Brain Network Topology Study on Precise Grip Force Controlunder Visual Feedback
Lv Yadong1, Li Ke1*, Hou Ying2, Zhang Dongmei2, Wei Na3,4*
1(Department of Biomedical Engineering, School of Control Science and Engineering, Shandong University, Jinan 250061, China) 2(Department of Rehabilitation, Suzhou Hospital, Nanjing Medical University, Suzhou 215008, Jiangsu, China) 3(Department of Geriatrics, Qilu Hospital, Shandong University, Jinan 250012, China) 4(Shandong University Suzhou Research Institute, Suzhou 215000, Jiangsu, China)
Abstract：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.
[1] Geevasinga N, Menon P, Kiernan MC, et al. Motor cortical function and the precision grip [J]. Physiol Rep, 2014, 2(12): 1-10. [2] Hu W, Wei N, Li ZM, et al. Effects of muscle fatigue on directional coordination of fingertip forces during precision grip [J]. PLoS ONE, 2018, 13(12): 1-15. [3] Li K, Nataraj R, Marquardt TL, et al. Directional coordination of thumb and finger forces during precision pinch [J]. PLoS ONE, 2013, 8(11): 1-8. [4] Mayhew SD, Porcaro C, Tecchio F, et al. Fmri characterisation of widespread brain networks relevant for behavioural variability in fine hand motor control with and without visual feedback [J]. Neuroimage, 2017, 148:330-342. [5] Dai TH, Liu JZ, Sahgal V, et al. Relationship between muscle output and functional mri-measured brain activation [J]. Exp Brain Res, 2001, 140(3): 290-300. [6] Huang CY, Su JH, Hwang IS. Rate control and quality assurance during rhythmic force tracking [J]. Behav Brain Res, 2014, 259: 186-195. [7] Yu M, Gouw AA, Hillebrand A, et al. Different functional connectivity and network topology in behavioral variant of frontotemporal dementia and alzheimer's disease: An EEG study [J]. Neurobiology of Aging, 2016, 42: 150-162. [8] Rubinov M, Sporns O. Complex network measures of brain connectivity: Uses and interpretations [J]. Neuroimage, 2010, 52(3): 1059-1069. [9] Stam CJ, Nolte G, Daffertshofer A. Phase lag index: Assessment of functional connectivity from multi channelEEG and MEG with diminished bias from common sources [J]. Hum Brain Mapp, 2007, 28(11): 1178-1193. [10] Hou F, Liu C, Yu Z, et al. Age-related alterations in electroencephalography connectivity and network topology during n-back working memory task [J]. Front Hum Neurosci, 2018, 12(484): 1-12. [11] Fraga Gonzalez G, Smit DJA, Van Der Molen MJW, et al. EEG resting state functional connectivity in adult dyslexics using phase lag index and graph analysis [J]. Front Hum Neurosci, 2018, 12(341): 1-12. [12] Olejarczyk E, Jernajczyk W. Graph-based analysis of brain connectivity in schizophrenia [J]. PLoS ONE, 2017, 12(11): 1-28. [13] Salvador R, Suckling J, Schwarzbauer C, et al. Undirected graphs of frequency-dependent functional connectivity in whole brain networks [J]. Philos Trans R Soc Lond B Biol Sci, 2005, 360(1457): 937-946. [14] Li K, Wei N, Cheng M, et al. Dynamical coordination of hand intrinsic muscles for precision grip in diabetes mellitus [J]. Scientific Reports, 2018, 8(4365): 1-13. [15] Stefano Filho CA, Attux R, Castellano G. Can graph metrics be used for EEG-BCIS based on hand motor imagery? [J]. Biomed Signal Process Control, 2018, 40: 359-365. [16] Porz S, Kiel M, Lehnertz K. Can spurious indications for phase synchronization due to superimposed signals be avoided? [J]. Chaos (Woodbury, NY), 2014, 24(3): 1-11. [17] Fraga Gonzalez G, Van Der Molen MJW, Zaric G, et al. Graph analysis of EEG resting state functional networks in dyslexic readers [J]. Clin Neurophysiol, 2016, 127(9): 3165-3175. [18] Huang D, Ren A, Shang J, et al. Combining partial directed coherence and graph theory to analyse effective brain networks of different mental tasks [J]. Front Hum Neurosci, 2016, 10: 1-11. [19] Dimitriadis SI, Zouridakis G, Rezaie R, et al. Functional connectivity changes detected with magnetoencephalography after mild traumatic brain injury [J]. Neuroimage Clin, 2015, 9: 519-531. [20] Rubinov M, Knock SA, Stam CJ, et al. Small-world properties of nonlinear brain activity in schizophrenia [J]. Hum Brain Mapp, 2009, 30(2): 403-416. [21] Delorme A, Makeig S. Eeglab: An open source toolbox for analysis of single-trial EEG dynamics including independent component analysis [J]. J Neurosci Methods, 2004, 134: 9-21. [22] Pfurtscheller G, Lopes Da Silva FH. Event-relatedEEG/MEG synchronization and desynchronization: Basic principles [J]. Clin Neurophysiol, 1999, 110(11): 1842-1857. [23] Tononi G, Sporns O, Edelman GM. A measure for brain complexity: Relating functional segregation and integration in the nervous system [J]. Proceedings of the National Academy of Sciences, 1994, 91(11): 5033-5037. [24] Bassett DS, Bullmore E. Small-world brain networks [J]. Neuroscientist, 2006, 12(6): 512-523. [25] 谢平, 杨芳梅, 陈晓玲, 等. 基于多尺度传递熵的脑肌电信号耦合分析 [J]. 物理学报, 2015, 64(24): 423-432. [26] 谢平, 杨芳梅, 李欣欣, 等. 基于变分模态分解-传递熵的脑肌电信号耦合分析 [J]. 物理学报, 2016, 65(11): 118701. [27] Godde B, Trautmann M, Erhard P, et al. Motor practice in a force modulation task in young and middle-aged adults [J]. J Electromyogr Kinesiol, 2018, 38: 224-231.
