Rebuttal from Farina

Abstract

POINT-COUNTERPOINTRebuttal from FarinaPublished Online:01 Nov 2008https://doi.org/10.1152/japplphysiol.90598.2008cMoreSectionsPDF (36 KB)Download PDF ToolsExport citationAdd to favoritesGet permissionsTrack citations ShareShare onFacebookTwitterLinkedInEmailWeChat Although von Tscharner and Nigg (8) focus on the time-frequency analysis of the surface EMG and the results obtained in a number of experimental studies (e.g., Refs. 6, 7), their point of view illustrates the general issues that I outlined in my Counterpoint (1). A fundamental confusion is apparent in one of the opening statements of my colleagues: “A refined subdivision of fibers was proposed correlating fiber types with EMG spectral properties” (8). According to this statement, slow and fast fibers are defined as fibers with surface action potentials with relative energy mainly at low and high frequencies, respectively, and this definition is considered more appropriate (“refined”) than the schemes based on histochemistry (type I and II) and physiology (slow and fast twitch). Surface EMG spectral properties are thus used to directly define, rather than indirectly identify, muscle fiber types, which is the central issue in this debate. Consistent with this perspective, the main evidence that my colleagues present to illustrate their Point (8) consists in the wavelet transform of surface EMG signals recorded during running. They associate high (low) frequencies in the surface EMG to recruitment of fast (slow) motor units, although they do not provide a direct measure or a theoretical rationale to prove this association when a histochemical or physiological definition of fiber type is used.In response to the unequivocal experimental evidence of inconsistency of surface EMG spectral properties with motor unit recruitment strategies in simple cases when these strategies are predictable (e.g., 2, 3, 10), my colleagues reply that when studying “single-task experiments such as isometric ramped increase in force…substantial changes in the power spectra should not be expected.” However, surprisingly, they support their own interpretations with results reported for this type of contraction (4, 5, 9). While my colleagues consider the lack of consistency of the association between EMG spectral analysis and recruitment strategies irrelevant because it comes from “single-task experiments”), the occasional observation of this association during the same type of “single-ask experiments” is used as the basis for a “refined subdivision” of fiber types.The assertion by von Tscharner and Nigg (8) that surface EMG spectral properties provide information about recruitment strategies and fiber-type proportions is based on distinguishing between surface motor unit action potentials and not on the physiological properties of the motor units.REFERENCES1 Farina D. Counterpoint: Spectral properties of the surface EMG do not provide information about motor unit recruitment and muscle fiber type. J Appl Physiol; doi:10.1152/japplphysiol.90598.2008a.Link | ISI | Google Scholar2 Gabriel DA, Kamen G. Experimental and modeling investigation of spectral compression of biceps brachii SEMG activity with increasing force levels. J Electromyogr Kinesiol. 2007 Dec 11. [Epub ahead of print].Google Scholar3 Gerdle B, Karlsson S, Crenshaw AG, Fridén J. The relationships between EMG and muscle morphology throughout sustained static knee extension at two submaximal force levels. Acta Physiol Scand 160: 341–351, 1997.Crossref | PubMed | Google Scholar4 Karlsson S, Gerdle B. Mean frequency and signal amplitude of the surface EMG of the quadriceps muscles increase with increasing torque—a study using the continuous wavelet transform. J Electromyogr Kinesiol 11: 131–140, 2001.Crossref | ISI | Google Scholar5 Solomonow M, Baten C, Smit J, Baratta R, Hermens H, D'Ambrosia R, Shoji H. Electromyogram power spectra frequencies associated with motor unit recruitment strategies. J Appl Physiol 68: 1177–1185, 1990.Link | ISI | Google Scholar6 von Tscharner V, Goepfert B. Estimation of the interplay between groups of fast and slow muscle fibers of the tibialis anterior and gastrocnemius muscle while running. J Electromyogr Kinesiol 16: 188–197, 2006.Crossref | ISI | Google Scholar7 von Tscharner V, Goepfert B, Nigg BM. Changes in EMG signals for the muscle tibialis anterior while running barefoot or with shoes resolved by non-linearly scaled wavelets. J Biomech 36: 1169–1176, 2003.Crossref | ISI | Google Scholar8 von Tscharner V, Nigg BM. Point: Spectral properties of the surface EMG provide information about motor unit recruitment and muscle fiber type. J Appl Physiol; doi:10.1152/japplphysiol.90598.2008.Link | ISI | Google Scholar9 Wakeling JM, Syme DA. Wave properties of action potentials from fast and slow motor units of rats. Muscle Nerve 26: 659–668, 2002.Crossref | PubMed | ISI | Google Scholar10 Westbury JR, Shaughnessy TG. Associations between spectral representation of the surface electromyogram and fiber type distribution and size in human masseter muscle. Electromyogr Clin Neurophysiol 27: 427–435, 1987.Google Scholar Download PDF Previous Back to Top Next FiguresReferencesRelatedInformation More from this issue > Volume 105Issue 5November 2008Pages 1675-1675 Copyright & PermissionsCopyright © 2008 the American Physiological Societyhttps://doi.org/10.1152/japplphysiol.90598.2008cHistory Published online 1 November 2008 Published in print 1 November 2008 Metrics