Neurological Sciences and Neurophysiology

REVIEW ARTICLE
Year
: 2021  |  Volume : 38  |  Issue : 1  |  Page : 1--5

MScanFit motor unit number estimation: A novel method for clinics and research


Hatice Tankisi 
 Department of Clinical Neurophysiology, Aarhus University Hospital, Aarhus, Denmark

Correspondence Address:
Hatice Tankisi
Department of Clinical Neurophysiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard, Aarhus N 8200
Denmark

Abstract

Motor unit number estimation (MUNE) methods have been found to be better suited than any other electrophysiological test to study the degree and time course of lower motor unit loss. However, MUNE methods have not yet been implemented in clinics and research. This may be because an ideal method has not been developed yet. This review aims to give an overview of the strengths and limitations of the existing MUNE methods, why a new method was necessary and how the novel MScanFit MUNE can overcome some of the limitations that the other methods had. In the end, the existing literature MScanFit applied has been summarised.



How to cite this article:
Tankisi H. MScanFit motor unit number estimation: A novel method for clinics and research.Neurol Sci Neurophysiol 2021;38:1-5


How to cite this URL:
Tankisi H. MScanFit motor unit number estimation: A novel method for clinics and research. Neurol Sci Neurophysiol [serial online] 2021 [cited 2021 Dec 5 ];38:1-5
Available from: http://www.nsnjournal.org/text.asp?2021/38/1/1/311966


Full Text



 Introduction



Motor unit number estimation (MUNE) methods have long been of interest to measure lower motor neuron loss. In muscles with denervation, measurement of muscle strength does not reflect the number of remaining motor units because of collateral sprouting, because healthy axons take over the muscle territory of axons that have been lost. Similarly, compound muscle action potential (CMAP) amplitude does not fall in value until 50% or more of motor units are lost due to collateral sprouting, therefore conventional nerve conduction studies cannot provide accurate information about the number of motor units lost. Accordingly, the degree of denervation in electromyography (EMG) does not correlate with the magnitude of motor unit loss. Besides, we are measuring the reinnervation rather than directly loss of motor unit with motor unit potential (MUP) analysis and in patients with fast disease progress such as amyotrophic lateral sclerosis, MUPs may look normal despite the severe loss of motor units. Thus, MUNE is better suited than any other electrophysiological test to study the degree and time course of lower motor unit loss.[1]

 The Strengths and Limitations of the Existing Motor Unit Number Estimation Methods



Incremental stimulation MUNE is the first method introduced in 1971 by McComas et al.[2] Since a number of different methods has been developed, and all methods have their strengths and limitations. Here, the most often used methods, shortly their methodologies, and the advantages and disadvantages for each method will be mentioned.

In all methods, a maximal CMAP by supramaximal stimulation is obtained and the methods differ in how the surface MUPs (sMUP) are sampled. In most methods, the average of approximately 10 sMUPs is divided into the maximal CMAP amplitude or area to calculate the MUNE value.

For the incremental stimulation MUNE, sMUPs are obtained by gradually increasing the stimulus intensity to recruit additional motor units. The main limitation of this method is the phenomenon called alternation whereby stimuli of the same strength could activate different combinations of individual motor units.[1],[3] For multipoint stimulation MUNE, sMUPs are obtained by stimulating the nerve at different points along the nerve to sample different motor axons.[3],[4] This method suffers from phase cancellation.[3] Alternation and phase cancellation limitations have been tried to be compensated by combining these two methods and performing incremental stimulation MUNE up to 3 steps at three different sites along the nerve, called adapted multipoint stimulation MUNE.[1],[5] Both incremental and multipoint stimulation MUNE require around 10 sMUPs to calculate the average sMUP, therefore these are time-consuming methods. In a recent study, the reproducibility and sensitivity of multipoint stimulation MUNE was found similar by the collection of 5 and 10 sMUPs, thereby reducing the time-consumption.[6] Both methods have the strength that they can be performed in any EMG machine without any requirement of special software and no special training is necessary. Spike-trigger averaging MUNE method differs from incremental and multipoint stimulation MUNE by obtaining sMUPs with an invasive needle EMG approach and voluntary muscle contraction.[1],[7] Hence, in addition to time-consumption, this method suffers from the limitations of being invasive and the requirement of training.

All these above-mentioned three methods are not automated, thereby have also the limitation of subjectivity. Among the automated MUNE methods, statistical MUNE using Poisson distribution statistics and the variability in motor unit firing has gained interest for several years,[1],[8] however, the requirement of specific software in certain EMG machines limited its use. In addition, this method appears limited when individual large motor units are measured at a particular stimulus level rather than a Poisson distribution of motor units. Another automated statistical method is CMAP Scan MUNE using Bayesian statistics introduced by Henderson et al.[9],[10] This method has not been used by other groups because of the requirement of specific software and several hours of analysis time. Among all methods, a more recently introduced motor unit number index (Munix) method has gained the most popularity.[11],[12],[13] Munix is using the surface interference patterns recorded during voluntary contractions to extract the average size of surface-recorded MUPs, therefore has the advantages of being fast, noninvasive and not being uncomfortable.[11] However, in a recent study, the Munix value extracted from surface recorded MUPs has been found to be highly correlated with CMAP amplitude, suggesting that Munix methodology may not be providing more information than simple CMAP amplitude measurements.[14] Several previous studies reported that special attention for an accurate CMAP amplitude is crucial for adequate Munix examination without mentioning this as a limitation,[12],[13] but further studies are warranted to examine whether Munix provides more information than CMAP amplitude.

Overall, the common criticisms of current MUNE methods include the presence of subjectivity in the estimation process, time-consumption, requirement of special training, and the failure to obtain a representative sample of units. MScanFit MUNE was introduced to overcome these limitations. An ideal MUNE method should be fast, automated, noninvasive, and easy to learn and apply.

 From Compound Muscle Action Potential Scans to Mscanfit Motor Unit Number Estimation



CMAP Scans were studied by different groups using visual and semi-quantitative analyses.[15],[16] In a normal subject, CMAP Scan has an S-shaped curve [Figure 1]a, and with the disease this changes to a stepwise shape [Figure 1]b. Among semi-quantitative methods, the parameters that were extracted from the CMAP scan are the maximum CMAP, the stimulus intensities (SIs) that elicited 5%, 50%, and 95% of the maximum CMAP (S5, S50, and S95, respectively), the absolute SI range (S95–S5), the relative SI range (S95–S5)/S5) and step percentage (step%).[15] Another parameter derived from CMAP Scans is D50 (the number of largest consecutive differences of recorded responses generating 50% of maximum CMAP) has been suggested to be a sensitive measure in ALS.[17] As mentioned above, a Bayesian statistics approach to calculate a MUNE value from CMAP Scan has been introduced by Henderson et al.[9],[10]{Figure 1}

The most recent MUNE method introduced in 2016 by professor Hugh Bostock, MScanFit calculates MUNE values from CMAP Scans by taking into account the probabilistic nature of motor unit firing.[18] First, a preliminary model is generated based on the change in the mean and standard deviation of response as a function of stimulus in the main scan and then the model is progressively improved by changing individual motor unit parameters[18],[19] [Figure 2]. MUNE analyses require a specific QTRACW© (Institute of Neurology, University College London, UK, distributed by Digitimer Ltd.). For CMAP Scan recordings, nerve excitability set-up and QTRACW© are usually used but any CMAP Scan recorded by conventional methods in EMG machines can be analyzed using a freeware MScanFit analysis program. MScanFit has shown a higher reproducibility and sensitivity than two more traditional methods, MUNIX and MPS, and a better ability to determine the disease progression of ALS.[20],[21] MScanFit has the advantages of being easy and fast to perform and automated fast analyses, noninvasive, and not requiring skilled operating staff.[20] With these features, the technique is attractive as a possibly useful diagnostic tool or endpoint measure in clinical drug trials. There are several parameters that can be extracted from the analyses. These parameters are listed in [Table 1].{Figure 2}{Table 1}

MScanFit has been applied in a various number of diseases including ALS,[19],[20],[21],[22],[23],[24],[25],[26] neurofibromatosis,[27] diabetic polyneuropathy,[28],[29] multifocal motor neuropathy,[30] spinal cord injury,[31] spinal muscular atrophy,[32] chemotherapy-induced polyneuropathy[33] and acute nerve injury.[34] In these studies, MScanFit showed higher sensitivities in detecting motor unit loss than conventional electrophysiological tests and clinical scores. In addition, the method has been applied on healthy subjects in the above-listed studies as well as in studies where only healthy controls were included to test the reproducibility[35] and applicability[36],[37] of MScanFit in different muscles. All existing MScanFit studies are summarized in [Table 2].{Table 2}

 Concluding Remarks



MScanFit is a promising novel MUNE method that can be a potential biomarker for the detection of motor unit loss and monitoring disease progress in neuromuscular disorders. However, the method still needs to be tested in multicenter studies in large cohorts.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.

References

1Gooch CL, Doherty TJ, Chan KM, Bromberg MB, Lewis RA, Stashuk DW, et al. Motor unit number estimation: A technology and literature review. Muscle Nerve 2014;50:884-93.
2McComas AJ, Fawcett PR, Campbell MJ, Sica RE. Electrophysiological estimation of the number of motor units within a human muscle. J Neurol Neurosurg Psychiatry 1971;34:121-31.
3Daube JR, Gooch C, Shefner J, Olney R, Felice K, Bromberg M. Motor unit number estimation (MUNE) with nerve conduction studies. Suppl Clin Neurophysiol 2000;53:112-5.
4Porter CL, Alvarez A, Jones KE, Ming CK. Test – retest reliability of a modified multiple point stimulation technique for motor unit number estimation. Clin Neurophysiol 2008;119:2287-90.
5Shefner JM, Watson ML, Simionescu L, Caress JB, Burns TM, Maragakis NJ, et al. Multipoint incremental motor unit number estimation as an outcome measure in ALS. Neurology 2011;77:235-41.
6Oliveira Santos M, Jacobsen AB, Tankisi H. Multiple Point Stimulation MUNE in ALS: Toward a faster modification. J Clin Neurophysiol 2019;36:220-3.
7Bromberg MB. Motor unit estimation: Reproducibility of the spike-triggered averaging technique in normal and ALS subjects. Muscle Nerve 1993;16:466-71.
8Blok JH, Visser GH, de Graaf S, Zwarts MJ, Stegeman DF. Statistical motor number estimation assuming a binomial distribution. Muscle Nerve 2005;31:182-91.
9Henderson RD, Ridall PG, Hutchinson NM, Pettitt AN, McCombe PA. Bayesian statistical MUNE method. Muscle Nerve 2007;36:206-13.
10Ridall PG, Pettitt AN, Henderson RD, McCombe PA. Motor unit number estimation – A Bayesian approach. Biometrics 2006;62:1235-50.
11Nandedkar SD, Nandedkar DS, Barkhaus PE, Stalberg EV. Motor unit number index (MUNIX). IEEE Trans Biomed Eng 2004;51:2209-11.
12Nandedkar SD, Barkhaus PE, Stålberg EV. Motor unit number index (MUNIX): Principle, method, and findings in healthy subjects and in patients with motor neuron disease. Muscle Nerve 2010;42:798-807.
13Neuwirth C, Nandedkar S, Stålberg E, Barkhaus PE, Carvalho Md, Furtula J, et al. Motor unit number index (MUNIX): A novel neurophysiological marker for neuromuscular disorders; test-retest reliability in healthy volunteers. Clin Neurophysiol 2011;122:1867-72.
14Bostock H, Jacobsen AB, Tankisi H. Motor unit number index and compound muscle action potential amplitude. Clin Neurophysiol 2019;130:1734-40.
15Maathuis EM, Drenthen J, Visser GH, Blok JH. Reproducibility of the CMAP scan. J Electromyogr Kinesiol 2011;21:433-7.
16Blok JH, van Dijk JP, Drenthen J, Maathuis EM, Stegeman DF. Size does matter: The influence of motor unit potential size on statistical motor unit number estimates in healthy subjects. Clin Neurophysiol 2010;121:1772-80.
17Sleutjes BT, Montfoort I, Maathuis EM, Drenthen J, van Doorn PA, Visser GH, et al. CMAP scan discontinuities: Automated detection and relation to motor unit loss. Clin Neurophysiol 2014;125:388-95.
18Bostock H. Estimating motor unit numbers from a CMAP scan. Muscle Nerve 2016;53:889-96.
19Jacobsen AB, Bostock H, Tankisi H. CMAP scan MUNE (MScan) – A novel motor unit number estimation (MUNE) Method. J Vis Exp 2018;(136):56805.
20Jacobsen AB, Bostock H, Fuglsang-Frederiksen A, Duez L, Beniczky S, Møller AT, et al. Reproducibility, and sensitivity to motor unit loss in amyotrophic lateral sclerosis, of a novel MUNE method: MScanFit MUNE. Clin Neurophysiol 2017;128:1380-8.
21Jacobsen AB, Bostock H, Tankisi H. Following disease progression in motor neuron disorders with 3 motor unit number estimation methods. Muscle Nerve 2019;59:82-7.
22Kristensen RS, Bostock H, Tan SV, Witt A, Fuglsang-Frederiksen A, Qerama E, et al. MScanFit motor unit number estimation (MScan) and muscle velocity recovery cycle recordings in amyotrophic lateral sclerosis patients. Clin Neurophysiol 2019;130:1280-8.
23Jacobsen AB, Kristensen RS, Witt A, Kristensen AG, Duez L, Beniczky S, et al. The utility of motor unit number estimation methods versus quantitative motor unit potential analysis in diagnosis of ALS. Clin Neurophysiol 2018;129:646-53.
24Sirin NG, Oguz Akarsu E, Kocasoy Orhan E, Erbas B, Artug T, Dede HO, et al. Parameters derived from compound muscle action potential scan for discriminating amyotrophic lateral sclerosis-related denervation. Muscle Nerve 2019;60:400-8.
25Oguz Akarsu E, Sirin NG, Kocasoy Orhan E, Erbas B, Dede HO, Baslo MB, et al. Repeater F-waves in amyotrophic lateral sclerosis: Electrophysiologic indicators of upper or lower motor neuron involvement? Clin Neurophysiol 2020;131:96-105.
26Gunes T, Sirin NG, Sahin S, Kose E, Isak B. Use of CMAP, MScan fit-MUNE, and MUNIX in understanding neurodegeneration pattern of ALS and detection of early motor neuron loss in daily practice. Neurosci Lett 2021;741:135488.
27Farschtschi S, Gelderblom M, Buschbaum S, Bostock H, Grafe P, Mautner VF. Muscle action potential scans and ultrasound imaging in neurofibromatosis type 2. Muscle Nerve 2017;55:350-8.
28Kristensen AG, Bostock H, Finnerup NB, Andersen H, Jensen TS, Gylfadottir S, et al. Detection of early motor involvement in diabetic polyneuropathy using a novel MUNE method – MScanFit MUNE. Clin Neurophysiol 2019;130:1981-7.
29Kristensen AG, Khan KS, Bostock H, Khan BS, Gylfadottir S, Andersen H, et al. MScanFit motor unit number estimation and muscle velocity recovery cycle recordings in diabetic polyneuropathy. Clin Neurophysiol 2020;131:2591-9.
30Garg N, Heard RNS, Kiers L, Gerraty R, Yiannikas C. Multifocal motor neuropathy presenting as pseudodystonia. Mov Disord Clin Pract 2017;4:100-4.
31Witt A, Fuglsang-Frederiksen A, Finnerup NB, Kasch H, Tankisi H. Detecting peripheral motor nervous system involvement in chronic spinal cord injury using two novel methods: MScanFit MUNE and muscle velocity recovery cycles. Clin Neurophysiol 2020;131:2383-92.
32Kariyawasam D, D'Silva A, Howells J, Herbert K, Geelan-Small P, Lin CS, et al. Motor unit changes in children with symptomatic spinal muscular atrophy treated with nusinersen. J Neurol Neurosurg Psychiatry 2020;92:78-85. Published online 2020 Oct 26. doi: 10.1136/jnnp-2020-324254.
33Bennedsgaard K, Ventzel L, Andersen NT, Themistocleous AC, Bennett DL, Jensen TS, et al. Oxaliplatin- and docetaxel-induced polyneuropathy: Clinical and neurophysiological characteristics. J Peripher Nerv Syst 2020;25:377-87.
34Kesim-Sahin O, Sirin NG, Erbas B, Artug T, Oguz-Akarsu E, Kocasoy-Orhan E, et al. Compound muscle action potential scan and MScanFit motor unit number estimation during Wallerian degeneration after nerve transections. Muscle Nerve 2020;62:239-46.
35Higashihara M, Menon P, van den Bos M, Pavey N, Vucic S. Reproducibility of motor unit number index and MScanFit motor unit number estimation across intrinsic hand muscles. Muscle Nerve 2020;62:192-200.
36Li X, Zong Y, Klein CS, Zhou P. Motor unit number estimation of human abductor hallucis from a compound muscle action potential scan. Muscle Nerve 2018;58:735-7.
37Zong Y, Lu Z, Zhang L, Li X, Zhou P. Motor unit number of the first dorsal interosseous muscle estimated from CMAP scan with different pulse widths and steps. J Neural Eng 2020;17:014001.