Brain mechanisms in motor control during reaching movements: Transition of functional connectivity according to movement states

被引:26
|
作者
Yeom, Hong Gi [1 ]
Kim, June Sic [2 ]
Chung, Chun Kee [2 ,3 ,4 ]
机构
[1] Chosun Univ, Dept Elect Engn, 309 Pilmundae Ro, Gwangju 61452, South Korea
[2] Seoul Natl Univ, Dept Brain & Cognit Sci, Coll Nat Sci, Seoul 08826, South Korea
[3] Seoul Natl Univ, Interdisciplinary Program Neurosci, Seoul 08826, South Korea
[4] Seoul Natl Univ Coll Med & Hosp, Dept Neurosurg, Seoul 03080, South Korea
基金
新加坡国家研究基金会;
关键词
CORTICAL CONTROL; NEURAL BASIS; CORTEX; ARM; DESYNCHRONIZATION; SENSORIMOTOR; TRAJECTORIES; DIRECTION; NETWORK; SYNCHRONIZATION;
D O I
10.1038/s41598-020-57489-7
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Understanding how the brain controls movements is a critical issue in neuroscience. The role of brain changes rapidly according to movement states. To elucidate the motor control mechanism of brain, it is essential to investigate the changes in brain network in motor-related regions according to movement states. Therefore, the objective of this study was to investigate the brain network transitions according to movement states. We measured whole brain magnetoencephalography (MEG) signals and extracted source signals in 24 motor-related areas. Functional connectivity and centralities were calculated according to time flow. Our results showed that brain networks differed between states of motor planning and movement. Connectivities between most motor-related areas were increased in the motor-planning state. In contrast, only connectivities with cerebellum and basal ganglia were increased while those of other motor-related areas were decreased during movement. Our results indicate that most processes involved in motor control are completed before movement. Further, brain developed network related to feedback rather than motor decision during movements. Our findings also suggest that neural signals during motor planning might be more predictive than neural signals during movement. They facilitate accurate prediction of movement for brain-machine interfaces and provide insight into brain mechanisms in motor control.
引用
收藏
页数:11
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