视觉反馈作用下精确抓握力量控制的脑网络拓扑研究

抓握的精确力量控制是实现各种复杂精细手功能的关键。在人体进行精确抓握力量控制时,力量变化的速度、力量的上升与下降是否由不同的脑功能网络支配尚不得而知,其潜在的感知运动控制机制仍不清楚。探讨视觉-精确抓握力量跟踪任务下,握力的变化速度和握力上升及下降时脑网络的拓扑变化。研究中招募11名健康受试者,首先测量其抓握的最大自主收缩力(MVC),然后要求受试者使用右手大拇指和食指执行3种速度下的视觉-精确抓握力量跟踪任务(包括握力上升和下降状态),其中3种速度分别为1% MVC/s(速度1)、2% MVC/s(速度2)、3% MVC/s(速度3),同时记录受试者全脑共32通道的脑电信号,随后使用网络拓扑的平均聚类系数C和特征路径长度L参数,对基于相位延迟指数的脑电功能网络进行分析。结果显示,C值在θ频带的速度1、速度2、速度3下,当握力上升时分别为(0.157±0.032)、(0.164±0.044)、(0.194±0.039),当握力下降时分别为(0.154±0.026)、(0.173±0.041)、(0.211±0.058),C值随着跟踪速度的增加而显著性地增加(P<0.05)。同样地,C值在β频带存在类似的变化(P<0.001)。与C值变化不同的是,L值在θ频带的速度1、速度2、速度3下,当握力上升时分别为(4.644±0.400)、(4.150±0.325)、(3.909±0.497),当握力下降时分别为(4.606±0.346)、(4.040±0.471)、(3.716±0.498),L值随着跟踪速度的增加而显著性地减小(P<0.001),并且L值在α、β、γ等3个频段存在与θ频带类似的变化。随着速度增加,中央沟和后顶叶局部激活加强。除了在跟踪速度2条件下β频带中L值的P=0.049之外,上升和下降状态之间没有显著差异。随着速度增加,脑功能网络的全局和局部信息传输效率得到增强,意味着在适应速度差异期间脑网络的连接模式发生改变。该项研究为探究不同力变化速度和上升下降状态下精确抓握的感知运动控制机制提供依据,为神经系统疾病后手功能的康复状态提供新的评估手段。

Brain Network Topology Study on Precise Grip Force Controlunder Visual Feedback

The precise force control of grip is the key to achieve a variety of sophisticated hand functions. When human performs precise force control of grip, whether the speed and the up-down states of force change or not is dominated by different brain functional networks, and the underlying sensorimotor control mechanism remains unclear. The purpose of this study was to investigate the topology changes of the EEG function network related with the speed and up-down states of grip force changes under the vision-precision grip force tracking task. In this study, 11 healthy subjects were recruited. First, the maximum voluntary contraction (MVC) of their grip was measured, and then the subjects were requested to use the thumb and index finger of their right hand to perform vision-precision grip force tracking task at the speed of 1% MVC/s (speed 1), 2% MVC/s (speed 2), and 3% MVC/s (speed 3). The network topology parameters of average clustering coefficient C and the characteristic path length L were used to analyze the EEG functional network based on the phase lag index. The results showed that C values in the θ frequency band during up-ramp state were (0.157±0.032), (0.164±0.044), (0.194±0.039) and during down-ramp state were (0.154±0.026), (0.173±0.041), (0.211±0.058). The C increased significantly in the θ (P<0.05) and similarly in the β (P<0.001) frequency bands. Different from the change of C values, L values in the θ frequency band during up-ramp state were (4.644±0.400), (4.150±0.325), (3.909±0.497) and during down-ramp state were (4.606±0.346), (4.040±0.471), (3.716±0.498). The L decreased significantly in the θ (P<0.001) and similarly in the α, β and γ frequency bands. The local activation of central and posterior parietal were improved with the increasing speed. Except for the L value in the β band P=0.049] under the condition of tracking speed 2, there was no significant difference between the up and down states. These results indicated that the global and local information transmission efficiency was enhanced with the increasing speed, which meant the brain network connectivity pattern was altered during the adaptation to speed differences. This study provided a basis for exploring the sensorimotor control mechanism of precision grip under the different speeds and up-down states, and provided a new evaluation method for the rehabilitation state of hand function after nervous system diseases.