Reducing intersubject variability in motor unit number estimation

Shefner, Jeremy M.; Jillapalli, Devanand; Bradshaw, Deborah Young

doi:10.1002/(sici)1097-4598(199910)22:10<1457::aid-mus18>3.0.co;2-t

Cited by 65 publications

(42 citation statements)

References 7 publications

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“…The mean size of the individual SMUPs that are activated intermittently is calculated from the variance of the CMAP. The total MUNE is calculated by dividing the maximal CMAP size by the calculated mean SMUP sizes obtained at different stimulus intensities 16. The statistical method is a useful means of calculating MUNE in normal subjects11, 17 and in patients with neurogenic diseases 5, 10, 14, 16…”

Section: Comparison Of Study Methods With the Standard Existing Technimentioning

confidence: 99%

“…For both technical and statistical reasons, different authors have restricted the data samples from which the variance is calculated (see Table 1). Thus, the selection of the set of data for variance analysis has varied among investigators 11, 14, 16. Typically, the smallest 20% of the CMAPs obtained at single stimulus intensity are not included in the calculation, i.e., only the upper 80% are used.…”

Section: Comparison Of Study Methods With the Standard Existing Technimentioning

confidence: 99%

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Effect of changing data collection parameters on statistical motor unit number estimates

2003

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The effect of number of samples and selection of data for analysis on the calculation of surface motor unit potential (SMUP) size in the statistical method of motor unit number estimates (MUNE) was determined in 10 normal subjects and 10 with amyotrophic lateral sclerosis (ALS). We recorded 500 sequential compound muscle action potentials (CMAPs) at three different stable stimulus intensities (10-50% of maximal CMAP). Estimated mean SMUP sizes were calculated using Poisson statistical assumptions from the variance of 500 sequential CMAP obtained at each stimulus intensity. The results with the 500 data points were compared with smaller subsets from the same data set. The results using a range of 50-80% of the 500 data points were compared with the full 500. The effect of restricting analysis to data between 5-20% of the CMAP and to standard deviation limits was also assessed. No differences in mean SMUP size were found with stimulus intensity or use of different ranges of data. Consistency was improved with a greater sample number. Data within 5% of CMAP size gave both increased consistency and reduced mean SMUP size in many subjects, but excluded valid responses present at that stimulus intensity. These changes were more prominent in ALS patients in whom the presence of isolated SMUP responses was a striking difference from normal subjects. Noise, spurious data, and large SMUP limited the Poisson assumptions. When these factors are considered, consistent statistical MUNE can be calculated from a continuous sequence of data points. A 2 to 2.5 SD or 10% window are reasonable methods of limiting data for analysis.

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Section: Comparison Of Study Methods With the Standard Existing Technimentioning

confidence: 99%

Section: Comparison Of Study Methods With the Standard Existing Technimentioning

confidence: 99%

Effect of changing data collection parameters on statistical motor unit number estimates

2003

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“…Traditionally the agreement between testing sessions is assessed using regression plots. To be consistent with the literature (Bromberg et al, 1993;Boe et al, 2006;Felice, 1995;Shefner et al, 1999), Pearson product-moment coefficients of correlation (r) was calculated. The Pearson correlation results was found to be low for amplitude (r = 0.24), duration (r = −0.14), number of phases (r = −0.30), number of turns (r = 0.14), mean MU firing rate (r = −0.04) and moderate for AAR (r = 0.56).…”

Section: Test-retest Limits Of Agreementmentioning

confidence: 99%

Reliability of quantitative EMG analysis of the extensor carpi radialis muscle

Calder

Agnew

Stashuk

et al. 2008

Journal of Neuroscience Methods

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“…20 This technique is also being employed currently in affected and unaffected carriers of the SOD gene in families with a history of amyotrophic lateral sclerosis as a means of identifying a pre-symptomatic phase of familial ALS. 21 Numerous modifications of the statistical method have been reported, but the one most widely adopted is the method of weighted averaging, 22 which weights the projection of SMUAP size more judiciously across the stimulus spectrum, increasing reproducibility.…”

Section: Multiple Point Stimulationmentioning

confidence: 99%

Mune

Cl¹,

Jm²

2004

Amyotrophic Lateral Sclerosis and Other Motor Neuron Disorders

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Over the last decade, motor unit number estimation (MUNE) methods have been applied with increasing frequency to the study of amyotrophic lateral sclerosis. MUNE is the ideal tool for the assessment of diseases in which the primary defect is motor unit loss, as it enables quantitation and tracking of motor unit numbers while simultaneously gauging countervailing collateral reinervation. These properties make it particularly useful for assessing the effects of both neuroprotective therapies and therapies designed to enhance collateral reinervation, not only in animal models but also in the living patient. Previous studies have supplied important natural history information, confirming an average 50% loss of motor units for every six months of disease progression, and newer pathophysiological investigations are providing unique insight into motor unit behavior in the face of progressive anterior horn cell death. More recent efforts have incorporated MUNE into ongoing, multi-center clinical trials as a putative early biomarker, with encouraging results. As MUNE methods continue to be refined and disseminated, they are proving to be useful and unique tools for the study of motor neuron disease.

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Reducing intersubject variability in motor unit number estimation

Cited by 65 publications

References 7 publications

Effect of changing data collection parameters on statistical motor unit number estimates

Effect of changing data collection parameters on statistical motor unit number estimates

Reliability of quantitative EMG analysis of the extensor carpi radialis muscle

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