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The effect of contraction intensity on motor unit number estimates of the tibialis anterior
Clinical Neurophysiology 116 (2005) 1342–1347 www.elsevier.com/locate/clinph
The effect of contraction intensity on motor unit number estimates of the tibialis anterior Chris J. McNeila, Timothy J. Dohertya,b, Daniel W. Stashukc, Charles L. Ricea,d,* a
Canadian Centre for Activity and Aging, St Joseph’s Health Annex, School of Kinesiology, Faculty of Health Sciences, The University of Western Ontario, 1490 Richmond Street, London, Ont., Canada, N6G 2M3 b Departments of Clinical Neurological Sciences and Rehabilitation Medicine, The University of Western Ontario, London, Ont., Canada c Department of Systems Design Engineering, University of Waterloo, Waterloo, Ont., Canada d Department of Anatomy and Cell Biology, Faculty of Medicine and Dentistry, The University of Western Ontario, London, Ont., Canada Accepted 8 February 2005 Available online 28 March 2005
Abstract Objective: To examine the effect of contractile level on motor unit number estimates (MUNEs) and establish the contraction intensity that will yield the most representative MUNE for the tibialis anterior (TA) muscle. Methods: Surface and intramuscular electromyographic (EMG) signals were collected during a range of submaximal (threshold, 10, 20, 30 and 40% MVC) isometric dorsiflexion contractions using decomposition-enhanced spike-triggered averaging (DE-STA). Six MUNEs were calculated, one for each of the five intensities, and an ensemble sixth MUNE that had equal MU contributions from all intensities. Results: Mean surface-motor unit potential sizes increased significantly (26–69 mV) and MUNEs decreased accordingly (226-91) as contraction intensity increased from threshold to 40% MVC, respectively (P!0.05). The ensemble MUNE was 153, and extrapolated to w25% MVC. Conclusions: There was a significant and progressive decline in the MUNE as contraction intensity increased, confirming the importance of monitoring torque during data collection. The ensemble MUNE suggests that collecting EMG signals at a contraction intensity of w25% MVC provides the most representative sample of the actual number and sizes of MUs in the TA. Significance: Establishing appropriate contraction intensities improves the utility of DE-STA as a useful method for tracking changes to the MU pool in disease states and healthy aging. q 2005 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology. Keywords: Motor unit number estimation; Electromyography; Isometric strength; Decomposition-enhanced spike-triggered averaging
1. Introduction Objective assessment of the number of motor units (MUs) in a skeletal muscle group is of considerable interest in determining the severity and following the natural history of diseases affecting the lower motor neuron, as well as the study of healthy aging. No method exists to count human
* Corresponding author. Address: Canadian Centre for Activity and Aging, St Joseph’s Health Annex, School of Kinesiology, Faculty of Health Sciences, The University of Western Ontario, 1490 Richmond Street, London, Ont., Canada, N6G 2M3. Tel.: C1 519 6611628; fax: C1 519 6611612. E-mail address: [email protected] (C.L. Rice).
MUs in vivo, so motor unit number estimation (MUNE) techniques are, at present, the best available indication of the numbers of MUs in a given muscle group. Over 30 years ago, McComas et al. (1971) developed the first MUNE technique, based on incremental stimulation of the peripheral nerve supplying a given muscle group. While this method was theoretically sound, it required considerable operator expertise and the phenomenon of alternation raised concerns regarding its validity and reproducibility. Accordingly, the clinical and investigative applications of the technique were limited. In recent years, research interest in MUNE has been renewed, in part, due to advances in computing capability in both research-based and clinically available EMG systems. These advances have allowed
1388-2457/$30.00 q 2005 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology. doi:10.1016/j.clinph.2005.02.006
C.J. McNeil et al. / Clinical Neurophysiology 116 (2005) 1342–1347
the development of EMG signal decomposition, in conjunction with spike-triggered averaging, as a means of collecting a sample of surface-detected motor unit action potentials (S-MUPs) to derive a MUNE. Decomposition-enhanced spike-triggered averaging (DE-STA) has proven to be a valid and reliable method for estimating the number of MUs in a muscle group (Boe et al., 2004). However, it was recently demonstrated by our group (Boe et al., 2005) that MUNEs of intrinsic hand muscles were affected by the contraction intensity during the collection of the motor unit trains, i.e. the MUNEs were significantly lower at higher contraction intensities than lower intensities. Therefore, although DE-STA has demonstrated reliability with a single investigator (Boe et al., 2004; Conwit et al., 1997), it is possible that different investigators, or indeed a single investigator, may employ different contraction intensities in serial studies that would significantly affect the MUNE result for any one subject. Such a scenario would add to the variability of MUNEs in longitudinal studies unless experimental conditions, contraction intensity in particular, are strictly controlled. Thus, in order for DE-STA to be most effective, it would be ideal to develop standardized protocols that could be employed by all investigators. Moreover, because disparate results are possible in the same subject, it is an important consideration when designing such a protocol to establish which MUNE (i.e. contraction intensity) is most representative of the size distribution of S-MUPs that can be sampled with the method. The size principle of MU recruitment states that smaller MUs will be activated preferentially at low level contraction intensities, and that larger MUs will be recruited progressively as the contraction intensity increases (Henneman and Mendell, 1981). Therefore, low intensity contractions will produce a small mean surface motor unit potential (S-MUP) size. Because MUNEs are obtained by dividing the maximum M-potential by the mean S-MUP, a small mean S-MUP size corresponds to a large MUNE at low intensities of contraction. In contrast, higher intensity contractions will yield a larger mean S-MUP size and hence a smaller MUNE. It is logical then that the MUNE that is most representative of the actual number of MUs would be obtained at a contraction intensity that incorporates small and large S-MUPs in proportions similar to the actual distribution of MU sizes in the muscle under investigation. Thus the purposes of this study were (1) to confirm, in a different muscle (tibialis anterior; TA), the effect of contraction intensity on MUNEs observed in the intrinsic hand muscles, and (2) to determine the contraction intensity that will produce the MUNE that is most representative of the number and sizes of MUs in the TA of healthy, young men. The TA was chosen because relatively few MUNE studies have been done in lower limb muscles, and the TA is an important muscle in balance and gait. Moreover, the TA is part of an important functional muscle group that is often affected early and preferentially in many progressive
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disorders that affect the lower motor neuron (e.g. hereditary neuropathies). Therefore, normative data for this muscle is important for future expansion of DE-STA to include elderly and patient populations.
2. Methods 2.1. Subjects Nine young men (26.8G3.6 years, 178.4G7.5 cm, 80.7G8.9 kg), recruited from the university environment, volunteered for this study. All subjects were healthy with no evidence of neuromuscular disease and were considered recreationally active. The study was conducted in accordance with the guidelines for experimentation on human subjects established by the local university’s ethics review board, and informed written consent was obtained from each of the nine subjects. 2.2. Experimental set-up Subjects were seated in a custom-built isometric dynamometer (Marsh et al., 1981) with their right ankle positioned at 308 of plantar flexion, and an angle of 908 at both the hip and knee joints. A C-clamp pressing down on the distal aspect of the right thigh minimized hip flexion during the dorsiflexion contractions. Velcro straps across the toes and the dorsum of the foot secured the limb to the dynamometer footplate. Surface electromyography (EMG) signals were recorded by self-adhering electrocardiogram electrodes (3!2 cm; Kendall-LTP, Chicopee, MA). The active electrode was positioned over the tibialis anterior (TA) to maximize the negative peak amplitude, and minimize the rise time of the M-potential (approximately 7 cm distal to the tibial tuberosity and 2 cm lateral to the anterior border of the tibia). The reference electrode was positioned over the distal tendon of the TA, and a ground electrode was placed on the patella. Intramuscular EMG signals were recorded by a disposable concentric needle electrode with a recording surface of 0.03 mm 2 (Model N53153, Teca Corp., Hawthorne NY) inserted into the belly of the TA. 2.3. Experimental procedures The DE-STA method and associated algorithms have been described previously (Doherty and Stashuk, 2003; Stashuk, 1999; Stashuk et al., 2003). Intramuscular and surface EMG data were acquired using the DE-STA software on the Neuroscan Comperio system (Neurosoft Corp., El Paso, TX). Intramuscular signals were bandpass filtered from 10 Hz to 10 kHz, while surface signals were filtered from 5 Hz to 5 kHz. Data collection began with the determination of the maximum M-potential in response to supramaximal stimulation of the common peroneal nerve,
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posterior to the fibular head. Current intensity was increased incrementally until the negative peak amplitude of the M-potential reached a plateau, at which time the current was increased a further 15% to be certain that stimulation was supramaximal. Subsequently, maximal voluntary torque of the dorsiflexors was determined as the peak of three maximum voluntary contractions (MVCs), each separated by 3 min of rest. Subjects held each MVC for 3 s, during which time they were provided with visual feedback of the torque via an oscilloscope, and received strong verbal encouragement. Central activation was assessed on the second MVC attempt by use of the interpolated twitch technique. The torque amplitude of a supramaximal twitch (Ts) delivered during the MVC was compared to a resting twitch (Tr) delivered after the MVC to quantify central activation {% activationZ[1K(Ts/Tr)]!100%}. During the third MVC, the peak root mean square (RMS) value of the surface EMG signal, over a 1 s interval, was calculated. This value is referred to as the MVC-RMS. A further 3 min of rest was provided between the final MVC attempt and the first contraction with simultaneous intramuscular and surface EMG recording. Motor unit potential (MUP) trains were collected at five different contraction intensities (threshold, 10, 20, 30, or 40% MVC). The order was determined for each subject via a randomized draw. A target line was set on the oscilloscope at the first contraction intensity and subjects were asked to match this target torque as closely as possible during the subsequent isometric contractions. After insertion of the needle 5–10 mm proximal or distal to the active electrode, subjects were asked to minimally contract the TA while the operator adjusted the needle position to minimize the risetimes of the MUPs of the first 2–3 recruited MUs. When a suitable needle position was found, the operator asked the subject to gradually increase his force, over 1–2 s, until the appropriate target torque was reached. Once at the target, the operator initiated the recording of both the intramuscular and surface EMG signals for 30 s. Three to eight contractions were required to obtain at least 20 acceptable MUP trains (see Section 2.4). As contraction intensity increased, fewer MUs were successfully decomposed per contraction and consequently greater numbers of contractions were required at 30 and 40% MVC versus threshold or 10% MVC to obtain the necessary 20C MUP trains. One minute of rest was provided between each contraction; during this time, the operator repositioned the needle in the muscle in order to sample different MUs. Repositioning was first done by altering the depth of the needle in the muscle and then by performing additional needle insertions at other sites near to the active electrode. Following the successful collection of MUP trains at the first contraction intensity, the target line on the oscilloscope was adjusted for the next intensity. The same procedures were carried out until all of the five contraction intensities were completed.
2.4. Data reduction and statistics During off-line analysis, decomposed EMG signals were reviewed to determine the acceptability of the needle detected MUP trains and their corresponding S-MUPs. First, an acceptable MUP train required greater than 50 discharges that would serve as triggers for STA. Next, a visual check was made of each train to ensure that it represented a consistent MU firing pattern with a physiologic mean firing rate and firing rate fluctuation (i.e. a coefficient of variation %0.30). Lastly, the inter-discharge interval (IDI) histogram was examined to confirm that the distribution was Gaussian. MUP trains that did not meet these inclusion criteria were excluded from further analysis. S-MUPs were visually inspected to determine that a distinct waveform, temporally aligned with the needle potential, was evident from the baseline RMS signal. The negative onset and negative peak markers of acceptable SMUPs were examined to verify that they had been set correctly. Any markers that were incorrectly placed were repositioned manually by the operator. A computer algorithm then aligned the negative onset markers for all of the accepted S-MUPs and created a mean S-MUP template based upon their data-point by data-point average (Doherty et al., 1993) at each contraction intensity. For each subject, a representative ensemble mean S-MUP was calculated by pooling the acceptable S-MUPs from all five contraction intensities into a single file. Individual subject MUNEs for the five contraction intensities and the pooled ensemble were calculated by dividing the negative peak amplitude of the maximum M-potential by the negative peak amplitude of the appropriate mean S-MUP. Torque data were sampled on-line using Spike2 (version 4.10, Cambridge Electronic Design Ltd, Cambridge, UK) software. Using a 12 bit A/D converter (model 1401 Plus, Cambridge Electronic Design Ltd, Cambridge, UK), the torque data were sampled at 500 Hz. During off-line analysis, Spike2 software was used to determine voluntary isometric torques. The dynamometer transducer was calibrated using a series of weights with known masses and demonstrated a linear output. All data are reported in the text as group meansG standard deviations, and were analyzed using a withinsubjects repeated measures, one-way analysis of variance; the level of significance was set at P!0.05. When significant effects were found, a Tukey’s post hoc test was used to perform pair-wise comparisons.
3. Results 3.1. Strength, central activation and surface electromyography Maximal isometric dorsiflexion torque for the group was 41.3G5.3 Nm and was not influenced by a central
C.J. McNeil et al. / Clinical Neurophysiology 116 (2005) 1342–1347
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Table 1 Change in neuromuscular properties of the tibialis anterior muscle with increasing contraction intensity
Target Torque (%MVC) Target RMS (%MVC-RMS) Mean S-MUP NPAmp (mV) Mean MUFR (Hz)
Threshold
10%
20%
30%
40%
6.0G1.8 6.1G2.0 26G3.1 (8–100) 9.7G0.9
10.4G1.6 8.5G1.5 28G5.5 (13–99) 10.5G1.1
20.5G1.2 13.7G2.9 37G9.1 (12–151) 12.3G1.2*
29.0G0.8 19.0G3.7 51G11.1 (12–163) 13.4G1.7†
38.2G1.0 24.5G3.9 69G20.2 (30–223) 14.9G1.1‡
Torque and root mean square recorded during the 30 s targeting contractions (expressed as a percentage of MVC and the peak RMS achieved over a 1 s interval during the MVC; MVC-RMS, respectively), mean surface motor unit potential negative peak amplitude (mean S-MUP NPAmp), and mean motor unit firing rate (MUFR). Values are meansGstandard deviation with the range of accepted S-MUPs in parentheses. The firing rate at 20% MVC was significantly higher than threshold (*P!0.05), whereas 30% MVC was significantly higher than both threshold and 10% MVC (†P!0.05), and 40% MVC was significantly higher than threshold, 10 and 20% MVC (‡P!0.05). See Fig. 1 with regards to significant differences in the mean S-MUP.
activation failure (i.e. activation O99% for all subjects). The actual mean torques and RMS values generated during the 30 s targeting contractions at threshold, 10, 20, 30, and 40% MVC are reported in Table 1. The EMG (expressed as a percentage of MVC-RMS) matched the torque (expressed as a percentage of MVC) at threshold, but was progressively lower than torque as the contraction intensity increased. 3.2. Motor unit properties
100 90 80 70 60 50 40 30 20 10 0
The most important aspect of the current study was the attempt to establish a contraction intensity that would yield the most representative MUNE for the TA muscle. As expected, the MUNE decreased significantly from threshold to 40% MVC (226 versus 91, respectively). We contend that neither of these extremes accurately reflects the number of MUs in the TA. At threshold, only small MUs are active and included in the mean S-MUP, hence the MUNE is overestimated. Conversely, at 40% MVC, small MUs are under-represented in the decomposed signal causing the large MUs to over-contribute to the mean S-MUP (Conwit et al., 1997) and yield a MUNE that is an underestimate. By pooling greater than 100 valid S-MUPs from five different contraction intensities in each subject, we ensured that both small and large MUs were accorded an appropriate weighting in the data-point by data-point averaging of the ensemble mean S-MUP. The result of this ensemble mean S-MUP was a MUNE of 153 and an extrapolated contraction intensity of w25% MVC. Perhaps not surprisingly, both the ensemble MUNE and the extrapolated contraction intensity approximated the results obtained at the middle of the range from the lowest possible contraction intensity to the highest. Ultimately, the ensemble MUNE of 153 remains by definition an estimate, but we feel that it is the most accurate representation of the number of MUs in
†
*
Threshold
10%
20%
Ensemble
30%
350
Number of Motor Units
mean S-MUP NPAmp (µV)
The mean negative peak amplitude of the maximum M-potential was 6.1G1.0 mV. The mean S-MUP negative peak amplitude increased significantly as contraction intensity increased (Table 1 and Fig. 1). The ensemble mean S-MUP had a negative peak amplitude between the values for 20 and 30% MVC (Fig. 1). As a consequence of the increasing mean S-MUP negative peak amplitude, MUNEs were significantly reduced with increasing contraction intensity. The MUNEs at threshold and 10% MVC were 226 and 224 MUs, but the MUNE declined to 177, 125, and 91 MUs at 20, 30, and 40% MVC, respectively (Fig. 2). The ensemble estimate was 153 MUs and was extrapolated to a contraction intensity of w25% MVC (Fig. 2). In contrast to the MUNE values, mean motor unit firing rate increased concomitantly with contraction intensity (Table 1).
4. Discussion
40%
Contraction Intensity
300 250
*
200 150
*
†
30%
40%
100 50 0 Threshold
10%
20%
Ensemble
Contraction Intensity Fig. 1. Comparison of mean surface-motor unit potential (mean S-MUP) negative peak amplitude (NPAmp) at various contraction intensities. Values are meansGstandard deviation. Mean S-MUP negative peak amplitude was significantly larger at 30% MVC than at threshold (*P! 0.05), whereas 40% MVC was larger than threshold, 10 and 20% MVC and the ensemble estimate (†P!0.05).
Fig. 2. Comparison of motor unit number estimates (MUNEs) at various contraction intensities. Values are meansGstandard deviation. The ensemble estimate and 30% MVC MUNEs were significantly smaller than threshold and 10% MVC (*P!0.05), whereas the MUNE at 40% MVC was smaller than threshold, 10 and 20% MVC (†P!0.05).
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the TA of healthy, young men available at this time. Furthermore, because anatomical MU counts in cadavers are influenced by the difficulty in differentiating between sensory and motor nerve fibers (Doherty et al., 1995), there is no true ‘gold standard’ for MU numbers in a muscle, even when the invasiveness of the method is not an issue. The most practical implication of the ensemble MUNE is that the result obtained via the time consuming collection of more than 100 S-MUPs at five contraction intensities should also be obtainable from 20 S-MUPs at w25% MVC in the TA. This expectation was confirmed in our recent study in which the MUNE collected at 25% MVC for 10 young men was 150G43 (McNeil et al., 2005). This MUNE value is remarkably similar to the ensemble MUNE of 153G46 from the current study. Therefore, collection of intramuscular and surface EMG at the most appropriate contraction intensity for a given muscle can provide the most valid MUNE without adding to the testing time. The observed contractile level effect on mean S-MUP amplitude and MUNE in the TA confirms the recent findings for the first dorsal interosseous (FDI) by Boe et al. (2005). Although, given the size principle this was an expected result, the extent to which the results from the TA would mirror those from the FDI was unknown. FDI MUs may be fully recruited by 30% MVC (Basmajian and DeLuca, 1985; Kukulka and Clamann, 1981), in which case any increase in torque beyond this level would be supplied by an increase in MU firing rate. Therefore, the EMG interference pattern beyond 30% MVC in the FDI might reasonably be less complex than that from a muscle (e.g. TA) that presumably continues to augment torque via both recruitment and rate coding. For this reason, Boe et al. (2005) were able to collect MUPs and S-MUPs at a somewhat higher maximum contraction intensity (50% MVC) as compared to the present study (40% MVC). Regardless, in both studies, mean S-MUP size increased as a result of increased detection of larger S-MUPs and decreased detection of small amplitude S-MUPs. This decreased sampling (underrepresentation) of small amplitude S-MUPs is based upon the combined influences of physiological (size principle), electrophysiological, and technical factors. Each of these influences has been previously described in detail by our group (Boe et al., 2005). In brief, as contraction intensity increases, more and larger MUs are recruited and firing rates increase resulting in an increasingly complex interference pattern. As a result, needle MUP superpositions decrease the likelihood of isolating smaller individual MUP trains to serve as triggers for STA. Concurrently, the increased baseline noise in the surface EMG signal results in a decrease in the signal-to-noise ratio of the averaged SMUPs, and makes the inclusion of small amplitude S-MUPs less likely than large amplitude S-MUPS, even if sufficient numbers of triggers exist. The practical implication of these two factors is a ceiling effect based on contraction intensity for DE-STA, that is, 40% MVC in the present study. A third contributing factor is that it becomes progressively more
difficult for a subject to maintain a steady 30 s contraction as the intensity increases. An unsteady contraction would cause highly variable firing rates and subsequently reduce the decomposition identification rate for any given MUP. Although it is a simple task to shorten the acquisition time, this would mean fewer detected MU firings and a decreased confidence that isolated MUP trains represent single MUs. Furthermore, fewer triggers, regardless of the contraction intensity, lead to a decreased probability of extracting acceptable S-MUPs as described above. Motor unit firing rate and RMS values increased with contraction intensity. Firing rates were similar to those reported in the TA by Doherty and Stashuk (2003) using DE-STA at a moderate contraction intensity (torque not quantified) and the range reported by Connelly et al. (1999) using tungsten intramuscular electrodes at 10, 25, and 50% of MVC. In contrast to the firing rate data, the RMS data do not match those previously reported in the literature. In our study, the percentage of MVC-RMS matched the percentage of MVC at threshold but was progressively lower than torque as the contraction intensity increased, indicating a non-linear relationship. Ng and Kent-Braun (1999) reported a linear EMG/force relationship in the TA of young men. They measured force in 10% increments from 10 to 100% MVC, and the matching of relative EMG to %MVC was poorest at 10% MVC and improved with increasing contraction intensity. The disparity between the two studies is most likely due to the measure of EMG utilized, i.e. RMS in the present study and integrated EMG in the study by Ng and Kent-Braun. The findings of the present study and that of Boe et al. (2005) demonstrate that contractile level effects need to be controlled when conducting MUNE studies. It is unknown if different pathologic conditions would have a similar representative contraction intensity (25% MVC) to the healthy, young men of the current study. For these reasons, future studies need to examine the impact on contraction intensity on MUNEs in different disease populations such as hereditary neuropathies and amyotrophic lateral sclerosis. In summary, there was a significant and progressive increase in the mean S-MUP size with increasing contraction intensity and a consequent significant and progressive reduction in the MUNE for the TA. The most novel aspect of the study was the use of an expanded testing session to determine a single contraction intensity (25% for the TA) to obtain the MUNE that is most representative of the number and sizes of MUs in a muscle.
Acknowledgements This work is supported in part by the National Science and Engineering Research Council of Canada.
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References Basmajian JV, DeLuca CJ. Control properties of motor units. In: DeLuca CJ, Basmajian JV, editors. Muscles alive: their functions revealed by electromyography. 5th ed. Balimore, MD: Lippincott, Williams, and Wilkins; 1985. p. 125–67. Boe SG, Stashuk DW, Doherty TJ. Motor unit number estimation by decomposition-enhanced spike-triggered averaging: control data, test– retest reliability, and contractile level effects. Muscle Nerve 2004;29: 693–9. Boe SG, Stashuk DW, Brown WF, Doherty TJ. Decomposition-based quantitative electromyography: effect of force on motor unit potentials and motor unit number estimates. Muscle Nerve 2005;31:365–73. Connelly DM, Rice CL, Roos MR, Vandervoort AA. Motor unit firing rates and contractile properties in tibialis anterior of young and old men. J Appl Physiol 1999;87:843–52. Conwit RA, Tracy B, Jamison C, McHugh M, Stashuk D, Brown WF, Metter EJ. Decomposition-enhanced spike-triggered averaging: contraction level effects. Muscle Nerve 1997;20:976–82. Doherty TJ, Stashuk DW. Decomposition-based quantitative electromyography: methods and initial normative data in five muscles. Muscle Nerve 2003;28:204–11. Doherty TJ, Stashuk DW, Brown WF. Determinants of mean motor unit size: impact on estimates of motor unit number. Muscle Nerve 1993;16: 1326–31.
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Doherty T, Simmons Z, O’Connell B, Felice KJ, Conwit R, Chan KM, Komori T, Brown T, Stashuk DW, Brown WF. Methods for estimating the numbers of motor units in human muscles. J Clin Neurophysiol 1995;12:565–84. Henneman E, Mendell LM. Functional organization of motoneuron pool and its inputs. In: Brooks VB, editor. Handbook of physiology: the nervous system. Bethesda, MD: American Physiological Society; 1981. p. 423–508. Kukulka CG, Clamann HP. Comparison of the recruitment and discharge properties of motor units in human brachial biceps and adductor pollicis during isometric contractions. Brain Res 1981;219:45–55. Marsh E, Sale D, McComas AJ, Quinlan J. Influence of joint position on ankle dorsiflexion in humans. J Appl Physiol 1981;51:160–7. McComas AJ, Fawcett PR, Campbell MJ, Sica RE. Electrophysiological estimation of the number of motor units within a human muscle. J Neurol Neurosurg Psychiatr 1971;34:121–31. McNeil CJ, Doherty TJ, Stashuk DW, Rice CL. Tibialis anterior motor unit number estimates in young, old, and very old men. Muscle Nerve 2005; Jan 31 [Epub ahead of print]. Ng AV, Kent-Braun JA. Slowed muscle contractile properties are not associated with a decreased EMG/force relationship in older humans. J Gerontol A Biol Sci Med Sci 1999;54:B452–B8. Stashuk DW. Decomposition and quantitative analysis of clinical electromyographic signals. Med Eng Phys 1999;21:389–404. Stashuk DW, Doherty TJ, Brown WF. MUNE using decompositionenhanced spike-triggered averaging. Clin Physiol 2003;55(Suppl.): 108–21.
The effect of contraction intensity on motor unit number estimates of the tibialis anterior Chris J. McNeila, Timothy J. Dohertya,b, Daniel W. Stashukc, Charles L. Ricea,d,* a
Canadian Centre for Activity and Aging, St Joseph’s Health Annex, School of Kinesiology, Faculty of Health Sciences, The University of Western Ontario, 1490 Richmond Street, London, Ont., Canada, N6G 2M3 b Departments of Clinical Neurological Sciences and Rehabilitation Medicine, The University of Western Ontario, London, Ont., Canada c Department of Systems Design Engineering, University of Waterloo, Waterloo, Ont., Canada d Department of Anatomy and Cell Biology, Faculty of Medicine and Dentistry, The University of Western Ontario, London, Ont., Canada Accepted 8 February 2005 Available online 28 March 2005
Abstract Objective: To examine the effect of contractile level on motor unit number estimates (MUNEs) and establish the contraction intensity that will yield the most representative MUNE for the tibialis anterior (TA) muscle. Methods: Surface and intramuscular electromyographic (EMG) signals were collected during a range of submaximal (threshold, 10, 20, 30 and 40% MVC) isometric dorsiflexion contractions using decomposition-enhanced spike-triggered averaging (DE-STA). Six MUNEs were calculated, one for each of the five intensities, and an ensemble sixth MUNE that had equal MU contributions from all intensities. Results: Mean surface-motor unit potential sizes increased significantly (26–69 mV) and MUNEs decreased accordingly (226-91) as contraction intensity increased from threshold to 40% MVC, respectively (P!0.05). The ensemble MUNE was 153, and extrapolated to w25% MVC. Conclusions: There was a significant and progressive decline in the MUNE as contraction intensity increased, confirming the importance of monitoring torque during data collection. The ensemble MUNE suggests that collecting EMG signals at a contraction intensity of w25% MVC provides the most representative sample of the actual number and sizes of MUs in the TA. Significance: Establishing appropriate contraction intensities improves the utility of DE-STA as a useful method for tracking changes to the MU pool in disease states and healthy aging. q 2005 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology. Keywords: Motor unit number estimation; Electromyography; Isometric strength; Decomposition-enhanced spike-triggered averaging
1. Introduction Objective assessment of the number of motor units (MUs) in a skeletal muscle group is of considerable interest in determining the severity and following the natural history of diseases affecting the lower motor neuron, as well as the study of healthy aging. No method exists to count human
* Corresponding author. Address: Canadian Centre for Activity and Aging, St Joseph’s Health Annex, School of Kinesiology, Faculty of Health Sciences, The University of Western Ontario, 1490 Richmond Street, London, Ont., Canada, N6G 2M3. Tel.: C1 519 6611628; fax: C1 519 6611612. E-mail address: [email protected] (C.L. Rice).
MUs in vivo, so motor unit number estimation (MUNE) techniques are, at present, the best available indication of the numbers of MUs in a given muscle group. Over 30 years ago, McComas et al. (1971) developed the first MUNE technique, based on incremental stimulation of the peripheral nerve supplying a given muscle group. While this method was theoretically sound, it required considerable operator expertise and the phenomenon of alternation raised concerns regarding its validity and reproducibility. Accordingly, the clinical and investigative applications of the technique were limited. In recent years, research interest in MUNE has been renewed, in part, due to advances in computing capability in both research-based and clinically available EMG systems. These advances have allowed
1388-2457/$30.00 q 2005 Published by Elsevier Ireland Ltd. on behalf of International Federation of Clinical Neurophysiology. doi:10.1016/j.clinph.2005.02.006
C.J. McNeil et al. / Clinical Neurophysiology 116 (2005) 1342–1347
the development of EMG signal decomposition, in conjunction with spike-triggered averaging, as a means of collecting a sample of surface-detected motor unit action potentials (S-MUPs) to derive a MUNE. Decomposition-enhanced spike-triggered averaging (DE-STA) has proven to be a valid and reliable method for estimating the number of MUs in a muscle group (Boe et al., 2004). However, it was recently demonstrated by our group (Boe et al., 2005) that MUNEs of intrinsic hand muscles were affected by the contraction intensity during the collection of the motor unit trains, i.e. the MUNEs were significantly lower at higher contraction intensities than lower intensities. Therefore, although DE-STA has demonstrated reliability with a single investigator (Boe et al., 2004; Conwit et al., 1997), it is possible that different investigators, or indeed a single investigator, may employ different contraction intensities in serial studies that would significantly affect the MUNE result for any one subject. Such a scenario would add to the variability of MUNEs in longitudinal studies unless experimental conditions, contraction intensity in particular, are strictly controlled. Thus, in order for DE-STA to be most effective, it would be ideal to develop standardized protocols that could be employed by all investigators. Moreover, because disparate results are possible in the same subject, it is an important consideration when designing such a protocol to establish which MUNE (i.e. contraction intensity) is most representative of the size distribution of S-MUPs that can be sampled with the method. The size principle of MU recruitment states that smaller MUs will be activated preferentially at low level contraction intensities, and that larger MUs will be recruited progressively as the contraction intensity increases (Henneman and Mendell, 1981). Therefore, low intensity contractions will produce a small mean surface motor unit potential (S-MUP) size. Because MUNEs are obtained by dividing the maximum M-potential by the mean S-MUP, a small mean S-MUP size corresponds to a large MUNE at low intensities of contraction. In contrast, higher intensity contractions will yield a larger mean S-MUP size and hence a smaller MUNE. It is logical then that the MUNE that is most representative of the actual number of MUs would be obtained at a contraction intensity that incorporates small and large S-MUPs in proportions similar to the actual distribution of MU sizes in the muscle under investigation. Thus the purposes of this study were (1) to confirm, in a different muscle (tibialis anterior; TA), the effect of contraction intensity on MUNEs observed in the intrinsic hand muscles, and (2) to determine the contraction intensity that will produce the MUNE that is most representative of the number and sizes of MUs in the TA of healthy, young men. The TA was chosen because relatively few MUNE studies have been done in lower limb muscles, and the TA is an important muscle in balance and gait. Moreover, the TA is part of an important functional muscle group that is often affected early and preferentially in many progressive
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disorders that affect the lower motor neuron (e.g. hereditary neuropathies). Therefore, normative data for this muscle is important for future expansion of DE-STA to include elderly and patient populations.
2. Methods 2.1. Subjects Nine young men (26.8G3.6 years, 178.4G7.5 cm, 80.7G8.9 kg), recruited from the university environment, volunteered for this study. All subjects were healthy with no evidence of neuromuscular disease and were considered recreationally active. The study was conducted in accordance with the guidelines for experimentation on human subjects established by the local university’s ethics review board, and informed written consent was obtained from each of the nine subjects. 2.2. Experimental set-up Subjects were seated in a custom-built isometric dynamometer (Marsh et al., 1981) with their right ankle positioned at 308 of plantar flexion, and an angle of 908 at both the hip and knee joints. A C-clamp pressing down on the distal aspect of the right thigh minimized hip flexion during the dorsiflexion contractions. Velcro straps across the toes and the dorsum of the foot secured the limb to the dynamometer footplate. Surface electromyography (EMG) signals were recorded by self-adhering electrocardiogram electrodes (3!2 cm; Kendall-LTP, Chicopee, MA). The active electrode was positioned over the tibialis anterior (TA) to maximize the negative peak amplitude, and minimize the rise time of the M-potential (approximately 7 cm distal to the tibial tuberosity and 2 cm lateral to the anterior border of the tibia). The reference electrode was positioned over the distal tendon of the TA, and a ground electrode was placed on the patella. Intramuscular EMG signals were recorded by a disposable concentric needle electrode with a recording surface of 0.03 mm 2 (Model N53153, Teca Corp., Hawthorne NY) inserted into the belly of the TA. 2.3. Experimental procedures The DE-STA method and associated algorithms have been described previously (Doherty and Stashuk, 2003; Stashuk, 1999; Stashuk et al., 2003). Intramuscular and surface EMG data were acquired using the DE-STA software on the Neuroscan Comperio system (Neurosoft Corp., El Paso, TX). Intramuscular signals were bandpass filtered from 10 Hz to 10 kHz, while surface signals were filtered from 5 Hz to 5 kHz. Data collection began with the determination of the maximum M-potential in response to supramaximal stimulation of the common peroneal nerve,
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posterior to the fibular head. Current intensity was increased incrementally until the negative peak amplitude of the M-potential reached a plateau, at which time the current was increased a further 15% to be certain that stimulation was supramaximal. Subsequently, maximal voluntary torque of the dorsiflexors was determined as the peak of three maximum voluntary contractions (MVCs), each separated by 3 min of rest. Subjects held each MVC for 3 s, during which time they were provided with visual feedback of the torque via an oscilloscope, and received strong verbal encouragement. Central activation was assessed on the second MVC attempt by use of the interpolated twitch technique. The torque amplitude of a supramaximal twitch (Ts) delivered during the MVC was compared to a resting twitch (Tr) delivered after the MVC to quantify central activation {% activationZ[1K(Ts/Tr)]!100%}. During the third MVC, the peak root mean square (RMS) value of the surface EMG signal, over a 1 s interval, was calculated. This value is referred to as the MVC-RMS. A further 3 min of rest was provided between the final MVC attempt and the first contraction with simultaneous intramuscular and surface EMG recording. Motor unit potential (MUP) trains were collected at five different contraction intensities (threshold, 10, 20, 30, or 40% MVC). The order was determined for each subject via a randomized draw. A target line was set on the oscilloscope at the first contraction intensity and subjects were asked to match this target torque as closely as possible during the subsequent isometric contractions. After insertion of the needle 5–10 mm proximal or distal to the active electrode, subjects were asked to minimally contract the TA while the operator adjusted the needle position to minimize the risetimes of the MUPs of the first 2–3 recruited MUs. When a suitable needle position was found, the operator asked the subject to gradually increase his force, over 1–2 s, until the appropriate target torque was reached. Once at the target, the operator initiated the recording of both the intramuscular and surface EMG signals for 30 s. Three to eight contractions were required to obtain at least 20 acceptable MUP trains (see Section 2.4). As contraction intensity increased, fewer MUs were successfully decomposed per contraction and consequently greater numbers of contractions were required at 30 and 40% MVC versus threshold or 10% MVC to obtain the necessary 20C MUP trains. One minute of rest was provided between each contraction; during this time, the operator repositioned the needle in the muscle in order to sample different MUs. Repositioning was first done by altering the depth of the needle in the muscle and then by performing additional needle insertions at other sites near to the active electrode. Following the successful collection of MUP trains at the first contraction intensity, the target line on the oscilloscope was adjusted for the next intensity. The same procedures were carried out until all of the five contraction intensities were completed.
2.4. Data reduction and statistics During off-line analysis, decomposed EMG signals were reviewed to determine the acceptability of the needle detected MUP trains and their corresponding S-MUPs. First, an acceptable MUP train required greater than 50 discharges that would serve as triggers for STA. Next, a visual check was made of each train to ensure that it represented a consistent MU firing pattern with a physiologic mean firing rate and firing rate fluctuation (i.e. a coefficient of variation %0.30). Lastly, the inter-discharge interval (IDI) histogram was examined to confirm that the distribution was Gaussian. MUP trains that did not meet these inclusion criteria were excluded from further analysis. S-MUPs were visually inspected to determine that a distinct waveform, temporally aligned with the needle potential, was evident from the baseline RMS signal. The negative onset and negative peak markers of acceptable SMUPs were examined to verify that they had been set correctly. Any markers that were incorrectly placed were repositioned manually by the operator. A computer algorithm then aligned the negative onset markers for all of the accepted S-MUPs and created a mean S-MUP template based upon their data-point by data-point average (Doherty et al., 1993) at each contraction intensity. For each subject, a representative ensemble mean S-MUP was calculated by pooling the acceptable S-MUPs from all five contraction intensities into a single file. Individual subject MUNEs for the five contraction intensities and the pooled ensemble were calculated by dividing the negative peak amplitude of the maximum M-potential by the negative peak amplitude of the appropriate mean S-MUP. Torque data were sampled on-line using Spike2 (version 4.10, Cambridge Electronic Design Ltd, Cambridge, UK) software. Using a 12 bit A/D converter (model 1401 Plus, Cambridge Electronic Design Ltd, Cambridge, UK), the torque data were sampled at 500 Hz. During off-line analysis, Spike2 software was used to determine voluntary isometric torques. The dynamometer transducer was calibrated using a series of weights with known masses and demonstrated a linear output. All data are reported in the text as group meansG standard deviations, and were analyzed using a withinsubjects repeated measures, one-way analysis of variance; the level of significance was set at P!0.05. When significant effects were found, a Tukey’s post hoc test was used to perform pair-wise comparisons.
3. Results 3.1. Strength, central activation and surface electromyography Maximal isometric dorsiflexion torque for the group was 41.3G5.3 Nm and was not influenced by a central
C.J. McNeil et al. / Clinical Neurophysiology 116 (2005) 1342–1347
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Table 1 Change in neuromuscular properties of the tibialis anterior muscle with increasing contraction intensity
Target Torque (%MVC) Target RMS (%MVC-RMS) Mean S-MUP NPAmp (mV) Mean MUFR (Hz)
Threshold
10%
20%
30%
40%
6.0G1.8 6.1G2.0 26G3.1 (8–100) 9.7G0.9
10.4G1.6 8.5G1.5 28G5.5 (13–99) 10.5G1.1
20.5G1.2 13.7G2.9 37G9.1 (12–151) 12.3G1.2*
29.0G0.8 19.0G3.7 51G11.1 (12–163) 13.4G1.7†
38.2G1.0 24.5G3.9 69G20.2 (30–223) 14.9G1.1‡
Torque and root mean square recorded during the 30 s targeting contractions (expressed as a percentage of MVC and the peak RMS achieved over a 1 s interval during the MVC; MVC-RMS, respectively), mean surface motor unit potential negative peak amplitude (mean S-MUP NPAmp), and mean motor unit firing rate (MUFR). Values are meansGstandard deviation with the range of accepted S-MUPs in parentheses. The firing rate at 20% MVC was significantly higher than threshold (*P!0.05), whereas 30% MVC was significantly higher than both threshold and 10% MVC (†P!0.05), and 40% MVC was significantly higher than threshold, 10 and 20% MVC (‡P!0.05). See Fig. 1 with regards to significant differences in the mean S-MUP.
activation failure (i.e. activation O99% for all subjects). The actual mean torques and RMS values generated during the 30 s targeting contractions at threshold, 10, 20, 30, and 40% MVC are reported in Table 1. The EMG (expressed as a percentage of MVC-RMS) matched the torque (expressed as a percentage of MVC) at threshold, but was progressively lower than torque as the contraction intensity increased. 3.2. Motor unit properties
100 90 80 70 60 50 40 30 20 10 0
The most important aspect of the current study was the attempt to establish a contraction intensity that would yield the most representative MUNE for the TA muscle. As expected, the MUNE decreased significantly from threshold to 40% MVC (226 versus 91, respectively). We contend that neither of these extremes accurately reflects the number of MUs in the TA. At threshold, only small MUs are active and included in the mean S-MUP, hence the MUNE is overestimated. Conversely, at 40% MVC, small MUs are under-represented in the decomposed signal causing the large MUs to over-contribute to the mean S-MUP (Conwit et al., 1997) and yield a MUNE that is an underestimate. By pooling greater than 100 valid S-MUPs from five different contraction intensities in each subject, we ensured that both small and large MUs were accorded an appropriate weighting in the data-point by data-point averaging of the ensemble mean S-MUP. The result of this ensemble mean S-MUP was a MUNE of 153 and an extrapolated contraction intensity of w25% MVC. Perhaps not surprisingly, both the ensemble MUNE and the extrapolated contraction intensity approximated the results obtained at the middle of the range from the lowest possible contraction intensity to the highest. Ultimately, the ensemble MUNE of 153 remains by definition an estimate, but we feel that it is the most accurate representation of the number of MUs in
†
*
Threshold
10%
20%
Ensemble
30%
350
Number of Motor Units
mean S-MUP NPAmp (µV)
The mean negative peak amplitude of the maximum M-potential was 6.1G1.0 mV. The mean S-MUP negative peak amplitude increased significantly as contraction intensity increased (Table 1 and Fig. 1). The ensemble mean S-MUP had a negative peak amplitude between the values for 20 and 30% MVC (Fig. 1). As a consequence of the increasing mean S-MUP negative peak amplitude, MUNEs were significantly reduced with increasing contraction intensity. The MUNEs at threshold and 10% MVC were 226 and 224 MUs, but the MUNE declined to 177, 125, and 91 MUs at 20, 30, and 40% MVC, respectively (Fig. 2). The ensemble estimate was 153 MUs and was extrapolated to a contraction intensity of w25% MVC (Fig. 2). In contrast to the MUNE values, mean motor unit firing rate increased concomitantly with contraction intensity (Table 1).
4. Discussion
40%
Contraction Intensity
300 250
*
200 150
*
†
30%
40%
100 50 0 Threshold
10%
20%
Ensemble
Contraction Intensity Fig. 1. Comparison of mean surface-motor unit potential (mean S-MUP) negative peak amplitude (NPAmp) at various contraction intensities. Values are meansGstandard deviation. Mean S-MUP negative peak amplitude was significantly larger at 30% MVC than at threshold (*P! 0.05), whereas 40% MVC was larger than threshold, 10 and 20% MVC and the ensemble estimate (†P!0.05).
Fig. 2. Comparison of motor unit number estimates (MUNEs) at various contraction intensities. Values are meansGstandard deviation. The ensemble estimate and 30% MVC MUNEs were significantly smaller than threshold and 10% MVC (*P!0.05), whereas the MUNE at 40% MVC was smaller than threshold, 10 and 20% MVC (†P!0.05).
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the TA of healthy, young men available at this time. Furthermore, because anatomical MU counts in cadavers are influenced by the difficulty in differentiating between sensory and motor nerve fibers (Doherty et al., 1995), there is no true ‘gold standard’ for MU numbers in a muscle, even when the invasiveness of the method is not an issue. The most practical implication of the ensemble MUNE is that the result obtained via the time consuming collection of more than 100 S-MUPs at five contraction intensities should also be obtainable from 20 S-MUPs at w25% MVC in the TA. This expectation was confirmed in our recent study in which the MUNE collected at 25% MVC for 10 young men was 150G43 (McNeil et al., 2005). This MUNE value is remarkably similar to the ensemble MUNE of 153G46 from the current study. Therefore, collection of intramuscular and surface EMG at the most appropriate contraction intensity for a given muscle can provide the most valid MUNE without adding to the testing time. The observed contractile level effect on mean S-MUP amplitude and MUNE in the TA confirms the recent findings for the first dorsal interosseous (FDI) by Boe et al. (2005). Although, given the size principle this was an expected result, the extent to which the results from the TA would mirror those from the FDI was unknown. FDI MUs may be fully recruited by 30% MVC (Basmajian and DeLuca, 1985; Kukulka and Clamann, 1981), in which case any increase in torque beyond this level would be supplied by an increase in MU firing rate. Therefore, the EMG interference pattern beyond 30% MVC in the FDI might reasonably be less complex than that from a muscle (e.g. TA) that presumably continues to augment torque via both recruitment and rate coding. For this reason, Boe et al. (2005) were able to collect MUPs and S-MUPs at a somewhat higher maximum contraction intensity (50% MVC) as compared to the present study (40% MVC). Regardless, in both studies, mean S-MUP size increased as a result of increased detection of larger S-MUPs and decreased detection of small amplitude S-MUPs. This decreased sampling (underrepresentation) of small amplitude S-MUPs is based upon the combined influences of physiological (size principle), electrophysiological, and technical factors. Each of these influences has been previously described in detail by our group (Boe et al., 2005). In brief, as contraction intensity increases, more and larger MUs are recruited and firing rates increase resulting in an increasingly complex interference pattern. As a result, needle MUP superpositions decrease the likelihood of isolating smaller individual MUP trains to serve as triggers for STA. Concurrently, the increased baseline noise in the surface EMG signal results in a decrease in the signal-to-noise ratio of the averaged SMUPs, and makes the inclusion of small amplitude S-MUPs less likely than large amplitude S-MUPS, even if sufficient numbers of triggers exist. The practical implication of these two factors is a ceiling effect based on contraction intensity for DE-STA, that is, 40% MVC in the present study. A third contributing factor is that it becomes progressively more
difficult for a subject to maintain a steady 30 s contraction as the intensity increases. An unsteady contraction would cause highly variable firing rates and subsequently reduce the decomposition identification rate for any given MUP. Although it is a simple task to shorten the acquisition time, this would mean fewer detected MU firings and a decreased confidence that isolated MUP trains represent single MUs. Furthermore, fewer triggers, regardless of the contraction intensity, lead to a decreased probability of extracting acceptable S-MUPs as described above. Motor unit firing rate and RMS values increased with contraction intensity. Firing rates were similar to those reported in the TA by Doherty and Stashuk (2003) using DE-STA at a moderate contraction intensity (torque not quantified) and the range reported by Connelly et al. (1999) using tungsten intramuscular electrodes at 10, 25, and 50% of MVC. In contrast to the firing rate data, the RMS data do not match those previously reported in the literature. In our study, the percentage of MVC-RMS matched the percentage of MVC at threshold but was progressively lower than torque as the contraction intensity increased, indicating a non-linear relationship. Ng and Kent-Braun (1999) reported a linear EMG/force relationship in the TA of young men. They measured force in 10% increments from 10 to 100% MVC, and the matching of relative EMG to %MVC was poorest at 10% MVC and improved with increasing contraction intensity. The disparity between the two studies is most likely due to the measure of EMG utilized, i.e. RMS in the present study and integrated EMG in the study by Ng and Kent-Braun. The findings of the present study and that of Boe et al. (2005) demonstrate that contractile level effects need to be controlled when conducting MUNE studies. It is unknown if different pathologic conditions would have a similar representative contraction intensity (25% MVC) to the healthy, young men of the current study. For these reasons, future studies need to examine the impact on contraction intensity on MUNEs in different disease populations such as hereditary neuropathies and amyotrophic lateral sclerosis. In summary, there was a significant and progressive increase in the mean S-MUP size with increasing contraction intensity and a consequent significant and progressive reduction in the MUNE for the TA. The most novel aspect of the study was the use of an expanded testing session to determine a single contraction intensity (25% for the TA) to obtain the MUNE that is most representative of the number and sizes of MUs in a muscle.
Acknowledgements This work is supported in part by the National Science and Engineering Research Council of Canada.
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References Basmajian JV, DeLuca CJ. Control properties of motor units. In: DeLuca CJ, Basmajian JV, editors. Muscles alive: their functions revealed by electromyography. 5th ed. Balimore, MD: Lippincott, Williams, and Wilkins; 1985. p. 125–67. Boe SG, Stashuk DW, Doherty TJ. Motor unit number estimation by decomposition-enhanced spike-triggered averaging: control data, test– retest reliability, and contractile level effects. Muscle Nerve 2004;29: 693–9. Boe SG, Stashuk DW, Brown WF, Doherty TJ. Decomposition-based quantitative electromyography: effect of force on motor unit potentials and motor unit number estimates. Muscle Nerve 2005;31:365–73. Connelly DM, Rice CL, Roos MR, Vandervoort AA. Motor unit firing rates and contractile properties in tibialis anterior of young and old men. J Appl Physiol 1999;87:843–52. Conwit RA, Tracy B, Jamison C, McHugh M, Stashuk D, Brown WF, Metter EJ. Decomposition-enhanced spike-triggered averaging: contraction level effects. Muscle Nerve 1997;20:976–82. Doherty TJ, Stashuk DW. Decomposition-based quantitative electromyography: methods and initial normative data in five muscles. Muscle Nerve 2003;28:204–11. Doherty TJ, Stashuk DW, Brown WF. Determinants of mean motor unit size: impact on estimates of motor unit number. Muscle Nerve 1993;16: 1326–31.
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Doherty T, Simmons Z, O’Connell B, Felice KJ, Conwit R, Chan KM, Komori T, Brown T, Stashuk DW, Brown WF. Methods for estimating the numbers of motor units in human muscles. J Clin Neurophysiol 1995;12:565–84. Henneman E, Mendell LM. Functional organization of motoneuron pool and its inputs. In: Brooks VB, editor. Handbook of physiology: the nervous system. Bethesda, MD: American Physiological Society; 1981. p. 423–508. Kukulka CG, Clamann HP. Comparison of the recruitment and discharge properties of motor units in human brachial biceps and adductor pollicis during isometric contractions. Brain Res 1981;219:45–55. Marsh E, Sale D, McComas AJ, Quinlan J. Influence of joint position on ankle dorsiflexion in humans. J Appl Physiol 1981;51:160–7. McComas AJ, Fawcett PR, Campbell MJ, Sica RE. Electrophysiological estimation of the number of motor units within a human muscle. J Neurol Neurosurg Psychiatr 1971;34:121–31. McNeil CJ, Doherty TJ, Stashuk DW, Rice CL. Tibialis anterior motor unit number estimates in young, old, and very old men. Muscle Nerve 2005; Jan 31 [Epub ahead of print]. Ng AV, Kent-Braun JA. Slowed muscle contractile properties are not associated with a decreased EMG/force relationship in older humans. J Gerontol A Biol Sci Med Sci 1999;54:B452–B8. Stashuk DW. Decomposition and quantitative analysis of clinical electromyographic signals. Med Eng Phys 1999;21:389–404. Stashuk DW, Doherty TJ, Brown WF. MUNE using decompositionenhanced spike-triggered averaging. Clin Physiol 2003;55(Suppl.): 108–21.