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

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


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

Date of Submission16-Feb-2021
Date of Acceptance24-Feb-2021
Date of Web Publication26-Mar-2021

Correspondence Address:
Hatice Tankisi
Department of Clinical Neurophysiology, Aarhus University Hospital, Palle Juul-Jensens Boulevard, Aarhus N 8200
Denmark
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/nsn.nsn_30_21

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  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.

Keywords: Electrodiagnosis, motor unit number estimation, MScanFit


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 Oct 25];38:1-5. Available from: http://www.nsnjournal.org/text.asp?2021/38/1/1/311966



[TAG:2]Introduction[/TAG:2]

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 Top


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 Top


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: Compound mucle action potential scans in abductor pollicis brevis muscle from a healthy subject with (a) a typical normal S-shaped form and (b) from a patient with amyotrophic lateral sclerosis

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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: Compound mucle action potential scans in abductor pollicis brevis muscle with the original scans (black) and modeled scans (magenda) from (a) a healthy subject, (b) a patient with amyotophic lateral sclerosis with moderately loss of motor units and (c) a patient with amyotophic lateral sclerosis with severe loss of motor units. MScanFit parameters are shown in the box for each scan. MUNE: Motor unit number estimation, mV: Millivolt, μV: Microvolt

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Table 1: MScanFit parameters

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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: Exsisting MScanFit studies

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  Concluding Remarks Top


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 Top

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2.
McComas 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.  Back to cited text no. 2
    
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20.
Jacobsen 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.  Back to cited text no. 20
    
21.
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22.
Kristensen 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.  Back to cited text no. 22
    
23.
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Sirin 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.  Back to cited text no. 24
    
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29.
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