Transcranial magnetic stimulation-induced finger force changes under various finger coordination patterns and target finger force phases****☆○

Xiaoying Wu1, Wensheng Hou1, Xiaolin Zheng1, Shan Shen○2, Yingtao Jiang○4, Jun Zheng○3, Yan He1

1Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing  400044, China
2Psychology Department, University of Surrey, Guildford, Surrey, Gu2 7XH, UK
3Department of Computer Science, New Mexico Institute of Mining and Technology, Socorro, NM  87801, USA
4Department of Electrical and Computer Engineering, University of Nevada, Las Vegas, NV  89154, USA

Xiaoying Wu☆, Studying for doctorate, Lecturer, Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing  400044, China

Corresponding author: Wensheng Hou, Doctor, Professor, Doctoral supervisor, Key Laboratory of Biorheological Science and Technology, Ministry of Education, Bioengineering College, Chongqing University, Chongqing  400044, China
hws21cn@yahoo.com.cn

Supported by: the National Natural Science Foundation of China, No. 30770546*; 30970758*; Chongqing Natural Science Foundation, No. 2006BB2043*; 2007BB5148*

Abstract
BACKGROUND: The detection of motor evoked potential is utilized to explore neuromuscular finger coordination. The influence of transcranial magnetic stimulation on finger force has been inves-tigated mainly on a single finger, and only time-dependent increased target finger force has been detected in the finger force task.
OBJECTIVE: To explore the neural mechanism of finger force coordination in the motor cortex by observing the influence of various finger coordination patterns and patterns of transcranial magnetic stimulation (TMS)-induced finger force changes.
DESIGN, TIME AND SETTING: Neurophysiological and behavioral study was performed at the Biomedical Engineering Laboratory of Chongqing University from April to June 2008.
PARTICIPANTS: A total of 10 healthy, university students, comprising 5 males and 5 females, aged 21–23 years, voluntarily participated in this study. All participants were right-handed, with normal or corrected vision. Individuals with upper limb complaints or other musculoskeletal disorders were excluded.
METHODS: A target force-tracking task was conducted on the index finger, the index and middle fingers, and four fingers (index, middle, ring, and little), respectively. Target force trace in a single trial consisted of a 6-second ramp phase, a 20-second constant phase, and a 6-second drop phase. During experimentation, an unpredictable single-pulse TMS (120% motor threshold) was applied to the primary motor cortex (M1) in each phase.
MAIN OUTCOME MEASURES: Changes in peak force induced by TMS were obtained for each finger pattern during each force-tracking phase. Differences in force changes were tested between different finger patterns with regard to ramp, constant, and drop phases of target force.
RESULTS: Under ramp, constant, and drop phases of target force, the increase in magnetic stimulation-induced finger forces changes positively correlated with the number of fingers involved in the force tracking task. The magnetic stimulation-induced force changes from the index finger were less than the combination of the index and middle fingers or all four fingers under the corresponding target force, and the force changes from the combination of the index and middle fingers were less than all four fingers, i.e., index finger < index and middle fingers < four fingers.
CONCLUSION: Different neuromuscular mechanisms could be involved in finger force production for different finger combination patterns. Results from the present study suggested that independent motor neurons regulated individual finger force production.
Key Words: transcranial magnetic stimulation; finger force; peak force changes; finger combination

INTRODUCTION
  
Transcranial magnetic stimulation (TMS) induces cur-rents in the brain via a brief magnetic pulse to the scalp through a coil, which results in excitation or inhibition of superficial cortical neurons[1]. TMS promises to be a unique tool for corticomotor research, because it offers a unique opportunity to investigate the role of specific cor-tical areas of motor function in the human. The technique has been widely applied in motor control studies, as well as patients with neurological disorders[2-3].
TMS has been extensively used to explore motor func-tion of the human hand, and the effect of TMS on forces produced by one finger[4-5] or multi-fingers[6-7] is important topic for its applications. A single-pulse TMS applied to the scalp, overlaying the primary motor cortex (M1), will activate a fast conducting component of corticospinal outflow and produce a twitch contraction in forearm and hand muscles, which transiently increase the force ex-erted by the finger(s). A variety of studies have explored the characteristics of transiently increased finger forces evoked by TMS, as well as the correlations between force changes and corresponding motor evoked poten-tials (MEPs) in single-finger force production  tasks[8-9] or in the pinch grasp[6-7]. The majority of these studies have demonstrated that the size of MEP responses to TMS depends on the level of pre-existing electromyog-raphy activity. In addition, a few studies that investigated the effect of TMS in a multi-finger force production task suggested that a relatively high degree of physiological independence exists between fingers, due to force changes evoked by TMS[10-11]. TMS has been employed to examine TMS effects on M1 using a thumb-index-middle grasping task; results revealed that TMS evoked a synergistic increase in force magni-tudes[12]. Finger force changes, or motor evoked force (MEF), induced by TMS partially depend on background force level and TMS strength. In addition MEF magnitude positively correlates with TMS strength and force level exerted by finger(s).
Dexterity of the human hand relies on multi-finger force coordination and synergetic movement, which is achieved through a variety of finger combination pat-terns[13]. Finger activity is dominated through cortical control of distal hand muscle. However, convergence and divergence of descending pathways, as well as widespread finger representations in M1, result in exten-sive activity even during single-finger movement[14]. Early studies on control of contact forces in the human grasp focused on the pinch grasp, and an increasing number of recent studies have attempted to better understand how the central nervous system controls finger forces in multi-finger grasps[12, 15-18]. Digit-specific compartments in multi-digit extrinsic muscles of the hand possess a high degree of finger physiological independence[10], but the neuromuscular system leads to finger enslaving[19]. An animal neurophysiology study showed no fixed relation-ship between neuronal firing rate in M1 and force in a force production task[20-21]. However, a human functional MRI study utilizing grasping tasks demonstrated that average M1 activity increases with the production of large forces and higher precision or coordination de-mands[22]. Moreover, measurements of motor-related cortical potentials during finger force production revealed that neural activity in the index finger is larger than in the ring finger[23].  
A better understanding of finger coordination can assist motor function rehabilitation and robot finger design[24]. However, the neuromuscular mechanisms of various finger combination patterns remain poorly understood. The present study analyzed neuromuscular activity force patterns produced by single or multi-finger combinations. Moreover, a force-tracking trial, consisting of ramp, con-stant, and drop phases, was designed in which partici-pants were required to track a target force using different finger combinations. A single, unpredictable, TMS stimulus was applied to M1 in each phase. The peak force changes of different TMS-induced finger combina-tions were acquired. 

SUBJECTS AND METHODS

Design
TMS-based neurophysiological and behavioral study.
Time and setting
The study was performed at the Biomedical Engineering Laboratory of Chongqing University from April to June 2008.
Subjects
A total of 10 healthy students, comprising 5 males and 5 females, aged 21–23 years, were selected from Chongqing University. All participants were right-handed, with normal or corrected vision. Students with a history of upper extremity complaints or other musculoskeletal disorders were excluded. Written informed consent was obtained from all participants, and the study was ap-proved by the Ethics Committee of Chongqing University.
Methods
Apparatus and data collection
The experimental apparatus is shown in Figure 1. During testing, the subject was seated in a chair facing the testing table, with the right upper arm at approximately 45° abduction in the frontal plane and 45° flexion in the sagittal plane and the elbow at approximately 135° flex-ion. Four cell force sensors were used to measure forces produced by index, middle, ring, and/or little fingertip.
Real-time fingertip forces detected by the sensor were sampled by an USB-6008 multifunction DAQ card (National Instruments, USA) using Labview (National Instruments, USA), and the sample rate was set to     1 000 samples/second (sps). The signals were proc-essed using a laptop computer (Shenzhou Q351D, Hasens) with a CPU of 1.3 GHz. Focal magnetic stimuli with monophasic pulses and a maximal mag-netic field strength of 3.9 T were delivered using a MagPro Compact stimulator (Dendy, Denmark), and the MEPs were monitored using Keypoint-P (Dendy, Denmark). According to International Guidelines[25], motor threshold (MT) was defined as the minimal in-tensity of stimulator output capable of evoking a MEP > 50 μV with 50% probability while the target muscle was at rest. The stimulating coil, which was tangential to the scalp surface, corresponded to the primary motor cortex, and the handle was aimed backward at an angle of 45° from the midline. The optimal position was marked on an adherent bathing cap to assure that coil replacements were accurate across experimental sessions.

Experimental procedures
Maximal voluntary fingertip forces (MVF) were measured for index finger (I), combination of index and middle fin-gers (IM), and combination of index, middle, ring, and little fingers (IMRL). Each participant was required to build up to a maximum exertion during 3 seconds and maintain the exertion for 3 additional seconds with I, IM, or IMRL. Fingertip forces were then recorded. MVF was tested three times, and the maximal force output was selected.
The main task consisted of producing an accurate pat-tern of finger-press force using a certain finger combina-tion to track a three-phase-involved target force trace. The target force trace started at zero force level, which was represented as a horizontal line maintained for 1 second. Subsequently, a 6-second positive slope was used for the ramp phase, by the end of which the target force reached 30% MVF of corresponding task finger(s). In addition, a 20-second horizontal trace was utilized as a constant phase, and the subject was required to maintain a constant force level of 30% MVF with task finger(s). Finally, a 6-second drop phase of target force was designed, and the target force was completed at a zero force level. Each 32-second target force trace con-stituted a task trial, and a typical task trial is illustrated in Figure 2. The target force trace for each I, IM, and IMRL pattern was specified with its individual MVF.

During testing, the subjects were asked to press the force cell with the task finger(s) to track the target force trace, and the uninvolved finger(s) remained naturally flexed. The force sensor positions were adjusted for each subject according to finger size, and the forearm was fastened to avoid undesired movement. For each task finger pattern, the task finger(s) conducted 3 testing trials. A TMS of 120% MT was applied to M1 when the subject produced the precise target force in each phase. The combination order for finger pattern was pseudo- ran-domized across subjects. To avoid finger fatigue, a 5-minute rest interval was set between the two trials.  
MEF peak analysis
Force changes of individual finger and combined fingers were employed as indices to evaluate single TMS effects on finger force production. A typical trial of recorded force signal is illustrated in Figure 3, in which a TMS-induced brief force increased in ramp phase, constant phase, and drop phase. Similar to a previous study[26],  was defined as the peak force for finger pattern i and force phase j (where i = I, IM, or IMRL; j = ramp, constant, or drop), and  was defined as the corresponding background force for finger pattern i and phase j, which was measured at 100 ms prior to the moment of  . The peak of force change was calculated as
 
where i = I, IM, or IMRL, j = ramp, constant, or drop;  was used to quantify the TMS effect on individual or combined finger forces production. All recorded force productions of task finger(s) were reviewed trial-by-trial using Microsoft Excel.
Main outcome measures
The changes of peak force induced by TMS were ob-tained for each finger pattern during the force tracking phase. Differences in force changes were compared between various finger patterns in ramp, constant, and drop phases of target force.
Design, enforcement and evaluation
The experiment was designed and performed by Xiaoy-ing Wu and evaluated by Wensheng Hou.
Statistical analysis
SPSS 11.0 (SPSS, Chicago, IL, USA) was employed to analyze force changes of various finger patterns and target force phases. Paired t-test was used to compare within-phase performances of MEF among different pat-terns of task finger(s) and the within-finger-pattern per-formances of MEF among different phases. P < 0.05 was considered statistically significant.

RESULTS

Quantitative analysis of participants
Ten healthy participants were included in the final analy-sis.
TMS-induced force changes using different finger patterns and target force phases
As illustrated in Tables 1 and 2, the peak force changes for finger patterns of I, IM, and IMRL differed from each other in each phase. During ramp, constant, and drop phases, the peak force changes in the single index finger (I) were less than in the combined fingers IM or IMRL. The combined fingers IMRL exhibited the largest peak force changes in all three phases (P < 0.01).
 

DISCUSSION

The present study attempted to explore the target force pattern effect on single TMS-induced fingertip force changes. In the ramp, constant, or drop phase of target force, TMS resulted in force increases on single finger or combined fingers, and the finger combination patterns affected the MEF peaks.
TMS-induced increments in total force depended on the finger combination. In the three target force phases, peak force changes of I were less than in IM and IMRL, while IM peak changes were less than IMRL peak changes. That is, peak force change posi-tively correlated with the number of task fingers.
Studies have shown that a single TMS pulse to M1 should excite M1 neurons, and the excited neurons eventually adjust activation levels of hand muscle and force changes produced by task finger(s). When TMS is applied to M1, neurons responsible for hand movement become excited and the task finger(s) produces a burst contraction force[3]. Peak force changes significantly increase with an increasing number of TMS-excited neurons[12], and findings from the present study suggest that the number of TMS-excited neurons increased with the number of task fingers. In addition, a previous study reported that TMS of the motor cortex increases activity of currently activated pools of neurons[12]. The present study preliminarily concluded that an increased number of M1 neurons were involved in force-production when a combination of a greater number of fingers was used. TMS-induced force during motor imagery of maximal force production by the index finger has been shown to be smaller than during motor imagery of maximal force production by all four fingers simultaneously[27]. The present results demonstrate that peak force change positively cor-relates with the number of task fingers, which is con-sistent with previous results[28]. This shows that an increase in the number of master fingers correlated to greater TMS-induced responses.

Results from the present study support the hypothesis that different neuron pools or networks control activa-tion of an individual finger. When the index finger served as the task finger, only neurons serving the index finger were activated. In addition, TMS to M1 excited only currently activated M1 neurons and in-duced index finger contraction. When the combination of index and middle finger served as the task finger, neurons serving the middle finger were activated, and TMS to M1 induced contraction of the index and mid-dle finger. This was partially consistent with the hy-pothesis that tendons dominating individual fingers are served by physiologically independent muscles[26, 29], and there are independent motor neurons involved in individual finger force production. Moreover, digit-specific compartments in multi-digit extrinsic muscles of the hand are innervated by a sub-pool of alpha-motoneurons, which possesses a high degree of finger independent activity[10].
There were limitations to the present study. The par-ticipant number size was quite small. In addition, TMS depth and area, which was applied to M1, should be more accurately estimated, because these two factors could significantly affect the neural response of TMS.

Acknowledgments
The authors gratefully acknowledge the help of all volunteers who participated in this study.

REFERENCES

[1] Hallett M. Transcranial magnetic stimulation: a primer. Neuron. 2007;55(2):187-199.
[2] Rossi S, Rossini PM. TMS in cognitive plasticity and the potential for rehabilitation. Trends Cogn Sci. 2004;8(6):273-279.
[3] Rossini PM, Rossi S. Transcranial magnetic stimulation: diagnostic, therapeutic, and research potential. Neurology. 2007;68(7):484-488.
[4] Hess CW, Mills KR, Murray NM. Responses in small hand muscles from magnetic stimulation of the human brain. J Physiol. 1987;388:397-419.
[5] Ni Z, Liang N, Takahashi M, et al. Motor strategies and excitability changes of human hand motor area are dependent on different voluntary drives. Eur J Neurosci. 2006;23(12):3399-3406.
[6] Hasegawa Y, Kasai T, Tsuji T, et al. Further insight into the task-dependent excitability of motor evoked potentials in first dorsal interosseous muscle in humans. Exp Brain Res. 2001; 140(4):387-396.
[7] Hasegaw Y, Kasai T, Kinoshita H, et al. Modulation of a motor evoked response to transcranial magnetic stimulation by the activity level of the first dorsal interosseous muscle in humans when grasping a stationary object with different grip widths. Neurosci Lett. 2001;299(1-2):1-4.
[8] Rossini PM, Caramia MD, Iani C, et al. Magnetic transcranial stimulation in healthy humans: influence on the behavior of upper limb motor units. Brain Res. 1995;676(2):314-324.
[9] Wassermann EM, Tormos JM, Pascual-Leone A. Finger movements induced by transcranial magnetic stimulation change with hand posture, but not with coil position. Hum Brain Mapp. 1998;6(5-6):390-393.
[10] Danion F, Latash ML, Li S. Finger interactions studied with transcranial magnetic stimulation during multi-finger force production tasks. Clin Neurophysiol. 2003;114(8):1445-1455.
[11] Latash ML, Yarrow K, Rothwell JC. Changes in finger coordination and responses to single pulse TMS of motor cortex during practice of a multifinger force production task. Exp Brain Res. 2003;151(1):60-71.
[12] Baud-Bovy G, Prattichizzo D, Rossi S. Contact forces evoked by transcranial magnetic stimulation of the motor cortex in a multi-finger grasp. Brain Res Bull. 2008;75(6):723-736.
[13] Latash ML, Scholz JP, Sch?ner G. Toward a new theory of motor synergies. Motor Control. 2007;11(3):276-308.
[14] Schieber MH. Training and synchrony in the motor system. J Neurosci. 2002;22(13):5277-5281.
[15] Zatsiorsky VM, Latash ML. Prehension synergies. Exerc Sport Sci Rev. 2004;32(2):75-80.
[16] Zatsiorsky VM, Latash ML, Gao F, et al. The principle of superposition in human prehension. Robotica. 2004;22:231-234
[17] Davare M, Andres M, Cosnard G, et al. Dissociating the role of ventral and dorsal premotor cortex in precision grasping. J Neurosci. 2006;26(8):2260-2268.
[18] Li S. Perception of individual finger forces during multi-finger force production tasks. Neurosci Lett. 2006;409(3):239-243.
[19] Kim SW, Shim JK, Zatsiorsky VM, et al. Finger inter-dependence: linking the kinetic and kinematic variables. Hum Mov Sci. 2008; 27(3):408-422.
[20] Hepp-Reymond M, Kirkpatrick-Tanner M, Gabernet L, et al. Context-dependent force coding in motor and premotor cortical areas. Exp Brain Res. 1999;128(1-2):123-133.
[21] Morrow MM, Jordan LR, Miller LE. Direct comparison of the task-dependent discharge of M1 in hand space and muscle space. J Neurophysiol. 2007;97(2):1786-1798.
[22] Kuhtz-Buschbeck JP, Ehrsson HH, Forssberg H. Human brain activity in the control of fine static precision grip forces: an fMRI study. Eur J Neurosci. 2001;14(2):382-390.
[23] Slobounov S, Johnston J, Chiang H, et al. Motor-related cortical potentials accompanying enslaving effect in single versus combination of fingers force production tasks. Clin Neurophysiol. 2002;113(9):1444-1453.
[24] Oliveira MA, Hsu J, Park J, et al. Age-related changes in multi-finger interactions in adults during maximum voluntary finger force production tasks. Hum Mov Sci. 2008;27(5):714-727.
[25] Rossini PM, Barker AT, Berardelli A, et al. Non-invasive electrical and magnetic stimulation of the brain, spinal cord and roots: basic principles and procedures for routine clinical application. Report of an IFCN committee. Electroencephalogr Clin Neurophysiol. 1994;91(2):79-92.
[26] Schieber MH. Muscular production of individuated finger movements: the roles of extrinsic finger muscles. J Neurosci. 1995;15(1 Pt 1):284-297.
[27] Li S, Latash ML, Zatsiorsky VM. Effects of motor imagery on finger force responses to transcranial magnetic stimulation. Brain Res Cogn Brain Res. 2004;20(2):273-280.
[28] Danion F, Latash ML, Li S. Finger interactions studied with transcranial magnetic stimulation during multi-finger force production tasks. Clin Neurophysiol. 2003;114(8):1445-1455.
[29] Danion F, Li S, Zatsiorsky VM, Latash ML. Relations between surface EMG of extrinsic flexors and individual finger forces support the notion of muscle compartments. Eur J Appl Physiol. 2002;88(1-2):185-188.
 (Edited by Liu ZP, Xiao N/Su LL/Song LP)