS
We performed a fMRI investigation on motor imagery learning from resting state. Based on previous studies, we identified eight sensory and cognitive resting-state networks (RSNs) corresponding to the brain systems and further explored the functional connectivity of these RSNs through the assessments, connectivity and network strengths before and after the two-week consecutive learning. Two intriguing results were revealed: (1) The sensory RSNs, specifically sensory-motor and lateral visual networks exhibited greater connectivity strengths in precuneus and fusiform gyrus after learning; (2) Decreased network strength induced by learning was proved in the default mode network, a cognitive RSN.


CONCLUSIONS
These results indicated that resting-state functional connectivity could be modulated by motor imagery learning in multiple brain systems, and such modulation displayed in the sensory-motor, visual and default brain systems may be associated with the establishment of motor schema and the regulation of introspective thought. These findings further revealed the neural substrates underlying motor skill learning and potentially provided new insights into the therapeutic benefits of motor imagery learning.
C. Lauterbur Research Centers for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, China ; Department of Biomedical Engineering, Peking University, Beijing, China ; School of Information Science and Technology, Beijing Normal University, Beijing, China.
Hang
H
Zhang
National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Zhiying
Z
Long
School of Information Science and Technology, Beijing Normal University, Beijing, China.
Ruiyang
R
Ge
School of Information Science and Technology, Beijing Normal University, Beijing, China.
Lele
L
Xu
Laboratory of Magnetic Resonance Imaging, the 306th Hospital of PLA, Beijing, China.
Zhen
Z
Jin
School of Information Science and Technology, Beijing Normal University, Beijing, China ; National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China.
Li
L
Yao
Department of Biomedical Engineering, Peking University, Beijing, China ; Department of Psychiatry and McKnight Brain Institute, University of Florida, Gainesville, Florida, United States of America.
Yijun
Y
Liu
D001921:Brain / Q000502:physiology*; D003071:Cognition / Q000502:physiology; D005260:Female; D006801:Humans; D019018:Imagery, Psychotherapy; D007858:Learning / Q000502:physiology; D008297:Male; D009043:Motor Activity / Q000502:physiology*; D009415:Nerve Net / Q000502:physiology*; D012146:Rest / Q000502:physiology*; D055815:Young Adult