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Statistical MUNE: A comparison of two methods of setting recording windows in healthy subjects and ALS patients
Clinical Neurophysiology 118 (2007) 2605–2611 www.elsevier.com/locate/clinph
Statistical MUNE: A comparison of two methods of setting recording windows in healthy subjects and ALS patients Yoon-Ho Hong a, Jung-Joon Sung b,*, Kyung Seok Park c, Ohyun Kwon d, Ju-Hong Min e, Kwang-Woo Lee b a
c
Department of Neurology, Seoul National University College of Medicine, Boramae Hospital, Seoul, Republic of Korea b Department of Neurology, Seoul National University College of Medicine, Seoul National University Hospital, 28 Yongon-Dong Chongno-gu, Seoul 110-744, Republic of Korea Department of Neurology, Seoul National University College of Medicine, Bundang Hospital, Seoul, Republic of Korea d Department of Neurology, Eulji General Hospital, Eulji University School of Medicine, Seoul, Republic of Korea e Department of Neurology, Chung-Ang University College of Medicine, Seoul, Republic of Korea
See Editorial, pages 2542–2543
Abstract Objective: To address the issue as to how best to perform statistical MUNE, we applied two different approaches and compared results in healthy subjects and ALS patients. Methods: Twelve normal subjects (women 8, mean age 52 years) and 11 ALS patients (women 4, mean age 54 years) underwent two consecutive MUNE studies, which differed in terms of setting and modifying the recording window. These are referred to as the ‘expansion’ and ‘narrowing’ methods, respectively. Size-weighted average (Av) SMUP and MUNE values were obtained using the two methods, and compared in control and patient groups. Results: Expansion method-derived Av SMUP sizes and MUNE values differed only slightly from those obtained using the narrowing method in healthy subjects, whereas the narrowing method resulted in significantly larger Av SMUP sizes and smaller MUNE values than the expansion method in ALS patients (Wilcoxon signed ranks test, p = 0.003). The sizes of tested areas (mean ± SD) were significantly larger for the narrowing method than the expansion method in both subject groups with much greater difference in ALS patients; 9.6 ± 3.1% vs. 7.9 ± 1.7% in healthy subjects and 16.1 ± 5.1% vs. 11.2 ± 3.0% in ALS patients (Student t-test, p < 0.01). Conclusions: The present study shows, unlike that found in normal subjects, that the results of statistical MUNE in ALS patients are heavily dependent on the approach used to set and modify recording windows. Significance: The expansion method using a 10%-sized window is likely to suffer from systemic errors due to the ceiling effect and the sampling of artifactually small motor units in ALS patients. The authors recommend that the narrowing method be considered as an alternative that avoids these problems. Ó 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. Keywords: Statistical MUNE; Recording window; ALS
1. Introduction Motor unit number estimation (MUNE) refers to an electrophysiological technique that estimates the number
*
Corresponding author. Tel.: +82 2 2072 1015; fax: +82 2 3672 7553. E-mail address: [email protected] (J.-J. Sung).
of functioning motor units in a given muscle or group of muscles (Shefner, 2001). MUNE is calculated by dividing the size of maximal compound muscle action potential (CMAP) by the size of average surface recorded motor unit potential (SMUP) (McComas et al., 1971). Following McComa’s first proposal in 1971, various MUNE techniques have been developed, which essentially obtain representative samples of SMUPs in different ways (Shefner,
1388-2457/$32.00 Ó 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2007.05.073
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2001; Bromberg, 2003). Among these methods, statistical MUNE developed by Daube in 1995 is unique in that it uses the stochastic properties of variation in recorded submaximal CMAP to derive an estimate of mean SMUP size (Daube, 1995). Using this technique, motor units can be sampled over a wider response range from the stimulus– response curve, which means that sampled motor units are more likely to represent the whole motor unit population. The test–retest reproducibility of statistical MUNE has been reported to be good for both ALS patients and healthy subjects (Lomen-Hoerth and Slawnych, 2003), and it has been claimed that statistical MUNE can yield reliable and meaningful results in the context of a multicenter trial (Shefner et al., 2004). However, when using commercially available software for statistical MUNE, different investigators use different methods, particularly regarding recording window selection. In this respect, one of the standardization strategies involves the use of 10%-sized recording windows at four predefined levels over the scan curve (Lomen-Hoerth and Olney, 2001). As compared with using recording window levels determined by the program based on the initial scan curve, this method has been suggested to improve reproducibility of statistical MUNE (Olney et al., 2000). However, several problems have hampered its use in patients with progressive motor unit loss. The first is that a considerable number of submaximal CMAP responses fall beyond predefined windows in patients with motor unit loss and are excluded from variation analysis, which yields erroneously small SMUP estimates (Henderson et al., 2003). The second problem was demonstrated in a recent multicenter drug trial on ALS by Shefner and his colleagues (Shefner et al., 2004), namely, that mean SMUP estimates can be paradoxically small using this approach in ALS patients. This can be explained by the inability of this approach to differentiate between true alternation of motor units of small size and single motor unit variability arising from neuromuscular junction instability due to denervation (Jillapalli and Shefner, 2004). To address the issue as to how best to perform statistical MUNE, we applied two different approaches and compared results in healthy subjects and ALS patients. The two methods adopted for this study differed primarily in terms of setting and modifying recording windows. According to their key features, the two methods are referred to as the ‘expansion’ and ‘narrowing’ methods, respectively. Details of which are provided in the methods section below. 2. Subjects and methods 2.1. Subjects We investigated 12 healthy subjects (M:F = 4:8, age 52 ± 13 years), and 11 patients (M:F = 7:4, age 54 ± 10 years) with probable, laboratory-supported probable, or definite ALS according to the El Escorial criteria
(Brooks et al., 2000). All subjects underwent routine ulnar sensory and motor nerve conduction studies including the across-elbow segment to exclude ulnar neuropathies. Strengths of abductor digiti minini (ADM) in patients ranged from 4 to 5 on the Medical Research Council scale. The Institutional Review Board approved the study, and all subjects gave informed consent to participate. 2.2. Electrophysiological techniques A set of two statistical MUNE studies were performed in all 23 subjects using the ‘expansion’ and ‘narrowing’ methods sequentially by the same examiner (YH Hong) on the same day without changing electrodes. Surface CMAP recordings were made at the abductor digiti minini (ADM) by stimulating the ulnar nerve at the wrist 6–7 cm proximal to the active electrode with a flat bar electrode that was taped in place. MUNE tests were performed using a commercially available program (Viking IV, Nicolet Biomedical Inc., Madison, WI). Briefly, a stimulus–response curve was obtained by delivering 30 consecutive stimuli at 1 Hz, which increased in intensity by equal increments from threshold to just-maximal currents. The sizes and levels of the four recording windows (expressed as percentages of maximal CMAP amplitude) were then determined manually. At each recording window, 120–300 stimuli were presented in groups of 30, to determine mean SMUP amplitude by Poisson distribution analysis of 30 CMAPs elicited by 2 Hz stimuli of constant intensity. From four to ten groups of SMUPs were determined within each window to yield one mean SMUP estimate, and the recording was stopped after 4–9 groups if the standard error of the mean was less than 10% of the mean SMUP value. As mentioned above, the two MUNE studies differed primarily in how to set and modify recording windows. Specifically, for the first MUNE study, four 10%-sized windows were set at the following levels; 10–20%, 25– 35%, 40–50%, and 55–65% (Lomen-Hoerth and Olney, 2001). Stimuli were set such that the majority of responses occupied a lower quarter of the window, and then stimulus intensity was varied slightly to ensure that submaximal CMAP responses filled more than two-thirds of the window. The recording window was expanded if more than 50% of the responses fell outside the predefined window (Lomen-Hoerth and Olney, 2001). Accordingly, this approach is referred to as the ‘expansion’ method. For the second MUNE test, we set the recording windows in accord with rules designed to include the most ‘neurogenically compensated’ segments, that is, segments in which even a small change in stimulus intensity produced a large variability in CMAP responses, thus indicating the presence of abnormally large MUPs because of compensatory reinnervation (Kwon and Lee, 2004). Initially, four recording windows were selected at each quartile over the scan curve (0–25%, 25–50%, 50–75%, 75–100%). Before acquiring CMAP responses for variation analysis,
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it is important to determine the optimal intensity of stimuli to elicit the largest variation of CMAP responses at each recording window. To accomplish this, a test session was carried out after obtaining a stimulus–response curve. In this test session, CMAP responses were not stored for variation analysis, but were visually inspected with varying current intensities to search for the optimal stimulus intensity to ensure the largest variability in each quartile. After determining the optimal stimulus level, we acquired CMAP responses leaving the window unchanged while recording the distribution. The window tended to be filled with CMAPs in ALS patients who have a greater CMAP variability (i.e., a wider distribution). However, in cases where the distribution of CMAP responses was smaller than the preset window, tested areas were naturally narrowed down during the test, and thus, this approach is referred to as the ‘narrowing method’. Recording window was set small enough to exclude artifact signals such as fasciculations and movements, but large enough so as not to restrict physiologic variabilities. Thus, the size and level of recording windows can be modified so as not to exclude properly elicited CMAPs from variation analysis. Recording windows occasionally exceeded 25% in size, and their margins were allowed to be overlaid unless the motor units sampled had been overlapped, i.e., as long as a message of ‘Axon Overlap’ had not been offered by the program. A Poisson distribution was assumed when responses in a group showed a distribution skewed slightly to lower amplitudes, and we did not include any group not conforming to the Poisson distribution as defined above. An exception was the area with dichotomously distributed CMAPs which was not sufficiently large to be counted manually. In this area, stimulus intensity was adjusted slightly to elicit the CMAPs adjacent to the lower bank 15–20 times among 30 stimuli, which would make the dichotomatous distribution more likely to be ‘‘Poisson-like’’ (Kwon and Lee, 2004). A sample obtained from a patient illustrates how a recording window is set differently for the narrowing and expansion methods (Fig. 1). For both methods, average SMUP size (Av SMUP) and MUNE were calculated using the size-weighted average method, the formulae of which are as follows (Olney et al., 2000):
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Av-SMUP ¼ ½ðSMUP1 x1 Þ þ ðSMUP2 x2 Þ þ ðSMUP3 x3 Þ þ ðSMUP4 x4 Þ=ðx1 þ x2 þ x3 þ x4 Þ MUNE ¼ Maximal CMAP Av SMUP where xn represents the size of a tested area within a recording windown, and SMUPn indicates mean SMUP amplitude of a recording windown. The tested area, expressed as percentage of maximal CMAP, refers to the part of a preset window within which submaximal CMAP responses fell. 2.3. Statistical analysis Av SMUP size and MUNE value obtained using the narrowing and expansion methods were compared using Wilcoxon signed ranks test in healthy subjects and ALS patients. To analyze the relation between the size of tested area and mean SMUP estimates, we assumed that the tested area size would be proportional to the standard deviation of responses. Since the standard deviation is proportional to the square root of mean SMUP by definition, Pearson correlation analysis was performed to assess the relation between tested area size and the square root of mean SMUP. The Student’s t-test was used to compare tested area sizes for the narrowing and expansion methods in healthy subjects and ALS patients. Statistical significance was set at a p value of <0.05. 3. Results Maximal CMAP (peak-to-peak) amplitude was smaller in ALS patients (mean 8.3 mV, SD 2.0 mV, range 5.6– 12.1 mV) than in healthy subjects (mean 10.1 mV, SD 1.7 mV, range 7.7–13.5 mV). In both patient and control groups, four recording windows were selected such that a total of 48 and 44 SMUP sizes were estimated in the 12 healthy subjects and 11 ALS patients, respectively. No consistent gap larger than 10% size was encountered, so that the manual counting method did not have to be applied for the present study. Although an initial scan curve occasionally hinted gaps of >10%, these gaps were not consistently large enough to be counted manually because of
Fig. 1. An example waveform graph (a), histogram (b), and area graph (c) obtained from a moderately affected ALS patient. CMAP responses show a dichotomous, although not discrete, distribution with consecutive stimuli of the same intensity. A window is set to cover the entire 40–70% segment for the narrowing method to include large reinnervated units, whereas it is supposed to be initially placed at 40–50% range for the expansion method according to the predetermined scheme. Note that the gap shown is not large enough to be counted manually, and it is not straightforward whether the small variabilities in the upper and lower banks of the gap represent the physiologic activation of small motor units or the artifact arising from neuromuscular junction instability in denervated units.
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the presence of several inbetween responses in following tests. The number of groups for each run (mean ± SD) was not different between the narrowing and expansion methods for both healthy subjects (5.3 ± 1.8 vs. 4.9 ± 1.4, respectively; Mann–Whitney U test, p = 0.7) and ALS patients (4.9 ± 1.4 vs. 5.2 ± 1.5, respectively; p = 0.4). The sizes of SMUP estimates obtained using the expansion and narrowing methods are shown in Fig. 2. In healthy subjects, SMUP estimates were similar for the expansion (mean 74 lV, SD 27 lV, range 36–160 lV) and narrowing methods (mean 77 lV, SD 29 lV, range 29–171 lV). However, in ALS patients, SMUP estimates obtained using the expansion method (mean 146 lV, SD 76 lV, range 48– 391 lV) tended to be smaller than those obtained using the narrowing method (mean 199 lV, SD 90 lV, range 78–515 lV). Results obtained using the two different methods are summarized and compared in normal and ALS patients in Table 1. Expansion method-derived Av SMUP sizes and MUNE values differed little from those obtained using the narrowing method in healthy subjects, whereas the narrowing method resulted in significantly larger Av SMUP sizes and smaller MUNE values than those obtained using the expansion method in ALS patients (Wilcoxon signed ranks test, p = 0.003 for both
Av SMUP and MUNE). However, for both patient and control groups, Av SMUP and MUNE values obtained using either method were significantly correlated with those of the other method (Spearman correlation analysis, p < 0.05). The relation between the square root of mean SMUP estimate and tested area size was then analyzed. For both patient and control groups, the square root of mean SMUP estimate showed a linear correlation with tested area size for both methods (Pearson correlation analysis, all p < 0.001, Fig. 3). In healthy subjects, recording windows were not enlarged at all with the expansion method, and tested areas were also mostly 15% or smaller despite a preset window size of 25%. In ALS patients, however, windows were not enlarged for 27 SMUPs (61.4%) with the expansion method, whereas tested areas were greater than 10% in the vast majority (36 SMUPs, 81.8%) with the narrowing method. In Fig. 4, the sizes of tested areas (means ± SD) are shown to be significantly larger for the narrowing method in both groups, but with a much greater difference in ALS patients; 9.6 ± 3.1% vs. 7.9 ± 1.7% in healthy subjects, 16.1 ± 5.1% vs. 11.2 ± 3.0% in ALS patients (Student t-test, p = 0.001 for controls, p < 0.001 for patients).
Fig. 2. Distribution of SMUP estimates obtained using the narrowing and expansion methods in 12 healthy subjects (n = 48) and 11 ALS patients (n = 44). SMUPs are grouped in 20 lV bins; columns are numbered with median SMUP amplitudes.
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Table 1 Comparison of results obtained using the expansion and narrowing methods in healthy subjects and ALS patients Healthy subjects (12)
Av SMUP (lV) MUNE
ALS patients (11)
Expansion method
Narrowing method
p Value
R
Expansion method
Narrowing method
p Value
R
77 ± 18 129 ± 26
82 ± 20 127 ± 28
NS NS
0.81* 0.75*
128 ± 40 69 ± 25
179 ± 45 49 ± 15
0.003 0.003
0.72* 0.82*
Values are expressed as means ± SD. Comparisons were made using the Wilcoxon signed ranks test. NS, nonsignificant (p P 0.05); R, Spearman’s rho. * Correlations were significant at the p < 0.05 level.
Fig. 3. Correlations between the square root of mean SMUP estimate and tested area size in healthy subjects and ALS patients. Pearson correlation analysis. All p values <0.001.
4. Discussion In the present study, we applied two different approaches for statistical MUNE in healthy subjects and ALS patients. These two different methods, named the ‘expansion’ and ‘narrowing’ methods in the current study, yielded almost the same Av SMUP sizes and MUNE values in healthy subjects, but significantly different results in ALS patients. For the two methods, the square root of mean SMUP estimates showed a linear correlation with tested area sizes in both patient and healthy subject groups. However, the tested area size was significantly larger for the narrowing method particularly in ALS patients, which might in part account for those significantly different results in ALS patients. Therefore, as for the two different
methods used in the current study, our data show that the results of statistical MUNE in ALS patients, unlike in healthy subjects, heavily depend on the approach for setting and modifying recording windows. The effect of recording window size has been studied in healthy subjects by Lomen-Hoerth and Olney who reported that increasing the window size from 5% to 10% lowered MUNE values and improved test reproducibility (Lomen-Hoerth and Olney, 2001). The authors did not expand the window further, but based on their findings they raised a concern that artificially low MUNE values could result from an operator decision to expand a recording window in healthy subjects. In the present study, however, when a larger window size, i.e., 25%, was applied in healthy subjects, tested areas actually narrowed
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Fig. 4. Comparisons of the tested area size of the narrowing and expansion methods in healthy subjects (a) and ALS patients (b). Boxes are drawn with ends at the quartiles Q1 and Q3. A horizontal line in the box represents the median, and the whiskers are extended to the farthest points within 1.5 times the interquartile range of Q1 and Q3 from the end of a box. Outliers beyond the whiskers are plotted as separate filled circles. The Student’s t-test was used throughout.
down from the preset windows during test, and resulting MUNE values were not significantly decreased. In contrast, application of the larger window led to large tested areas resulting in significantly lower MUNE values in ALS patients, and the use of 10%-sized window seemed to exclude many properly elicited CMAP responses. Thus, it is likely that the previous observation by Lomen-Hoerth and Olney might reflect a ceiling effect of 5%-sized window for maximum response variability in heathy subjects. The ceiling effect, however, may not be significant at the size of window larger than 10% in healthy subjects, whereas it continues to affect the results in ALS patients according to the degree of MU loss and compensatory reinnervation. The large size difference in tested area between the narrowing and expansion methods in ALS patients can be attributed in part to the strict criterion (>50%) used for window expansion in the present study, and thus, this difference may have been resolved had looser criteria been applied. However, as the current program for statistical MUNE does not automatically quantify the number of rejected responses, this strategy does not appear to be a practical solution. Regarding the ceiling effect in the use of 10% window, an operational guideline (Bromberg, in press) recommends the use of absolute window size rather than percent range of total scan in severely affected patients. Specifically, in cases with a maximal CMAP of <2.5 mV, windows of 250 lV were preferred to avoid estimating artificially small SMUPs. Given that all patients included showed maximal CMAPs of >5 mV, this guideline may not be relevant for the present study. Nevertheless, the expansion method using a 10% window appeared to suffer from the ceiling effect when applied to our mildly affected ALS patients, which became evident when findings were compared with the narrowing method. It should be pointed out that the size and level of windows adopted for the narrowing method is somewhat arbitrary in the present study. We initially set the window size
at 25% in each quartile in order to obtain four representative SMUP estimates as evenly spaced as possible over the scan curve. The 25% window size was also found to be effective to exclude artificial signals such as fasciculations and movements in both healthy subjects and mildly affected ALS patients included in the present study. This is in line with the observation by Miller et al., that is, post-test data filtering based on 25%-sized window included a reasonable amount of data and excluded fewer than 5% of responses for both healthy subjects and mildly affected ALS patients (Miller et al., 2004). However, when applying statistical MUNE in more advanced ALS patients, it is not uncommon experience that the window size should be larger than 25% to perform statistical MUNE appropriately. In such cases, our practice is to widen window size further to avoid a ceiling effect for maximum response variability. The artificial outliers could be differentiated from physiologic activation of motor unit by visual inspection of any movements and fasciculations of the tested muscle, which would help to determine the optimal size of the window. An appropriate way of size adjustment, however, remains to be elucidated in future studies, particularly for advanced ALS patients. Alternatively, post-test data filtering without initial window setting could be a good way-out providing a means of excluding spurious signals from analysis and including the majority of data (Henderson et al., 2003; Miller et al., 2004). The narrowing method in our study adopted the strategy of selecting the most neurogenically compensated segments as test areas. Previously, Olney et al. have tried a similar approach selecting the recording windows based on the separation between consecutive CMAPs in the stimulus–response curve (Olney et al., 2000). The SMUP estimates obtained from the largest window failed to yield reproducible results, which might be attributed to variability of the scan curve that consisted of only 30 incremental stimuli in their study. The variability of scan curve, however, could be minimized by getting more than one
Y.-H. Hong et al. / Clinical Neurophysiology 118 (2007) 2605–2611
stimulus–response curve (Kwon and Lee, 2004), or to obtain the scan curve with a large number of stimuli, for example several hundreds (Henderson et al., 2006). Alternatively, a direct search for the segment having the largest CMAP variability through a test session, as performed in our narrowing method, would be another good solution to avoid or minimize possible variation in MU sampling. By selecting the most neurogenically compensated segments as test areas the narrowing method suffers from a bias toward large motor unit potentials, resulting in lower MUNE values than determined anatomically. Indeed, the present study showed that the narrowing method yielded 29% larger mean SMUP estimates and lower MUNE values compared with the expansion method when applied in mildly affected ALS patients. The bias, however, is not random, but rather systemic in nature, so that the reproducibility of this approach seems to be little affected. Moreover, despite the bias, this approach seems to have some advantages in terms of reproducibility. Whereas the use of a predetermined window set has turned out to be susceptible to artifactually small motor units (Shefner et al., 2004), the narrowing method is inherently far from this problem because the largest reinnervated units were sampled first. In addition, given that the ‘neurogenic distribution’, where the proportion of reinnervated motor units is increased, is associated with a much smaller estimation error, we could expect a better reproducibility for this method (Slawnych et al., 1997). Indeed, the test–retest reproducibility of statistical MUNE employing this approach has been reported to be high in ALS patients, with a coefficient of variation of 12.2% (Kwon and Lee, 2004). The greatest utility of MUNE is monitoring changes over time, and greater reproducibility may be more important for that purpose. Thus, it is likely that the advantage in reproducibility may outweigh the concomitant reduction in MUNE value. However, it remains to be seen whether the approach will reflect the progress of MU loss and compensatory reinnervation in a clinically meaningful way. A direct comparison with other MUNE techniques such as multiple point stimulation and spike triggered averaging may also help to clarify the utility and monitoring rates of this approach for statistical MUNE. To our knowledge, this study is the first to formally compare the two described approaches for statistical MUNE, i.e., the ‘expansion’ and ‘narrowing’ methods, in healthy subjects and ALS patients. The results obtained indicate that the expansion method using 10%-sized windows may result in erroneously small SMUP estimates
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due to the exclusion from variation analysis of properly elicited CMAPs beyond the predetermined recording window even in mildly affected ALS patients. The narrowing method may be considered as an alternative that could help us to avoid this problem. Further study is required to determine an optimal approach for the longitudinal application of statistical MUNE in ALS. References Bromberg M, editor. Motor unit number estimation (MUNE) 2003;vol. 55. Amsterdam: Elsevier; 2003. Bromberg, M.B. Updating motor unit number estimation (MUNE). Clin Neurophysiology, in press. doi:10.1016/j.clinph.2006.07.304. Brooks BR, Miller RG, Swash M, Munsat TL. 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 Mot Neuron Disord 2000;1:293–9. Daube JR. Estimating the number of motor units in a muscle. J Clin Neurophysiol 1995;12:585–94. Henderson RD, McClelland R, Daube J. Effect of changing data collection parameters on statistical motor unit number estimates. Muscle Nerve 2003;27:320–31. Henderson RD, Ridall GR, Pettitt AN, McCombe PA, Daube JR. The stimulus–response curve and motor unit variability in normal subjects and subjects with amyotrophic lateral sclerosis. Muscle Nerve 2006;34:34–43. Jillapalli D, Shefner J. Single motor unit variability with threshold stimulation in patients with amotrophic lateral sclerosis and normal subjects. Muscle Nerve 2004;30:578–84. Kwon O, Lee KW. Reproducibility of statistical motor unit number estimates in amyotrophic lateral sclerosis: comparisons between sizeand number-weighted modifications. Muscle Nerve 2004;29:211–7. Lomen-Hoerth C, Olney RK. Effect of recording window and stimulation variables on the statistical technique of motor unit number estimation. Muscle Nerve 2001;24:1659–64. Lomen-Hoerth C, Slawnych MP. Statistical motor unit number estimation: from theory to practice. Muscle Nerve 2003;28:263–72. McComas AJ, Fawcett PRW, Campbell MJ, Sica REP. Electrophysiological estimation of the number of motor units within a human muscle. J Neurol Neurosurg Psychiatry 1971;34:121–31. Miller T, Kogelnik A, Olney R. Proposed modification to data analysis for statistical motor unit number estimate. Muscle Nerve 2004;29: 700–6. Olney RK, Yuen EC, Engstrom JW. Statistical motor unit number estimation: reproducibility and sources of error in patients with amyotrophic lateral sclerosis. Muscle Nerve 2000;23:193–7. Shefner J. Motor unit number estimation in human neurologic diseases and animal models. Clin Neurophysiol 2001;112:955–64. Shefner J, Cudkowicz M, Zhang M, Schoenfeld D, Jillapalli D. Northeast ALS Consortium. The use of statistical MUNE in a multicenter clinical trial. Muscle Nerve 2004;30:463–9. Slawnych M, Laszlo C, Hershler C. Motor unit number estimation: sample size considerations. Muscle Nerve 1997;20:22–8.
Statistical MUNE: A comparison of two methods of setting recording windows in healthy subjects and ALS patients Yoon-Ho Hong a, Jung-Joon Sung b,*, Kyung Seok Park c, Ohyun Kwon d, Ju-Hong Min e, Kwang-Woo Lee b a
c
Department of Neurology, Seoul National University College of Medicine, Boramae Hospital, Seoul, Republic of Korea b Department of Neurology, Seoul National University College of Medicine, Seoul National University Hospital, 28 Yongon-Dong Chongno-gu, Seoul 110-744, Republic of Korea Department of Neurology, Seoul National University College of Medicine, Bundang Hospital, Seoul, Republic of Korea d Department of Neurology, Eulji General Hospital, Eulji University School of Medicine, Seoul, Republic of Korea e Department of Neurology, Chung-Ang University College of Medicine, Seoul, Republic of Korea
See Editorial, pages 2542–2543
Abstract Objective: To address the issue as to how best to perform statistical MUNE, we applied two different approaches and compared results in healthy subjects and ALS patients. Methods: Twelve normal subjects (women 8, mean age 52 years) and 11 ALS patients (women 4, mean age 54 years) underwent two consecutive MUNE studies, which differed in terms of setting and modifying the recording window. These are referred to as the ‘expansion’ and ‘narrowing’ methods, respectively. Size-weighted average (Av) SMUP and MUNE values were obtained using the two methods, and compared in control and patient groups. Results: Expansion method-derived Av SMUP sizes and MUNE values differed only slightly from those obtained using the narrowing method in healthy subjects, whereas the narrowing method resulted in significantly larger Av SMUP sizes and smaller MUNE values than the expansion method in ALS patients (Wilcoxon signed ranks test, p = 0.003). The sizes of tested areas (mean ± SD) were significantly larger for the narrowing method than the expansion method in both subject groups with much greater difference in ALS patients; 9.6 ± 3.1% vs. 7.9 ± 1.7% in healthy subjects and 16.1 ± 5.1% vs. 11.2 ± 3.0% in ALS patients (Student t-test, p < 0.01). Conclusions: The present study shows, unlike that found in normal subjects, that the results of statistical MUNE in ALS patients are heavily dependent on the approach used to set and modify recording windows. Significance: The expansion method using a 10%-sized window is likely to suffer from systemic errors due to the ceiling effect and the sampling of artifactually small motor units in ALS patients. The authors recommend that the narrowing method be considered as an alternative that avoids these problems. Ó 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. Keywords: Statistical MUNE; Recording window; ALS
1. Introduction Motor unit number estimation (MUNE) refers to an electrophysiological technique that estimates the number
*
Corresponding author. Tel.: +82 2 2072 1015; fax: +82 2 3672 7553. E-mail address: [email protected] (J.-J. Sung).
of functioning motor units in a given muscle or group of muscles (Shefner, 2001). MUNE is calculated by dividing the size of maximal compound muscle action potential (CMAP) by the size of average surface recorded motor unit potential (SMUP) (McComas et al., 1971). Following McComa’s first proposal in 1971, various MUNE techniques have been developed, which essentially obtain representative samples of SMUPs in different ways (Shefner,
1388-2457/$32.00 Ó 2007 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2007.05.073
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2001; Bromberg, 2003). Among these methods, statistical MUNE developed by Daube in 1995 is unique in that it uses the stochastic properties of variation in recorded submaximal CMAP to derive an estimate of mean SMUP size (Daube, 1995). Using this technique, motor units can be sampled over a wider response range from the stimulus– response curve, which means that sampled motor units are more likely to represent the whole motor unit population. The test–retest reproducibility of statistical MUNE has been reported to be good for both ALS patients and healthy subjects (Lomen-Hoerth and Slawnych, 2003), and it has been claimed that statistical MUNE can yield reliable and meaningful results in the context of a multicenter trial (Shefner et al., 2004). However, when using commercially available software for statistical MUNE, different investigators use different methods, particularly regarding recording window selection. In this respect, one of the standardization strategies involves the use of 10%-sized recording windows at four predefined levels over the scan curve (Lomen-Hoerth and Olney, 2001). As compared with using recording window levels determined by the program based on the initial scan curve, this method has been suggested to improve reproducibility of statistical MUNE (Olney et al., 2000). However, several problems have hampered its use in patients with progressive motor unit loss. The first is that a considerable number of submaximal CMAP responses fall beyond predefined windows in patients with motor unit loss and are excluded from variation analysis, which yields erroneously small SMUP estimates (Henderson et al., 2003). The second problem was demonstrated in a recent multicenter drug trial on ALS by Shefner and his colleagues (Shefner et al., 2004), namely, that mean SMUP estimates can be paradoxically small using this approach in ALS patients. This can be explained by the inability of this approach to differentiate between true alternation of motor units of small size and single motor unit variability arising from neuromuscular junction instability due to denervation (Jillapalli and Shefner, 2004). To address the issue as to how best to perform statistical MUNE, we applied two different approaches and compared results in healthy subjects and ALS patients. The two methods adopted for this study differed primarily in terms of setting and modifying recording windows. According to their key features, the two methods are referred to as the ‘expansion’ and ‘narrowing’ methods, respectively. Details of which are provided in the methods section below. 2. Subjects and methods 2.1. Subjects We investigated 12 healthy subjects (M:F = 4:8, age 52 ± 13 years), and 11 patients (M:F = 7:4, age 54 ± 10 years) with probable, laboratory-supported probable, or definite ALS according to the El Escorial criteria
(Brooks et al., 2000). All subjects underwent routine ulnar sensory and motor nerve conduction studies including the across-elbow segment to exclude ulnar neuropathies. Strengths of abductor digiti minini (ADM) in patients ranged from 4 to 5 on the Medical Research Council scale. The Institutional Review Board approved the study, and all subjects gave informed consent to participate. 2.2. Electrophysiological techniques A set of two statistical MUNE studies were performed in all 23 subjects using the ‘expansion’ and ‘narrowing’ methods sequentially by the same examiner (YH Hong) on the same day without changing electrodes. Surface CMAP recordings were made at the abductor digiti minini (ADM) by stimulating the ulnar nerve at the wrist 6–7 cm proximal to the active electrode with a flat bar electrode that was taped in place. MUNE tests were performed using a commercially available program (Viking IV, Nicolet Biomedical Inc., Madison, WI). Briefly, a stimulus–response curve was obtained by delivering 30 consecutive stimuli at 1 Hz, which increased in intensity by equal increments from threshold to just-maximal currents. The sizes and levels of the four recording windows (expressed as percentages of maximal CMAP amplitude) were then determined manually. At each recording window, 120–300 stimuli were presented in groups of 30, to determine mean SMUP amplitude by Poisson distribution analysis of 30 CMAPs elicited by 2 Hz stimuli of constant intensity. From four to ten groups of SMUPs were determined within each window to yield one mean SMUP estimate, and the recording was stopped after 4–9 groups if the standard error of the mean was less than 10% of the mean SMUP value. As mentioned above, the two MUNE studies differed primarily in how to set and modify recording windows. Specifically, for the first MUNE study, four 10%-sized windows were set at the following levels; 10–20%, 25– 35%, 40–50%, and 55–65% (Lomen-Hoerth and Olney, 2001). Stimuli were set such that the majority of responses occupied a lower quarter of the window, and then stimulus intensity was varied slightly to ensure that submaximal CMAP responses filled more than two-thirds of the window. The recording window was expanded if more than 50% of the responses fell outside the predefined window (Lomen-Hoerth and Olney, 2001). Accordingly, this approach is referred to as the ‘expansion’ method. For the second MUNE test, we set the recording windows in accord with rules designed to include the most ‘neurogenically compensated’ segments, that is, segments in which even a small change in stimulus intensity produced a large variability in CMAP responses, thus indicating the presence of abnormally large MUPs because of compensatory reinnervation (Kwon and Lee, 2004). Initially, four recording windows were selected at each quartile over the scan curve (0–25%, 25–50%, 50–75%, 75–100%). Before acquiring CMAP responses for variation analysis,
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it is important to determine the optimal intensity of stimuli to elicit the largest variation of CMAP responses at each recording window. To accomplish this, a test session was carried out after obtaining a stimulus–response curve. In this test session, CMAP responses were not stored for variation analysis, but were visually inspected with varying current intensities to search for the optimal stimulus intensity to ensure the largest variability in each quartile. After determining the optimal stimulus level, we acquired CMAP responses leaving the window unchanged while recording the distribution. The window tended to be filled with CMAPs in ALS patients who have a greater CMAP variability (i.e., a wider distribution). However, in cases where the distribution of CMAP responses was smaller than the preset window, tested areas were naturally narrowed down during the test, and thus, this approach is referred to as the ‘narrowing method’. Recording window was set small enough to exclude artifact signals such as fasciculations and movements, but large enough so as not to restrict physiologic variabilities. Thus, the size and level of recording windows can be modified so as not to exclude properly elicited CMAPs from variation analysis. Recording windows occasionally exceeded 25% in size, and their margins were allowed to be overlaid unless the motor units sampled had been overlapped, i.e., as long as a message of ‘Axon Overlap’ had not been offered by the program. A Poisson distribution was assumed when responses in a group showed a distribution skewed slightly to lower amplitudes, and we did not include any group not conforming to the Poisson distribution as defined above. An exception was the area with dichotomously distributed CMAPs which was not sufficiently large to be counted manually. In this area, stimulus intensity was adjusted slightly to elicit the CMAPs adjacent to the lower bank 15–20 times among 30 stimuli, which would make the dichotomatous distribution more likely to be ‘‘Poisson-like’’ (Kwon and Lee, 2004). A sample obtained from a patient illustrates how a recording window is set differently for the narrowing and expansion methods (Fig. 1). For both methods, average SMUP size (Av SMUP) and MUNE were calculated using the size-weighted average method, the formulae of which are as follows (Olney et al., 2000):
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Av-SMUP ¼ ½ðSMUP1 x1 Þ þ ðSMUP2 x2 Þ þ ðSMUP3 x3 Þ þ ðSMUP4 x4 Þ=ðx1 þ x2 þ x3 þ x4 Þ MUNE ¼ Maximal CMAP Av SMUP where xn represents the size of a tested area within a recording windown, and SMUPn indicates mean SMUP amplitude of a recording windown. The tested area, expressed as percentage of maximal CMAP, refers to the part of a preset window within which submaximal CMAP responses fell. 2.3. Statistical analysis Av SMUP size and MUNE value obtained using the narrowing and expansion methods were compared using Wilcoxon signed ranks test in healthy subjects and ALS patients. To analyze the relation between the size of tested area and mean SMUP estimates, we assumed that the tested area size would be proportional to the standard deviation of responses. Since the standard deviation is proportional to the square root of mean SMUP by definition, Pearson correlation analysis was performed to assess the relation between tested area size and the square root of mean SMUP. The Student’s t-test was used to compare tested area sizes for the narrowing and expansion methods in healthy subjects and ALS patients. Statistical significance was set at a p value of <0.05. 3. Results Maximal CMAP (peak-to-peak) amplitude was smaller in ALS patients (mean 8.3 mV, SD 2.0 mV, range 5.6– 12.1 mV) than in healthy subjects (mean 10.1 mV, SD 1.7 mV, range 7.7–13.5 mV). In both patient and control groups, four recording windows were selected such that a total of 48 and 44 SMUP sizes were estimated in the 12 healthy subjects and 11 ALS patients, respectively. No consistent gap larger than 10% size was encountered, so that the manual counting method did not have to be applied for the present study. Although an initial scan curve occasionally hinted gaps of >10%, these gaps were not consistently large enough to be counted manually because of
Fig. 1. An example waveform graph (a), histogram (b), and area graph (c) obtained from a moderately affected ALS patient. CMAP responses show a dichotomous, although not discrete, distribution with consecutive stimuli of the same intensity. A window is set to cover the entire 40–70% segment for the narrowing method to include large reinnervated units, whereas it is supposed to be initially placed at 40–50% range for the expansion method according to the predetermined scheme. Note that the gap shown is not large enough to be counted manually, and it is not straightforward whether the small variabilities in the upper and lower banks of the gap represent the physiologic activation of small motor units or the artifact arising from neuromuscular junction instability in denervated units.
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the presence of several inbetween responses in following tests. The number of groups for each run (mean ± SD) was not different between the narrowing and expansion methods for both healthy subjects (5.3 ± 1.8 vs. 4.9 ± 1.4, respectively; Mann–Whitney U test, p = 0.7) and ALS patients (4.9 ± 1.4 vs. 5.2 ± 1.5, respectively; p = 0.4). The sizes of SMUP estimates obtained using the expansion and narrowing methods are shown in Fig. 2. In healthy subjects, SMUP estimates were similar for the expansion (mean 74 lV, SD 27 lV, range 36–160 lV) and narrowing methods (mean 77 lV, SD 29 lV, range 29–171 lV). However, in ALS patients, SMUP estimates obtained using the expansion method (mean 146 lV, SD 76 lV, range 48– 391 lV) tended to be smaller than those obtained using the narrowing method (mean 199 lV, SD 90 lV, range 78–515 lV). Results obtained using the two different methods are summarized and compared in normal and ALS patients in Table 1. Expansion method-derived Av SMUP sizes and MUNE values differed little from those obtained using the narrowing method in healthy subjects, whereas the narrowing method resulted in significantly larger Av SMUP sizes and smaller MUNE values than those obtained using the expansion method in ALS patients (Wilcoxon signed ranks test, p = 0.003 for both
Av SMUP and MUNE). However, for both patient and control groups, Av SMUP and MUNE values obtained using either method were significantly correlated with those of the other method (Spearman correlation analysis, p < 0.05). The relation between the square root of mean SMUP estimate and tested area size was then analyzed. For both patient and control groups, the square root of mean SMUP estimate showed a linear correlation with tested area size for both methods (Pearson correlation analysis, all p < 0.001, Fig. 3). In healthy subjects, recording windows were not enlarged at all with the expansion method, and tested areas were also mostly 15% or smaller despite a preset window size of 25%. In ALS patients, however, windows were not enlarged for 27 SMUPs (61.4%) with the expansion method, whereas tested areas were greater than 10% in the vast majority (36 SMUPs, 81.8%) with the narrowing method. In Fig. 4, the sizes of tested areas (means ± SD) are shown to be significantly larger for the narrowing method in both groups, but with a much greater difference in ALS patients; 9.6 ± 3.1% vs. 7.9 ± 1.7% in healthy subjects, 16.1 ± 5.1% vs. 11.2 ± 3.0% in ALS patients (Student t-test, p = 0.001 for controls, p < 0.001 for patients).
Fig. 2. Distribution of SMUP estimates obtained using the narrowing and expansion methods in 12 healthy subjects (n = 48) and 11 ALS patients (n = 44). SMUPs are grouped in 20 lV bins; columns are numbered with median SMUP amplitudes.
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Table 1 Comparison of results obtained using the expansion and narrowing methods in healthy subjects and ALS patients Healthy subjects (12)
Av SMUP (lV) MUNE
ALS patients (11)
Expansion method
Narrowing method
p Value
R
Expansion method
Narrowing method
p Value
R
77 ± 18 129 ± 26
82 ± 20 127 ± 28
NS NS
0.81* 0.75*
128 ± 40 69 ± 25
179 ± 45 49 ± 15
0.003 0.003
0.72* 0.82*
Values are expressed as means ± SD. Comparisons were made using the Wilcoxon signed ranks test. NS, nonsignificant (p P 0.05); R, Spearman’s rho. * Correlations were significant at the p < 0.05 level.
Fig. 3. Correlations between the square root of mean SMUP estimate and tested area size in healthy subjects and ALS patients. Pearson correlation analysis. All p values <0.001.
4. Discussion In the present study, we applied two different approaches for statistical MUNE in healthy subjects and ALS patients. These two different methods, named the ‘expansion’ and ‘narrowing’ methods in the current study, yielded almost the same Av SMUP sizes and MUNE values in healthy subjects, but significantly different results in ALS patients. For the two methods, the square root of mean SMUP estimates showed a linear correlation with tested area sizes in both patient and healthy subject groups. However, the tested area size was significantly larger for the narrowing method particularly in ALS patients, which might in part account for those significantly different results in ALS patients. Therefore, as for the two different
methods used in the current study, our data show that the results of statistical MUNE in ALS patients, unlike in healthy subjects, heavily depend on the approach for setting and modifying recording windows. The effect of recording window size has been studied in healthy subjects by Lomen-Hoerth and Olney who reported that increasing the window size from 5% to 10% lowered MUNE values and improved test reproducibility (Lomen-Hoerth and Olney, 2001). The authors did not expand the window further, but based on their findings they raised a concern that artificially low MUNE values could result from an operator decision to expand a recording window in healthy subjects. In the present study, however, when a larger window size, i.e., 25%, was applied in healthy subjects, tested areas actually narrowed
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Fig. 4. Comparisons of the tested area size of the narrowing and expansion methods in healthy subjects (a) and ALS patients (b). Boxes are drawn with ends at the quartiles Q1 and Q3. A horizontal line in the box represents the median, and the whiskers are extended to the farthest points within 1.5 times the interquartile range of Q1 and Q3 from the end of a box. Outliers beyond the whiskers are plotted as separate filled circles. The Student’s t-test was used throughout.
down from the preset windows during test, and resulting MUNE values were not significantly decreased. In contrast, application of the larger window led to large tested areas resulting in significantly lower MUNE values in ALS patients, and the use of 10%-sized window seemed to exclude many properly elicited CMAP responses. Thus, it is likely that the previous observation by Lomen-Hoerth and Olney might reflect a ceiling effect of 5%-sized window for maximum response variability in heathy subjects. The ceiling effect, however, may not be significant at the size of window larger than 10% in healthy subjects, whereas it continues to affect the results in ALS patients according to the degree of MU loss and compensatory reinnervation. The large size difference in tested area between the narrowing and expansion methods in ALS patients can be attributed in part to the strict criterion (>50%) used for window expansion in the present study, and thus, this difference may have been resolved had looser criteria been applied. However, as the current program for statistical MUNE does not automatically quantify the number of rejected responses, this strategy does not appear to be a practical solution. Regarding the ceiling effect in the use of 10% window, an operational guideline (Bromberg, in press) recommends the use of absolute window size rather than percent range of total scan in severely affected patients. Specifically, in cases with a maximal CMAP of <2.5 mV, windows of 250 lV were preferred to avoid estimating artificially small SMUPs. Given that all patients included showed maximal CMAPs of >5 mV, this guideline may not be relevant for the present study. Nevertheless, the expansion method using a 10% window appeared to suffer from the ceiling effect when applied to our mildly affected ALS patients, which became evident when findings were compared with the narrowing method. It should be pointed out that the size and level of windows adopted for the narrowing method is somewhat arbitrary in the present study. We initially set the window size
at 25% in each quartile in order to obtain four representative SMUP estimates as evenly spaced as possible over the scan curve. The 25% window size was also found to be effective to exclude artificial signals such as fasciculations and movements in both healthy subjects and mildly affected ALS patients included in the present study. This is in line with the observation by Miller et al., that is, post-test data filtering based on 25%-sized window included a reasonable amount of data and excluded fewer than 5% of responses for both healthy subjects and mildly affected ALS patients (Miller et al., 2004). However, when applying statistical MUNE in more advanced ALS patients, it is not uncommon experience that the window size should be larger than 25% to perform statistical MUNE appropriately. In such cases, our practice is to widen window size further to avoid a ceiling effect for maximum response variability. The artificial outliers could be differentiated from physiologic activation of motor unit by visual inspection of any movements and fasciculations of the tested muscle, which would help to determine the optimal size of the window. An appropriate way of size adjustment, however, remains to be elucidated in future studies, particularly for advanced ALS patients. Alternatively, post-test data filtering without initial window setting could be a good way-out providing a means of excluding spurious signals from analysis and including the majority of data (Henderson et al., 2003; Miller et al., 2004). The narrowing method in our study adopted the strategy of selecting the most neurogenically compensated segments as test areas. Previously, Olney et al. have tried a similar approach selecting the recording windows based on the separation between consecutive CMAPs in the stimulus–response curve (Olney et al., 2000). The SMUP estimates obtained from the largest window failed to yield reproducible results, which might be attributed to variability of the scan curve that consisted of only 30 incremental stimuli in their study. The variability of scan curve, however, could be minimized by getting more than one
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stimulus–response curve (Kwon and Lee, 2004), or to obtain the scan curve with a large number of stimuli, for example several hundreds (Henderson et al., 2006). Alternatively, a direct search for the segment having the largest CMAP variability through a test session, as performed in our narrowing method, would be another good solution to avoid or minimize possible variation in MU sampling. By selecting the most neurogenically compensated segments as test areas the narrowing method suffers from a bias toward large motor unit potentials, resulting in lower MUNE values than determined anatomically. Indeed, the present study showed that the narrowing method yielded 29% larger mean SMUP estimates and lower MUNE values compared with the expansion method when applied in mildly affected ALS patients. The bias, however, is not random, but rather systemic in nature, so that the reproducibility of this approach seems to be little affected. Moreover, despite the bias, this approach seems to have some advantages in terms of reproducibility. Whereas the use of a predetermined window set has turned out to be susceptible to artifactually small motor units (Shefner et al., 2004), the narrowing method is inherently far from this problem because the largest reinnervated units were sampled first. In addition, given that the ‘neurogenic distribution’, where the proportion of reinnervated motor units is increased, is associated with a much smaller estimation error, we could expect a better reproducibility for this method (Slawnych et al., 1997). Indeed, the test–retest reproducibility of statistical MUNE employing this approach has been reported to be high in ALS patients, with a coefficient of variation of 12.2% (Kwon and Lee, 2004). The greatest utility of MUNE is monitoring changes over time, and greater reproducibility may be more important for that purpose. Thus, it is likely that the advantage in reproducibility may outweigh the concomitant reduction in MUNE value. However, it remains to be seen whether the approach will reflect the progress of MU loss and compensatory reinnervation in a clinically meaningful way. A direct comparison with other MUNE techniques such as multiple point stimulation and spike triggered averaging may also help to clarify the utility and monitoring rates of this approach for statistical MUNE. To our knowledge, this study is the first to formally compare the two described approaches for statistical MUNE, i.e., the ‘expansion’ and ‘narrowing’ methods, in healthy subjects and ALS patients. The results obtained indicate that the expansion method using 10%-sized windows may result in erroneously small SMUP estimates
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due to the exclusion from variation analysis of properly elicited CMAPs beyond the predetermined recording window even in mildly affected ALS patients. The narrowing method may be considered as an alternative that could help us to avoid this problem. Further study is required to determine an optimal approach for the longitudinal application of statistical MUNE in ALS. References Bromberg M, editor. Motor unit number estimation (MUNE) 2003;vol. 55. Amsterdam: Elsevier; 2003. Bromberg, M.B. Updating motor unit number estimation (MUNE). Clin Neurophysiology, in press. doi:10.1016/j.clinph.2006.07.304. Brooks BR, Miller RG, Swash M, Munsat TL. 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 Mot Neuron Disord 2000;1:293–9. Daube JR. Estimating the number of motor units in a muscle. J Clin Neurophysiol 1995;12:585–94. Henderson RD, McClelland R, Daube J. Effect of changing data collection parameters on statistical motor unit number estimates. Muscle Nerve 2003;27:320–31. Henderson RD, Ridall GR, Pettitt AN, McCombe PA, Daube JR. The stimulus–response curve and motor unit variability in normal subjects and subjects with amyotrophic lateral sclerosis. Muscle Nerve 2006;34:34–43. Jillapalli D, Shefner J. Single motor unit variability with threshold stimulation in patients with amotrophic lateral sclerosis and normal subjects. Muscle Nerve 2004;30:578–84. Kwon O, Lee KW. Reproducibility of statistical motor unit number estimates in amyotrophic lateral sclerosis: comparisons between sizeand number-weighted modifications. Muscle Nerve 2004;29:211–7. Lomen-Hoerth C, Olney RK. Effect of recording window and stimulation variables on the statistical technique of motor unit number estimation. Muscle Nerve 2001;24:1659–64. Lomen-Hoerth C, Slawnych MP. Statistical motor unit number estimation: from theory to practice. Muscle Nerve 2003;28:263–72. McComas AJ, Fawcett PRW, Campbell MJ, Sica REP. Electrophysiological estimation of the number of motor units within a human muscle. J Neurol Neurosurg Psychiatry 1971;34:121–31. Miller T, Kogelnik A, Olney R. Proposed modification to data analysis for statistical motor unit number estimate. Muscle Nerve 2004;29: 700–6. Olney RK, Yuen EC, Engstrom JW. Statistical motor unit number estimation: reproducibility and sources of error in patients with amyotrophic lateral sclerosis. Muscle Nerve 2000;23:193–7. Shefner J. Motor unit number estimation in human neurologic diseases and animal models. Clin Neurophysiol 2001;112:955–64. Shefner J, Cudkowicz M, Zhang M, Schoenfeld D, Jillapalli D. Northeast ALS Consortium. The use of statistical MUNE in a multicenter clinical trial. Muscle Nerve 2004;30:463–9. Slawnych M, Laszlo C, Hershler C. Motor unit number estimation: sample size considerations. Muscle Nerve 1997;20:22–8.