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Voluntary Contraction Direction Dependence of Motor Unit Number Index in Patients with Amyotrophic Lateral Sclerosis Ping Zhou*, Senior Member, IEEE, Sanjeev D. Nandedkar, and Paul E. Barkhaus
Abstract—We investigated the voluntary contraction direction dependence of motor unit number index (MUNIX) for multifunctional muscles in patients with amyotrophic lateral sclerosis (ALS). The MUNIX technique was applied in nine first dorsal interosseous muscles of eight ALS subjects, using surface electromyography (EMG) signals from index finger abduction and flexion, respectively. In seven examined muscles, the MUNIX derived from the index finger abduction mode was smaller than that from the flexion mode. For the remaining two muscles, one had the same MUNIX; the other showed an abduction mode MUNIX much higher than the flexion mode MNUIX. Across all muscles, the median MUNIX was 96 for the index finger abduction mode and 161 for the flexion mode. The findings reveal the dependence of multifunctional muscle MUNIX on voluntary contraction directions in ALS patients. Based on this analysis, we further explored the concept of “multidimensional MUNIX” for an appropriate performance or interpretation of MUNIX in multifunctional muscles of ALS patients. An effort towards such a development was presented using both abduction and flexion mode surface EMG for MUNIX calculation. Index Terms—Amyotrophic lateral sclerosis (ALS), first dorsal interosseous (FDI) muscle, M wave, motor unit number index (MUNIX), surface electromyography (EMG).
I. INTRODUCTION
M
OTOR unit number estimation (MUNE) has been extensively used in basic and clinical neurophysiology since its introduction in 1971 [1], [2]. Conventional MUNE and its various forms of improvement require recording of maximum M wave or compound muscle action potential (CMAP) and the
Manuscript received July 23, 2013; revised December 11, 2013; accepted January 12, 2014. Date of publication April 01, 2014; date of current version September 04, 2014. This work was supported in part the National Institute on Disability and Rehabilitation Research of the United States Department of Education under Grant H133G090093. P. Zhou is with the Biomedical Engineering Program, University of Science and Technology of China, Hefei, 230027, China, and also with the Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, and TIRR Memorial Hermann Research Center, Houston, TX 77030 USA (e-mail: [email protected]). S. D. Nandedkar is with Natus Medical Inc., Middleton, WI 53562 USA (e-mail: [email protected]). P. E. Barkhaus is with Milwaukee Veterans Administration Medical Center and Medical College of Wisconsin, Milwaukee, WI 53226 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TNSRE.2014.2314391
estimates of single motor unit action potential size. The latter can be estimated from either incremental nerve stimulation or electromyography (EMG) decomposition based spike triggered averaging techniques, both potentially laborious and time-consuming or invasive causing pain. To overcome this difficulty, a motor unit number index (MUNIX) technique was developed to use different levels of voluntary surface EMG signals and maximum M wave recording to derive an index that is proportional to the number of motor units in a muscle [3], [4]. The primary feature of the MUNIX technique is that it induces minimal discomfort and can be performed quickly. The MUNIX measurement also demonstrates good repeatability [5]–[8]. Because of these advantages, the MUNIX has achieved increasing applications in detecting motor neuron loss and measuring disease progression in amyotrophic lateral sclerosis (ALS) [5], [6], [9]. The MUNIX has also been used to assess motor unit changes in other neurological disorder or aging studies [10]–[15]. Recently, the sensitivity of the MUNIX to changes in motor neuron and muscle properties was explored by a simulation approach. The findings suggest that the MUNIX is most suitable for motor neuron diseases that demonstrate evidence of muscle fiber reinnervation [16]. The validity of the MUNIX measurement in ALS patients was also confirmed by direct experimental comparisons between MUNE and MUNIX techniques [17]–[19]. The MUNIX technique requires recording of different levels of voluntary surface EMG signals. For muscles that exert forces about a single degree-of-freedom joint, the protocol for voluntary surface EMG recording is straightforward. However, when MUNIX is performed in multifunctional muscles (e.g., biceps brachii, deltoid, and the interossei muscles), there is a practical issue of voluntary task selection, since these muscles are capable of exerting forces in multiple directions about their respective joints. A previous study has assessed how the different lines of force activation may influence MUNIX measurement of neurologically intact muscles [20]. Given the fact that the primary target population of MUNIX is ALS, the previous study is limited by lack of assessing ALS patients. As a result, it is presently unknown how the MUNIX of ALS patients might be affected by different voluntary contraction directions. The objective of this study was to assess directional dependence of MUNIX in multifunctional muscles of ALS patients. The first dorsal interosseous (FDI) muscle was examined, which is a primary abductor and synergistic flexor about the second metacarpophalangeal (MCP) joint. Surface EMG signals of the FDI muscle generated from index finger abduction and flexion
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ZHOU et al.: VOLUNTARY CONTRACTION DIRECTION DEPENDENCE OF MOTOR UNIT NUMBER INDEX
were respectively used to calculate the MUNIX of ALS patients. We assessed whether and how the MUNIX may vary with direction of force generation in ALS patients. Furthermore, by combining both abduction and flexion mode surface EMG for MUNIX calculation, we explored the concept of “multidimensional MUNIX” for an appropriate performance or interpretation of MUNIX in multifunctional muscles of ALS patients. II. METHODS A. Subjects Eight subjects (five male, three female, 60.1 11.5 years) with the diagnosis of probable ALS or definite ALS (El Escorial criteria [21]) participated in this study. For seven subjects, the FDI muscle of one hand was tested. For one subject, the testing was performed bilaterally. This resulted in nine muscles being studied in total. The data recording of the study was performed in the last author’s institute, approved by the local Institutional Review Board. All the subjects gave their written consent before the experiment. B. Experimental Protocol The studies were performed using disposable surface electrodes (Natus Medical Inc., Middleton, WI, USA) and a Synergy EMG system (Natus Medical Inc.). Electrode placement was similar to that for standard ulnar motor studies. The active surface electrode was positioned over the motor point of the FDI muscle and the reference electrode was positioned over the base of the thumb. This montage gives the desired initially upward deflection of the CMAP. An adhesive ground electrode was placed on the back of the hand. The maximum M wave or CMAP was first recorded. The ulnar nerve was stimulated approximately 2 cm proximal to the wrist crease. To ensure that the M wave amplitude was maximized, the electrode placement was optimized by testing several different locations. After the maximum M wave recording, the voluntary surface EMG signals were recorded from the FDI muscle while the subject generated an isometric muscle contraction at five different levels (roughly corresponding to 10%, 25%, 50%, submaximal and maximal voluntary effort) in two different directions (i.e., index finger abduction or flexion). Each level of surface interference pattern (SIP) EMG lasted approximately 300 ms. For each direction, the force was not measured. Instead, the force levels were defined by the operator offering resistance to help the subject produce different force levels. For all the subjects, the M wave was recorded using a band-pass filter setting at 3–10000 Hz. The voluntary surface EMG signal was recorded using a band-pass filter setting at 10–1000 Hz. C. Data Analysis The maximum M wave and different levels of surface SIP EMG were used to compute the MUNIX for the FDI muscle. The detailed MUNIX derivation can be found in [3], [4]. In brief, the area and power of the maximum M wave, and the area and power of each level of SIP EMG (for 1 s) were first calculated. These values were used to compute the “ideal case motor unit count (ICMUC)” defined as
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. It follows that each level of SIP resulted in an ICMUC. Regression analysis was then used to define the relation between the SIP area and the ICMUC by an exponential fitting: . The parameters and were obtained from the regression using different levels of SIP EMG. Finally, the SIP area of 20 mVms was used to compute the MUNIX from the established exponential fitting. During the MUNIX calculation four criteria were imposed to ensure validity of the results [4]: 1) ; 2) ; 3) ; and 4) . The above calculation was performed using analysis software written by the second author. The FDI muscle MUNIX was calculated using SIP EMG from index finger abduction and flexion, respectively. Furthermore, we also calculated the MUNIX values by combining SIP EMG from both index finger abduction and flexion. We examined whether and how the MUNIX was dependent on different directions of muscle activation. Due to the relatively small sample size , the Wilcoxon signed-rank test was used for statistical analysis. III. RESULTS For all the examined muscles, exponential regression analysis showed a good fitting for the relationship between SIP area and ICMUC (Figs. 1 and 2). In seven examined muscles, the MUNIX derived from the index finger abduction mode (median 96, range 12–311) was smaller than that from the flexion mode (median 161, range 17–382) ; and the MUNIX from both directions (median 143, range 19–354) was lower than the flexion but higher than the abduction mode . Fig. 1 shows a representative example of the FDI muscle MUNIX calculation in an ALS subject, where the maximum M wave, different levels of SIP EMG during index finger abduction and flexion, and MUNIX from the different directions are demonstrated, respectively. It is noted that the voluntary surface EMG generated by the FDI muscle flexion mode was relatively small compared with that from the abduction mode [Fig. 1(a)]. With the measured maximum M wave and different levels of SIP signals this ALS subject showed a MUNIX of 141 for the FDI muscle abduction mode, and 165 for the flexion mode [Fig. 1(b)]. The MUNIX changed to 156 when SIPs from both abduction and flexion modes were used for calculation [Fig. 1(c)]. For the remaining two muscles, one had the same MUNIX (9) for all the three conditions (abduction, flexion, and their combination). The other showed an abduction mode MUNIX much higher than the flexion mode MUNIX. Fig. 2 shows the MUNIX calculation in this muscle. It was observed that although the M wave amplitude was 15.2 mV, the voluntary surface EMG was poorly activated, particularly during index finger abduction [Fig. 2(a)]. The combination of M wave and SIP EMG signals resulted in a MUNIX of 278 for the index finger abduction mode and 193 for the index finger flexion mode [Fig. 2(b)]. Using voluntary surface EMG from both directions, the MUNIX of this muscle was 237 [Fig. 2(c)]. Table I summarizes the MUNIX calculation for all the examined FDI muscles. Across all muscles, the median MUNIX was 96 (range 9–311) for the index finger abduction mode, 161
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Fig. 2. FDI MUNIX calculation of the ALS subject that showed higher MUNIX using index finger abduction than the flexion mode. (a) CMAP and poorly activated voluntary surface EMG in index finger abduction. (b) MUNIX calculation using abduction and flexion mode, respectively. (c) MUNIX calculation using both abduction and flexion modes.
Fig. 1. FDI MUNIX calculation of an ALS subject that showed lower MUNIX using the index finger abduction than the flexion mode. (a) CMAP and voluntary surface EMG from index finger abduction and flexion, respectively. (b) MUNIX calculation using abduction and flexion mode EMG, respectively. (c) MUNIX calculation using both abduction and flexion modes.
TABLE I COMPARISON OF THE FDI MUSCLE MUNIX OF ALS PATIENTS, DERIVED FROM VOLUNTARY SURFACE EMG SIGNALS DURING INDEX FINGER ABDUCTION, FLEXION AND BOTH DIRECTIONS
(range 9–382) for the flexion mode, and 143 (range 9–354) for both directions, respectively . IV. DISCUSSION The primary target population of the development of MUNIX technique is ALS patients. This study addresses a practical question of voluntary muscle activation for calculation of MUNIX in multifunctional muscles of ALS patients. This is important considering that our body has many muscles (e.g., biceps brachii, deltoid, and the interossei muscles) that can produce forces in multiple directions about the respective joint. For these multifunctional muscles, the force generation in the desired direction can be realized by choosing groups of motor units that are
tuned selectively or by pairing antagonist muscles or parts of the muscle against each other [22]–[26]. The FDI muscle was examined in this study, whose plane of force production about the MCP joint primarily consists of
ZHOU et al.: VOLUNTARY CONTRACTION DIRECTION DEPENDENCE OF MOTOR UNIT NUMBER INDEX
linear combinations of abduction and flexion of the index finger. Thus the choice of directions was focused on pure index finger abduction and flexion, respectively. The dependence of MUNIX on voluntary contraction directions of the FDI muscle was previously examined in 15 healthy control subjects [20]. Across all subjects, the average MUNIX value of the FDI muscle was 228 45 for index finger abduction, slightly smaller than the MUNIX estimate of 251 56 for index finger flexion. As would be expected, the MUNIX estimates from the ALS subjects are significantly less than those from normal control subjects. We also note that the MUNIX variability of ALS subjects is much higher than that of the normal control subjects. This may be due to different disease progress stages or impairments of the tested ALS subjects. For most of the examined muscles in ALS subjects, the findings were consistent with those from the neurologically intact subjects [20] that the MUNIX using the index finger flexion mode was higher than the MUNIX using the abduction mode, except for two ALS subjects, one having the same MUNIX and the other demonstrating an opposite trend. The MUNIX calculation is based on exponential curve fitting using the relation between the CMAP and different levels SIP EMG signals. During our calculation of MUNIX for the FDI muscle using index finger abduction or flexion, the M wave was the same. Thus, the MUNIX difference was induced by different SIP EMG signals from the index finger abduction and flexion. The higher FDI muscle MUNIX derived from index finger flexion compared with abduction was primarily due to the relatively small surface EMG amplitude or area during the flexion mode in comparison with the abduction mode [27]. Different from the abduction force produced at the MCP joint that is generated largely by the FDI muscle, the flexion force of the index finger involves several other muscles (e.g., the flexor digitorum profundus and flexor digitorum superficialis muscles) in addition to the FDI muscle [28], [29]. It was also reported that in the FDI muscle the groups of motor units more suitable for abduction are located relatively superficial within the muscle compared with those motor units more suitable for flexion [30]. Because of these factors, the surface EMG amplitude in the flexion mode can be lower than that in the abduction mode. Since MUNIX computation derives an index proportional to the number of motor units in a muscle (rather than a direct estimation of the number motor units, as provided by MUNE methods), one principle for MUNIX application is to compare the MUNIX changes in different situations, and the same definition for all parameters should be used throughout the comparison study. According to this principle, when MUNIX methods are used in ALS patients, the direction of contraction should be considered for multifunctional muscles, so the MUNIX comparison will not be compromised by the difference induced from varying directions. For the muscle showing that abduction MUNIX was higher than flexion MUNIX, it was observed that although with relatively large or normal CMAP, the muscle was poorly activated voluntarily, particularly for index finger abduction. It implies that in ALS, motor neurons innervating a multifunctional muscle may be selectively degenerated, or the central nervous system may experience selective impairment in activation of functionally useful groups of motor
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units. As a result, parts of the muscle may be more severely affected, and the ALS subject may fail to perform contraction in a specific direction. It follows that for multifunctional muscles, a potential limitation of applying MUNIX technique is that the CMAP measures the contribution of both subpopulations of motor units, while the voluntary EMG may primarily measure one subpopulation or the other. To reduce potential artifact or to more reliably reflect motor unit number changes of ALS patients, a “multidimensional MUNIX” may be needed that utilizes surface EMG in different directions at once. Such an approach may result in more consistent MUNIX estimates than those from one direction. This can be realized by using voluntary surface EMG from both abduction and flexion as performed in this study, or using surface EMG in a combined direction (45 in the flexion–abduction plane) for MUNIX calculation. Alternatively, if the contribution of the CMAP to different directions can be estimated by recording the muscle twitch, it is feasible to calculate the direction specific MUNIX using the CMAP and voluntary surface EMG solely for that direction. A standard laboratory setup with a multiple-degree of freedoms load cell is required for such an approach, making it not appropriate for clinical application. Nonetheless, the approach is still worth of exploring to help a comprehensive and appropriate understanding of the MUNIX application in multifunctional muscles. REFERENCES [1] A. J. McComas, P. R. Fawcett, M. J. Campbell, and R. E. Sica, “Electrophysiological estimation of the number of motor units within a human muscle,” J. Neurol. Neurosurg. Psychiatry, vol. 34, pp. 121–131, 1971. [2] M. B. Bromberg, “Updating motor unit number estimation (MUNE),” Clin. Neurophysiol., vol. 118, pp. 1–8, 2007. [3] S. D. Nandedkar, D. S. Nandedkar, P. E. Barkhaus, and E. V. Stalberg, “Motor unit number index (MUNIX),” IEEE Trans. Biomed. Eng., vol. 51, no. 12, pp. 2209–2211, Dec. 2004. [4] S. D. Nandedkar, P. E. Barkhaus, and E. V. Stalberg, “Motor unit number index (MUNIX): Principle, method, findings in healthy subjects and in patients with motor neuron disease,” Muscle Nerve, vol. 42, pp. 798–807, 2010. [5] S. W. Ahn, S. H. Kim, J. E. Kim, S. M. Kim, S. H. Kim, K. S. Park, J. J. Sung, K. W. Lee, and Y. H. Hong, “Reproducibility of the motor unit number index (MUNIX) in normal controls and amyotrophic lateral sclerosis patients,” Muscle Nerve, vol. 42, pp. 808–813, 2010. [6] S. D. Nandedkar, P. E. Barkhaus, and E. V. Stålberg, “Reproducibility of MUNIX in patients with amyotrophic lateral sclerosis,” Muscle Nerve, vol. 44, pp. 919–922, 2011. [7] C. Neuwirth, S. Nandedkar, E. Stålberg, P. E. Barkhaus, M. Carvalho, J. Furtula, J. P. van Dijk, R. Baldinger, J. Castro, J. Costa, M. Otto, A. Sandberg, and M. Weber, “Motor Unit Number Index (MUNIX): A novel neurophysiological marker for neuromuscular disorders; testretest reliability in healthy volunteers,” Clin. Neurophysiol., vol. 122, pp. 1867–1872, 2011. [8] C. Neuwirth, S. Nandedkar, E. Stålberg, P. E. Barkhaus, M. Carvalho, J. Furtula, J. P. van Dijk, R. Baldinger, J. Castro, J. Costa, M. Otto, A. Sandberg, and M. Weber, “Motor Unit Number Index (MUNIX): Reference values of five different muscles in healthy subjects from a multi-centre study,” Clin. Neurophysiol., vol. 122, pp. 1895–1898, 2011. [9] C. Neuwirth, S. Nandedkar, E. Stalberg, and M. Weber, “Motor unit number index (MUNIX): A novel neurophysiological technique to follow disease progression in amyotrophic lateral sclerosis,” Muscle Nerve, vol. 42, pp. 379–384, 2010. [10] A. Sandberg, S. D. Nandedkar, and E. Stålberg E, “Macro electromyography and motor unit number index in the tibialis anterior muscle: Differences and similarities in characterizing motor unit properties in prior polio,” Muscle Nerve, vol. 43, pp. 335–341, 2011.
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[11] X. Li, Y. C. Wang, N. L. Suresh, W. Z. Rymer, and P. Zhou, “Motor unit number reductions in paretic muscles of stroke survivors,” IEEE Trans. Inf. Technol. Biomed., vol. 15, no. 4, pp. 505–512, Jul. 2011. [12] X. Li, W. Z. Rymer, G. Li, and P. Zhou, “The effects of notch filtering on electrically evoked myoelectric signals and associated motor unit index estimates,” J. Neuroeng. Rehabil., vol. 8, no. 64, 2011. [13] X. Li, F. Jahanmiri-Nezhad, W. Z. Rymer, and P. Zhou, “An examination of the motor unit number index (MUNIX) in muscles paralyzed by spinal cord injury,” IEEE Trans. Inf. Technol. Biomed., vol. 16, no. 6, pp. 1143–1149, Nov. 2012. [14] M. Drey, C. Grösch, C. Neuwirth, J. M. Bauer, and C. C. Sieber, “The motor unit number index (MUNIX) in sarcopenic patients,” Exp. Gerontol., vol. 48, pp. 381–384, 2013. [15] R. D. Kaya, M. Nakazawa, R. L. Hoffman, and B. C. Clark, “Interrelationship between muscle strength, motor units, aging,” Exp. Gerontol., vol. 48, pp. 920–925, 2013. [16] X. Li, W. Z. Rymer, and P. Zhou, “A simulation-based analysis of motor unit number index (MUNIX) technique using motoneuron pool and surface electromyogram models,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 20, no. 3, pp. 297–304, May 2012. [17] W. A. Boekestein, H. J. Schelhaas, M. J. van Putten, D. F. Stegeman, M. J. Zwarts, and J. P. van Dijk, “Motor unit number index (MUNIX) versus motor unit number estimation (MUNE): A direct comparison in a longitudinal study of ALS patients,” Clin. Neurophysiol., vol. 123, pp. 1644–1649, 2012. [18] J. Furtula, B. Johnsen, P. B. Christensen, K. Pugdahl, C. Bisgaard, M. K. Christensen, J. Arentsen, M. Frydenberg, and A. Fuglsang-Frederiksen, “MUNIX and incremental stimulation MUNE in ALS patients and control subjects,” Clin. Neurophysiol., vol. 124, pp. 610–618, 2013. [19] M. B. Bromberg, “MUNIX and MUNE in ALS,” Clin. Neurophysiol., vol. 124, pp. 433–434, 2013. [20] P. Zhou, X. Li, and W. Z. Rymer, “Computing motor unit number index of the first dorsal interosseous muscle with two different contraction tasks,” Med. Eng. Phys., vol. 34, pp. 1209–1212, 2012. [21] B. R. Brooks, R. G. Miller, M. Swash, and T. L. Munsat, World Federation of Neurology Research Group on Motor Neuron Diseases, “El Escorial revisited: Revised criteria for the diagnosis of amyotrophic lateral sclerosis,” Amyotroph. Lateral Scler. Other Motor Neuron Disord., vol. 1, pp. 293–299, 2000. [22] U. Herrman and M. Flanders, “Directional tuning of single motor units,” J. Neurosci., vol. 18, pp. 8402–8416, 1998. [23] B. M. Ter Haar Romeny, J. J. van der Gon, and C. C. Gielen, “Relation between location of a motor unit in the human biceps brachii and its critical firing levels for different tasks,” Exp. Neurol., vol. 85, pp. 631–650, 1984. [24] M. Theeuwen, C. C. Gielen, L. E. Miller, and C. Doorenbosch, “The relation between the direction dependence of electromyographic amplitude and motor unit recruitment thresholds during isometric contractions,” Exp. Brain Res., vol. 98, pp. 488–500, 1994. [25] C. K. Thomas, B. H. Ross, and R. B. Stein, “Motor unit recruitment in human first dorsal interosseous muscle for static contractions in three different directions,” J. Neurophysiol., vol. 55, pp. 1017–1029, 1986. [26] I. Zijdewind, D. Kernell, and C. G. Kukulka, “Spatial differences in fatigue-associated electromyographic behavior of the human first dorsal interosseous muscle,” J. Physiol., vol. 483, pt. 2, pp. 499–509, 1995. [27] P. Zhou, N. L. Suresh, and W. Z. Rymer, “Surface electromyogram analysis of the direction of isometric torque generation by the first dorsal interosseous muscle,” J. Neural Eng., vol. 8, no. 3, p. 036028, 2011. [28] Z. M. Li, V. M. Zatsiorsky, and M. L. Latash, “The effect of finger extensor mechanism on the flexor force during isometric tasks,” J. Biomech., vol. 34, pp. 1097–1102, 2001. [29] Z. M. Li, H. J. Pfaeffle, D. G. Sotereanos, R. J. Goitz, and S. L. Woo, “Multi-directional strength and force envelope of the index finger,” Clin. Biomech. (Bristol, Avon), vol. 18, pp. 908–915, 2003. [30] N. L. Suresh, A. D. Kuo, C. J. Heckman, M. D. Ellis, and W. Z. Rymer, “Correlation of mechanical action with directional tuning in the first dorsal interosseous (FDI),” presented at the XIVth Congr. Int. Soc. Electrophysiol. Kinesiol., Vienna, Austria, Jun. 22–25, 2002.
Ping Zhou (S’01–M’05–SM’07) received the B.S. degree in electrical engineering and the M.S. degree in biomedical engineering from the University of Science and Technology of China, Hefei, China, in 1995 and 1999, respectively, and the Ph.D. degree in biomedical engineering from Northwestern University, Evanston, IL, USA, in 2004. From 1999 to 2014, he was progressively a Research Assistant, Research Associate, and (full and part time) Research Faculty at the Rehabilitation Institute of Chicago, Chicago, IL, USA. He was also an Adjunct Research Assistant and later Associate Professor in Physical Medicine and Rehabilitation of Northwestern University, Evanston, IL, USA, from 2006 to 2014. He currently holds an Adjunct Associate Professor position in Physical Medicine and Rehabilitation at the University of Texas Health Science Center at Houston, TX, USA. He directs the Neuromyoelectric Engineering for Rehabilitation laboratory housed in the outpatient clinic at TIRR Memorial Hermann Research Center. He is also affiliated with the biomedical engineering program of the University of Science and Technology of China. His research interests include biomedical signal (in particular, EMG) processing, motor unit pathophysiology and electrodiagnosis, myoelectric control, and assistive devices for neurorehabilitation.
Sanjeev D. Nandedkar received the Ph.D. degree in biomedical engineering from the University of Virginia, Charlottesville, VA, USA. He continued his research at the Duke University Medical Center before joining the TECA Corporation. Currently he is a Senior Consultant at Natus Medical Inc., Middleton, WI, USA, where he is involved in design, testing, and training for new electrodiagnostic instruments. He is actively conducting research in quantitative electromyography, signal processing, and study of the number of motor units. He has co-developed techniques such as EQUIP and MUNIX. He has lectured and conducted workshops in many countries. He also edits the EMG on DVD series of educational videos. Dr. Nandedkar received the “Distinguished Service” award by the American Association of Neuromuscular and Electrodiagnostic Medicine.
Paul E. Barkhaus received the B.S. and M.D. degrees from Wayne State University, Detroit, MI, USA. He completed his training in neurology at Wayne State University in 1978, followed by fellowships in clinical neuromuscular diseases at the University of Arizona, and two fellowships in electromyography at the University of Minnesota and Duke University. He has been on faculty at the Medical College of Wisconsin, Milwaukee, WI, USA, since 1993. He was appointed Professor of Neurology in 2002, and awarded tenure in 2011. He developed the Amyotrophic Lateral Sclerosis (Lou Gehrig Disease) Program in the late 1990s and it is currently one of almost 40 centers of the ALS Association’s Centers of Excellence in the USA. He also serves as the Department of Neurology’s Program Director for Clinical neurophysiology and is Section Chief for Neuromuscular and Autonomic Disorders. His current research interests are in clinical neurophysiology (electromyography and electroneurography) and amyotrophic lateral sclerosis.
IEEE TRANSACTIONS ON NEURAL SYSTEMS AND REHABILITATION ENGINEERING, VOL. 22, NO. 5, SEPTEMBER 2014
Voluntary Contraction Direction Dependence of Motor Unit Number Index in Patients with Amyotrophic Lateral Sclerosis Ping Zhou*, Senior Member, IEEE, Sanjeev D. Nandedkar, and Paul E. Barkhaus
Abstract—We investigated the voluntary contraction direction dependence of motor unit number index (MUNIX) for multifunctional muscles in patients with amyotrophic lateral sclerosis (ALS). The MUNIX technique was applied in nine first dorsal interosseous muscles of eight ALS subjects, using surface electromyography (EMG) signals from index finger abduction and flexion, respectively. In seven examined muscles, the MUNIX derived from the index finger abduction mode was smaller than that from the flexion mode. For the remaining two muscles, one had the same MUNIX; the other showed an abduction mode MUNIX much higher than the flexion mode MNUIX. Across all muscles, the median MUNIX was 96 for the index finger abduction mode and 161 for the flexion mode. The findings reveal the dependence of multifunctional muscle MUNIX on voluntary contraction directions in ALS patients. Based on this analysis, we further explored the concept of “multidimensional MUNIX” for an appropriate performance or interpretation of MUNIX in multifunctional muscles of ALS patients. An effort towards such a development was presented using both abduction and flexion mode surface EMG for MUNIX calculation. Index Terms—Amyotrophic lateral sclerosis (ALS), first dorsal interosseous (FDI) muscle, M wave, motor unit number index (MUNIX), surface electromyography (EMG).
I. INTRODUCTION
M
OTOR unit number estimation (MUNE) has been extensively used in basic and clinical neurophysiology since its introduction in 1971 [1], [2]. Conventional MUNE and its various forms of improvement require recording of maximum M wave or compound muscle action potential (CMAP) and the
Manuscript received July 23, 2013; revised December 11, 2013; accepted January 12, 2014. Date of publication April 01, 2014; date of current version September 04, 2014. This work was supported in part the National Institute on Disability and Rehabilitation Research of the United States Department of Education under Grant H133G090093. P. Zhou is with the Biomedical Engineering Program, University of Science and Technology of China, Hefei, 230027, China, and also with the Department of Physical Medicine and Rehabilitation, University of Texas Health Science Center at Houston, and TIRR Memorial Hermann Research Center, Houston, TX 77030 USA (e-mail: [email protected]). S. D. Nandedkar is with Natus Medical Inc., Middleton, WI 53562 USA (e-mail: [email protected]). P. E. Barkhaus is with Milwaukee Veterans Administration Medical Center and Medical College of Wisconsin, Milwaukee, WI 53226 USA (e-mail: [email protected]). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TNSRE.2014.2314391
estimates of single motor unit action potential size. The latter can be estimated from either incremental nerve stimulation or electromyography (EMG) decomposition based spike triggered averaging techniques, both potentially laborious and time-consuming or invasive causing pain. To overcome this difficulty, a motor unit number index (MUNIX) technique was developed to use different levels of voluntary surface EMG signals and maximum M wave recording to derive an index that is proportional to the number of motor units in a muscle [3], [4]. The primary feature of the MUNIX technique is that it induces minimal discomfort and can be performed quickly. The MUNIX measurement also demonstrates good repeatability [5]–[8]. Because of these advantages, the MUNIX has achieved increasing applications in detecting motor neuron loss and measuring disease progression in amyotrophic lateral sclerosis (ALS) [5], [6], [9]. The MUNIX has also been used to assess motor unit changes in other neurological disorder or aging studies [10]–[15]. Recently, the sensitivity of the MUNIX to changes in motor neuron and muscle properties was explored by a simulation approach. The findings suggest that the MUNIX is most suitable for motor neuron diseases that demonstrate evidence of muscle fiber reinnervation [16]. The validity of the MUNIX measurement in ALS patients was also confirmed by direct experimental comparisons between MUNE and MUNIX techniques [17]–[19]. The MUNIX technique requires recording of different levels of voluntary surface EMG signals. For muscles that exert forces about a single degree-of-freedom joint, the protocol for voluntary surface EMG recording is straightforward. However, when MUNIX is performed in multifunctional muscles (e.g., biceps brachii, deltoid, and the interossei muscles), there is a practical issue of voluntary task selection, since these muscles are capable of exerting forces in multiple directions about their respective joints. A previous study has assessed how the different lines of force activation may influence MUNIX measurement of neurologically intact muscles [20]. Given the fact that the primary target population of MUNIX is ALS, the previous study is limited by lack of assessing ALS patients. As a result, it is presently unknown how the MUNIX of ALS patients might be affected by different voluntary contraction directions. The objective of this study was to assess directional dependence of MUNIX in multifunctional muscles of ALS patients. The first dorsal interosseous (FDI) muscle was examined, which is a primary abductor and synergistic flexor about the second metacarpophalangeal (MCP) joint. Surface EMG signals of the FDI muscle generated from index finger abduction and flexion
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ZHOU et al.: VOLUNTARY CONTRACTION DIRECTION DEPENDENCE OF MOTOR UNIT NUMBER INDEX
were respectively used to calculate the MUNIX of ALS patients. We assessed whether and how the MUNIX may vary with direction of force generation in ALS patients. Furthermore, by combining both abduction and flexion mode surface EMG for MUNIX calculation, we explored the concept of “multidimensional MUNIX” for an appropriate performance or interpretation of MUNIX in multifunctional muscles of ALS patients. II. METHODS A. Subjects Eight subjects (five male, three female, 60.1 11.5 years) with the diagnosis of probable ALS or definite ALS (El Escorial criteria [21]) participated in this study. For seven subjects, the FDI muscle of one hand was tested. For one subject, the testing was performed bilaterally. This resulted in nine muscles being studied in total. The data recording of the study was performed in the last author’s institute, approved by the local Institutional Review Board. All the subjects gave their written consent before the experiment. B. Experimental Protocol The studies were performed using disposable surface electrodes (Natus Medical Inc., Middleton, WI, USA) and a Synergy EMG system (Natus Medical Inc.). Electrode placement was similar to that for standard ulnar motor studies. The active surface electrode was positioned over the motor point of the FDI muscle and the reference electrode was positioned over the base of the thumb. This montage gives the desired initially upward deflection of the CMAP. An adhesive ground electrode was placed on the back of the hand. The maximum M wave or CMAP was first recorded. The ulnar nerve was stimulated approximately 2 cm proximal to the wrist crease. To ensure that the M wave amplitude was maximized, the electrode placement was optimized by testing several different locations. After the maximum M wave recording, the voluntary surface EMG signals were recorded from the FDI muscle while the subject generated an isometric muscle contraction at five different levels (roughly corresponding to 10%, 25%, 50%, submaximal and maximal voluntary effort) in two different directions (i.e., index finger abduction or flexion). Each level of surface interference pattern (SIP) EMG lasted approximately 300 ms. For each direction, the force was not measured. Instead, the force levels were defined by the operator offering resistance to help the subject produce different force levels. For all the subjects, the M wave was recorded using a band-pass filter setting at 3–10000 Hz. The voluntary surface EMG signal was recorded using a band-pass filter setting at 10–1000 Hz. C. Data Analysis The maximum M wave and different levels of surface SIP EMG were used to compute the MUNIX for the FDI muscle. The detailed MUNIX derivation can be found in [3], [4]. In brief, the area and power of the maximum M wave, and the area and power of each level of SIP EMG (for 1 s) were first calculated. These values were used to compute the “ideal case motor unit count (ICMUC)” defined as
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. It follows that each level of SIP resulted in an ICMUC. Regression analysis was then used to define the relation between the SIP area and the ICMUC by an exponential fitting: . The parameters and were obtained from the regression using different levels of SIP EMG. Finally, the SIP area of 20 mVms was used to compute the MUNIX from the established exponential fitting. During the MUNIX calculation four criteria were imposed to ensure validity of the results [4]: 1) ; 2) ; 3) ; and 4) . The above calculation was performed using analysis software written by the second author. The FDI muscle MUNIX was calculated using SIP EMG from index finger abduction and flexion, respectively. Furthermore, we also calculated the MUNIX values by combining SIP EMG from both index finger abduction and flexion. We examined whether and how the MUNIX was dependent on different directions of muscle activation. Due to the relatively small sample size , the Wilcoxon signed-rank test was used for statistical analysis. III. RESULTS For all the examined muscles, exponential regression analysis showed a good fitting for the relationship between SIP area and ICMUC (Figs. 1 and 2). In seven examined muscles, the MUNIX derived from the index finger abduction mode (median 96, range 12–311) was smaller than that from the flexion mode (median 161, range 17–382) ; and the MUNIX from both directions (median 143, range 19–354) was lower than the flexion but higher than the abduction mode . Fig. 1 shows a representative example of the FDI muscle MUNIX calculation in an ALS subject, where the maximum M wave, different levels of SIP EMG during index finger abduction and flexion, and MUNIX from the different directions are demonstrated, respectively. It is noted that the voluntary surface EMG generated by the FDI muscle flexion mode was relatively small compared with that from the abduction mode [Fig. 1(a)]. With the measured maximum M wave and different levels of SIP signals this ALS subject showed a MUNIX of 141 for the FDI muscle abduction mode, and 165 for the flexion mode [Fig. 1(b)]. The MUNIX changed to 156 when SIPs from both abduction and flexion modes were used for calculation [Fig. 1(c)]. For the remaining two muscles, one had the same MUNIX (9) for all the three conditions (abduction, flexion, and their combination). The other showed an abduction mode MUNIX much higher than the flexion mode MUNIX. Fig. 2 shows the MUNIX calculation in this muscle. It was observed that although the M wave amplitude was 15.2 mV, the voluntary surface EMG was poorly activated, particularly during index finger abduction [Fig. 2(a)]. The combination of M wave and SIP EMG signals resulted in a MUNIX of 278 for the index finger abduction mode and 193 for the index finger flexion mode [Fig. 2(b)]. Using voluntary surface EMG from both directions, the MUNIX of this muscle was 237 [Fig. 2(c)]. Table I summarizes the MUNIX calculation for all the examined FDI muscles. Across all muscles, the median MUNIX was 96 (range 9–311) for the index finger abduction mode, 161
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Fig. 2. FDI MUNIX calculation of the ALS subject that showed higher MUNIX using index finger abduction than the flexion mode. (a) CMAP and poorly activated voluntary surface EMG in index finger abduction. (b) MUNIX calculation using abduction and flexion mode, respectively. (c) MUNIX calculation using both abduction and flexion modes.
Fig. 1. FDI MUNIX calculation of an ALS subject that showed lower MUNIX using the index finger abduction than the flexion mode. (a) CMAP and voluntary surface EMG from index finger abduction and flexion, respectively. (b) MUNIX calculation using abduction and flexion mode EMG, respectively. (c) MUNIX calculation using both abduction and flexion modes.
TABLE I COMPARISON OF THE FDI MUSCLE MUNIX OF ALS PATIENTS, DERIVED FROM VOLUNTARY SURFACE EMG SIGNALS DURING INDEX FINGER ABDUCTION, FLEXION AND BOTH DIRECTIONS
(range 9–382) for the flexion mode, and 143 (range 9–354) for both directions, respectively . IV. DISCUSSION The primary target population of the development of MUNIX technique is ALS patients. This study addresses a practical question of voluntary muscle activation for calculation of MUNIX in multifunctional muscles of ALS patients. This is important considering that our body has many muscles (e.g., biceps brachii, deltoid, and the interossei muscles) that can produce forces in multiple directions about the respective joint. For these multifunctional muscles, the force generation in the desired direction can be realized by choosing groups of motor units that are
tuned selectively or by pairing antagonist muscles or parts of the muscle against each other [22]–[26]. The FDI muscle was examined in this study, whose plane of force production about the MCP joint primarily consists of
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linear combinations of abduction and flexion of the index finger. Thus the choice of directions was focused on pure index finger abduction and flexion, respectively. The dependence of MUNIX on voluntary contraction directions of the FDI muscle was previously examined in 15 healthy control subjects [20]. Across all subjects, the average MUNIX value of the FDI muscle was 228 45 for index finger abduction, slightly smaller than the MUNIX estimate of 251 56 for index finger flexion. As would be expected, the MUNIX estimates from the ALS subjects are significantly less than those from normal control subjects. We also note that the MUNIX variability of ALS subjects is much higher than that of the normal control subjects. This may be due to different disease progress stages or impairments of the tested ALS subjects. For most of the examined muscles in ALS subjects, the findings were consistent with those from the neurologically intact subjects [20] that the MUNIX using the index finger flexion mode was higher than the MUNIX using the abduction mode, except for two ALS subjects, one having the same MUNIX and the other demonstrating an opposite trend. The MUNIX calculation is based on exponential curve fitting using the relation between the CMAP and different levels SIP EMG signals. During our calculation of MUNIX for the FDI muscle using index finger abduction or flexion, the M wave was the same. Thus, the MUNIX difference was induced by different SIP EMG signals from the index finger abduction and flexion. The higher FDI muscle MUNIX derived from index finger flexion compared with abduction was primarily due to the relatively small surface EMG amplitude or area during the flexion mode in comparison with the abduction mode [27]. Different from the abduction force produced at the MCP joint that is generated largely by the FDI muscle, the flexion force of the index finger involves several other muscles (e.g., the flexor digitorum profundus and flexor digitorum superficialis muscles) in addition to the FDI muscle [28], [29]. It was also reported that in the FDI muscle the groups of motor units more suitable for abduction are located relatively superficial within the muscle compared with those motor units more suitable for flexion [30]. Because of these factors, the surface EMG amplitude in the flexion mode can be lower than that in the abduction mode. Since MUNIX computation derives an index proportional to the number of motor units in a muscle (rather than a direct estimation of the number motor units, as provided by MUNE methods), one principle for MUNIX application is to compare the MUNIX changes in different situations, and the same definition for all parameters should be used throughout the comparison study. According to this principle, when MUNIX methods are used in ALS patients, the direction of contraction should be considered for multifunctional muscles, so the MUNIX comparison will not be compromised by the difference induced from varying directions. For the muscle showing that abduction MUNIX was higher than flexion MUNIX, it was observed that although with relatively large or normal CMAP, the muscle was poorly activated voluntarily, particularly for index finger abduction. It implies that in ALS, motor neurons innervating a multifunctional muscle may be selectively degenerated, or the central nervous system may experience selective impairment in activation of functionally useful groups of motor
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units. As a result, parts of the muscle may be more severely affected, and the ALS subject may fail to perform contraction in a specific direction. It follows that for multifunctional muscles, a potential limitation of applying MUNIX technique is that the CMAP measures the contribution of both subpopulations of motor units, while the voluntary EMG may primarily measure one subpopulation or the other. To reduce potential artifact or to more reliably reflect motor unit number changes of ALS patients, a “multidimensional MUNIX” may be needed that utilizes surface EMG in different directions at once. Such an approach may result in more consistent MUNIX estimates than those from one direction. This can be realized by using voluntary surface EMG from both abduction and flexion as performed in this study, or using surface EMG in a combined direction (45 in the flexion–abduction plane) for MUNIX calculation. Alternatively, if the contribution of the CMAP to different directions can be estimated by recording the muscle twitch, it is feasible to calculate the direction specific MUNIX using the CMAP and voluntary surface EMG solely for that direction. A standard laboratory setup with a multiple-degree of freedoms load cell is required for such an approach, making it not appropriate for clinical application. Nonetheless, the approach is still worth of exploring to help a comprehensive and appropriate understanding of the MUNIX application in multifunctional muscles. REFERENCES [1] A. J. McComas, P. R. Fawcett, M. J. Campbell, and R. E. Sica, “Electrophysiological estimation of the number of motor units within a human muscle,” J. Neurol. Neurosurg. Psychiatry, vol. 34, pp. 121–131, 1971. [2] M. B. Bromberg, “Updating motor unit number estimation (MUNE),” Clin. Neurophysiol., vol. 118, pp. 1–8, 2007. [3] S. D. Nandedkar, D. S. Nandedkar, P. E. Barkhaus, and E. V. Stalberg, “Motor unit number index (MUNIX),” IEEE Trans. Biomed. Eng., vol. 51, no. 12, pp. 2209–2211, Dec. 2004. [4] S. D. Nandedkar, P. E. Barkhaus, and E. V. Stalberg, “Motor unit number index (MUNIX): Principle, method, findings in healthy subjects and in patients with motor neuron disease,” Muscle Nerve, vol. 42, pp. 798–807, 2010. [5] S. W. Ahn, S. H. Kim, J. E. Kim, S. M. Kim, S. H. Kim, K. S. Park, J. J. Sung, K. W. Lee, and Y. H. Hong, “Reproducibility of the motor unit number index (MUNIX) in normal controls and amyotrophic lateral sclerosis patients,” Muscle Nerve, vol. 42, pp. 808–813, 2010. [6] S. D. Nandedkar, P. E. Barkhaus, and E. V. Stålberg, “Reproducibility of MUNIX in patients with amyotrophic lateral sclerosis,” Muscle Nerve, vol. 44, pp. 919–922, 2011. [7] C. Neuwirth, S. Nandedkar, E. Stålberg, P. E. Barkhaus, M. Carvalho, J. Furtula, J. P. van Dijk, R. Baldinger, J. Castro, J. Costa, M. Otto, A. Sandberg, and M. Weber, “Motor Unit Number Index (MUNIX): A novel neurophysiological marker for neuromuscular disorders; testretest reliability in healthy volunteers,” Clin. Neurophysiol., vol. 122, pp. 1867–1872, 2011. [8] C. Neuwirth, S. Nandedkar, E. Stålberg, P. E. Barkhaus, M. Carvalho, J. Furtula, J. P. van Dijk, R. Baldinger, J. Castro, J. Costa, M. Otto, A. Sandberg, and M. Weber, “Motor Unit Number Index (MUNIX): Reference values of five different muscles in healthy subjects from a multi-centre study,” Clin. Neurophysiol., vol. 122, pp. 1895–1898, 2011. [9] C. Neuwirth, S. Nandedkar, E. Stalberg, and M. Weber, “Motor unit number index (MUNIX): A novel neurophysiological technique to follow disease progression in amyotrophic lateral sclerosis,” Muscle Nerve, vol. 42, pp. 379–384, 2010. [10] A. Sandberg, S. D. Nandedkar, and E. Stålberg E, “Macro electromyography and motor unit number index in the tibialis anterior muscle: Differences and similarities in characterizing motor unit properties in prior polio,” Muscle Nerve, vol. 43, pp. 335–341, 2011.
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Ping Zhou (S’01–M’05–SM’07) received the B.S. degree in electrical engineering and the M.S. degree in biomedical engineering from the University of Science and Technology of China, Hefei, China, in 1995 and 1999, respectively, and the Ph.D. degree in biomedical engineering from Northwestern University, Evanston, IL, USA, in 2004. From 1999 to 2014, he was progressively a Research Assistant, Research Associate, and (full and part time) Research Faculty at the Rehabilitation Institute of Chicago, Chicago, IL, USA. He was also an Adjunct Research Assistant and later Associate Professor in Physical Medicine and Rehabilitation of Northwestern University, Evanston, IL, USA, from 2006 to 2014. He currently holds an Adjunct Associate Professor position in Physical Medicine and Rehabilitation at the University of Texas Health Science Center at Houston, TX, USA. He directs the Neuromyoelectric Engineering for Rehabilitation laboratory housed in the outpatient clinic at TIRR Memorial Hermann Research Center. He is also affiliated with the biomedical engineering program of the University of Science and Technology of China. His research interests include biomedical signal (in particular, EMG) processing, motor unit pathophysiology and electrodiagnosis, myoelectric control, and assistive devices for neurorehabilitation.
Sanjeev D. Nandedkar received the Ph.D. degree in biomedical engineering from the University of Virginia, Charlottesville, VA, USA. He continued his research at the Duke University Medical Center before joining the TECA Corporation. Currently he is a Senior Consultant at Natus Medical Inc., Middleton, WI, USA, where he is involved in design, testing, and training for new electrodiagnostic instruments. He is actively conducting research in quantitative electromyography, signal processing, and study of the number of motor units. He has co-developed techniques such as EQUIP and MUNIX. He has lectured and conducted workshops in many countries. He also edits the EMG on DVD series of educational videos. Dr. Nandedkar received the “Distinguished Service” award by the American Association of Neuromuscular and Electrodiagnostic Medicine.
Paul E. Barkhaus received the B.S. and M.D. degrees from Wayne State University, Detroit, MI, USA. He completed his training in neurology at Wayne State University in 1978, followed by fellowships in clinical neuromuscular diseases at the University of Arizona, and two fellowships in electromyography at the University of Minnesota and Duke University. He has been on faculty at the Medical College of Wisconsin, Milwaukee, WI, USA, since 1993. He was appointed Professor of Neurology in 2002, and awarded tenure in 2011. He developed the Amyotrophic Lateral Sclerosis (Lou Gehrig Disease) Program in the late 1990s and it is currently one of almost 40 centers of the ALS Association’s Centers of Excellence in the USA. He also serves as the Department of Neurology’s Program Director for Clinical neurophysiology and is Section Chief for Neuromuscular and Autonomic Disorders. His current research interests are in clinical neurophysiology (electromyography and electroneurography) and amyotrophic lateral sclerosis.
