Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects

Clinical Neurophysiology xxx (2015) xxx–xxx

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Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects Maddison L. Hourigan a, Neal B. McKinnon b, Marjorie Johnson a, Charles L. Rice a,b, Daniel W. Stashuk e, Timothy J. Doherty b,c,d,⇑ a

Department of Anatomy and Cell Biology, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada School of Kinesiology, Faculty of Health Sciences, Western University, London, Ontario, Canada c Department of Physical Medicine and Rehabilitation, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada d Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada e Department of Systems Design Engineering, University of Waterloo, Waterloo, Ontario, Canada b

a r t i c l e

i n f o

Article history: Accepted 2 February 2015 Available online xxxx Keywords: Aging Decomposition-based quantitative electromyography (DQEMG) Jiggle Motor unit potential (MUP) Neuromuscular transmission Skeletal muscle

h i g h l i g h t s  Motor unit potential (MUP) shape variability was quantified across consecutive motor unit (MU) dis-

charges in healthy older men compared to young control subjects.  Near fiber (NF) jiggle was significantly higher in the older age group, and was significantly correlated

with multiple MUP parameters indicative of MU loss.  NF jiggle may be a valuable quantitative measure used in conjunction with other MUP parameters

indicative of MU remodeling and the stability of neuromuscular transmission.

a b s t r a c t Objective: To study the potential utility of using near fiber (NF) jiggle as an assessment of neuromuscular transmission stability in healthy older subjects using decomposition-based quantitative electromyography (DQEMG). Methods: The tibialis anterior (TA) and vastus medialis (VM) muscles were tested in 9 older men (77 ± 5 years) and 9 young male control subjects (23 ± 0.3 years). Simultaneous surface and needle-detected electromyographic (EMG) signals were collected during voluntary contractions, and then analyzed using DQEMG. Motor unit potential (MUP) and NF MUP parameters were analyzed. Results: NF jiggle was significantly increased for both the TA and VM in the old age group relative to the younger controls (P < 0.05). NF jiggle was significantly higher in the TA compared to VM (P < 0.05). For TA, NF jiggle was negatively correlated with MUNE, and positively correlated with S-MUP amplitude, NF count, MUP duration, MUP peak-to-peak voltage, and MUP area (P < 0.05). For VM, NF jiggle was positively correlated with NF count and MUP area (P < 0.05), and no significant correlations were found between NF jiggle and S-MUP amplitude, MUP duration, or MUP peak-to-peak voltage (MUNE was not calculated for VM, so no correlation could be made). Conclusions: Healthy aging is associated with neuromuscular transmission instability (increased NF jiggle) and MU remodeling, which can be measured using DQEMG. Significance: NF jiggle derived from DQEMG can be a useful method of identifying neuromuscular dysfunction at various stages of MU remodeling and aging. Ó 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

⇑ Corresponding author at: Department of Physical Medicine and Rehabilitation, Schulich School of Medicine and Dentistry, Western University, Parkwood Hospital, St. Joseph’s Health Care, 801 Commissioners Rd. East, London, Ontario N6C 5J1, Canada. Tel.: +1 519 685 4292x45062; fax: +1 519 685 4017. E-mail address: [email protected] (T.J. Doherty).

1. Introduction Sarcopenia describes the decline in skeletal muscle mass, strength, and contractile quality associated with the normal aging

http://dx.doi.org/10.1016/j.clinph.2015.02.002 1388-2457/Ó 2015 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002

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M.L. Hourigan et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

process (Doherty, 2003). Skeletal muscle atrophy and weakness associated with sarcopenia ultimately leads to profound functional impairment, disability, and when severe, loss of independence (Baumgartner et al., 1998; Roubenoff, 2001; Janssen et al., 2002). There are a number of factors that contribute to the development or progression of sarcopenia. Numerous studies suggest that the most significant cause of skeletal muscle strength decline, as well as associated disability and functional impairment, is the loss of skeletal muscle mass (Doherty, 2003). Evidence including reduction in type I and II muscle fibers, type II muscle fiber atrophy, muscle fiber grouping, and coexpression of myosin heavy chain isoforms in the same muscle fiber suggest the occurrence of a chronic progressive denervation and reinnervation process (Essen-Gustavsson and Borges, 1986; Oertel, 1986; Andersen et al., 1999; Doherty, 2003; Kovacic et al., 2009). It has been shown that axons damaged due to natural aging undergo a ‘‘dying back’’ process whereby the distal axon regresses towards the cell body (Misgeld, 2011; Manini et al., 2013). This axonal damage seen in senescence is thought to be caused by inflammation and oxidative damage to myelinated peripheral nerves (Kovacic et al., 2009; Opalach et al., 2010; Misgeld, 2011; Manini et al., 2013) that preferentially effects type II muscle fibers (Krutki et al., 2013). Thus it has been postulated that one of the more significant causes of sarcopenia is the loss of alpha motor neuron innervation to muscle with age (Brown, 1972; Essen-Gustavsson and Borges, 1986; Doherty and Brown, 1993, 1997; Roos et al., 1997; Doherty, 2003). Anatomical data are consistent with this hypothesis from studies showing decreased anterior horn cells in the spinal cord and decreased ventral roots with age (Kawamura et al., 1977a,b; Tomlinson and Irving, 1977; Mittal and Logmani, 1987; Doherty and Brown, 1993; Doherty et al., 1993; Doherty, 2003; McNeil et al., 2005a). The change in alpha motor neuron innervation can be studied in vivo electrophysiologically using quantitative electromyographic (EMG) techniques, which allow for the examination of MU number and size. As a result of collateral reinnervation, the process by which surviving motor units (MUs) supply new nerve sprouts to denervated muscle fibers, the recorded surface and needle detected MU potentials (MUPs) of surviving MUs often have higher amplitudes and longer durations. As a result of fewer contributing MUs, the firing rates are often increased for a given level of contractile force (Larsson and Ansved, 1995; Gordon et al., 2004). Krutki et al. (2013) reported in mice an age-related decrease in MUP amplitude for MUs comprised of type II muscle fibers with a corresponding decrease in force production, but a twofold increase in mean MUP amplitude for MUs comprised of type I muscle fibers; suggesting that aging targets type II MUs first, with a relative preservation of type I fibers due to the collateral reinnervation process. Using electrophysiological techniques, age-related declines in MU number estimates (MUNEs) have been found in many upper and lower limb muscles (Brown, 1972; Campbell et al., 1973; Sica et al., 1974; Vandervoort and McComas, 1986; Brown et al., 1988; Doherty and Brown, 1993; Doherty et al., 1993; Galea, 1996; Murga Oporto et al., 2003; McNeil et al., 2005b). McNeil et al. (2005b) found that collateral reinnervation compensated for MU loss and maintained skeletal muscle strength until a critical threshold of MU loss was reached in the very old (McNeil et al., 2005b). Decomposition-based quantitative electromyography (DQEMG) is capable of extracting clinically useful information regarding the MU pool from simultaneously acquired surface and needle-detected signals (Stashuk, 1999a). DQEMG has been shown to be a valid and reliable tool for obtaining information regarding MU number, size, and complexity within healthy subjects, as well as from patients with neuromuscular disease and peripheral neuropathy (Boe et al., 2004; McNeil et al., 2005a,b; Boe et al., 2006, 2009, 2010; Calder et al., 2008; Berger et al., 2011; Ives and Doherty,

2012, 2014; Allen et al., 2013). Using these techniques, we can evaluate the extent of collateral reinnervation following MU loss and the stability of neuromuscular transmission. Whereas the exact mechanism is not entirely understood, evidence suggests that age-related post-synaptic damage and muscle fiber atrophy lead to neuromuscular junction remodeling and impaired neuromuscular transmission (Deschenes, 2011; Jang and Van Remmen, 2011; Li et al., 2011; Manini et al., 2013). DQEMG can be used to assess the stability of neuromuscular transmission by measuring the shape variability of MUPs created by a single MU. Two key aspects of MUP shape variability measured electrophysiologically are jitter and jiggle (Stalberg and Sonoo, 1994). Jitter refers to the variability in the time intervals between pairs of individual muscle fiber contributions to MUPs detected across consecutive discharges of a single MU, and is increased in cases of disturbed neuromuscular transmission. In more severe cases, intermittent failures in neuromuscular transmission may occur, which is referred to as impulse blocking (Stalberg and Sonoo, 1994). Jitter has been extensively studied and will not be the focus of this study. Jiggle describes the amount of variability in the overall shape of MUPs detected across consecutive discharges of a single MU (Stalberg and Sonoo, 1994). Increased jiggle is thought to be the result of increased jitter and impulse blocking of the contributing single fiber action potentials (Stalberg and Sonoo, 1994). In contrast to jitter, the quantification of jiggle has been minimally studied. Prior studies were limited by the EMG algorithms available at the time, but two studies still found higher jiggle values in the small number of MUPs studied in patients with ALS (Stalberg and Sonoo, 1994; Campos et al., 2000). Another study conducted by Benatar et al. (2006) found significantly increased jiggle in patients with myasthenia gravis; a disorder of neuromuscular transmission. Most recently, increased jiggle was found in subjects with diabetic neuropathy (Allen et al., 2014). Despite advancements in quantitative EMG, the validity of using jiggle as a clinically useful MUP parameter has yet to be studied. The objective of our study was to assess whether jiggle could be a valuable MUP parameter indicative of the stability of neuromuscular transmission. The jiggle of MUPs detected in tibialis anterior (TA) and vastus medialis (VM) muscles of healthy older men compared to young controls was quantified. More specifically, near fiber (NF) jiggle (Allen et al., 2014), as opposed to traditional jiggle (Stalberg and Sonoo, 1994) was measured. NF jiggle is primarily related to the consistency of the activity and contributions of muscle fibers close to the selective detection surface of a concentric needle electrode (within 350 lm) (Stashuk, 1999b). NF jiggle in a proximal (VM) versus a distal (TA) lower limb muscle was also compared. In addition, NF jiggle was compared to other MUP parameters, including indicators of MUP size and complexity, and to MUNEs. Finally, traditional jiggle was measured for comparison to NF jiggle. The following was hypothesized: (i) NF jiggle would be significantly increased in old men compared to younger controls; (ii) based on findings from previous studies that showed greater age-related neurogenic changes in distal muscles, there would be greater changes in NF jiggle in the TA muscle compared to the VM muscle (Campbell et al., 1973; Galea, 1996); (iii) NF jiggle would be negatively correlated with MUNE; (iv) NF jiggle would be positively correlated with increased values of MUP parameters indicative of collateral reinnervation including surface MUP (SMUP) amplitude, as well as MUP duration, area, and peak-to-peak voltage; (v) NF jiggle would be positively correlated with NF count, a measurement similar to fiber density that reflects the number of muscle fibers close to the selective detection surface of a concentric needle contributing to a recorded MUP, and therefore related to reinnervation (Stashuk, 1999b); and (vi) traditional jiggle would be lower than NF jiggle and exhibit non-significant age-related changes.

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002

M.L. Hourigan et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

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Nine young men (mean 23 ± 0.3 years) and nine older men (mean 77 ± 5 years) volunteered for this study. Participants in the younger age group were recruited from the Western University community. Subjects in the older group were recruited from the Retirement Research Association of the Canadian Center for Activity and Aging, a local exercise group associated with Western University. Subjects in both groups were excluded if they had any evidence of neuromuscular or musculoskeletal disease that would impact their results. All subjects gave written, informed consent in accordance with the Western University Health Sciences Research Ethics Board, which approved this study (File number: 103980).

maintaining the desired contraction intensity. Subsequent submaximal 30 s contractions were performed until a minimum of 20 suitable MUP trains were collected. Each contraction was separated by a 30–60 s rest period, or as required by the participant. In order to collect information from different portions of the muscle, the needle position was adjusted between contractions by small alterations in the depth or angle, or the needle was inserted at a new site (Doherty and Stashuk, 2003; Boe et al., 2004; McNeil et al., 2005b; Ives and Doherty, 2012, 2014). This same protocol was repeated for the VM muscle, but a maximum CMAP was not elicited due to limitations in obtaining a valid, reliable CMAP for this muscle, and subsequently MUNEs for the VM muscles were not calculated. Additionally, the subject performed voluntary knee extension, not dorsiflexion, to obtain an EMG signal from this muscle.

2.2. Electromyographic data collection

2.3. Electromyographic signal decomposition and analysis

The EMG data were collected using DQEMG and the Viking EMG system software (Nicolet, Natus Medical Incorporated). Self-adhering Silver Mactrode electrodes (GE Medical Systems, Milwaukee, WI) were used to detect surface signals, and 25 mm  30 gauge disposable concentric needle electrodes (TECA elite, CareFusion, Middleton, WI) were used to detect intramuscular signals, with bandpass settings of 5 Hz to 1 kHz and 10 Hz to 10 kHz, respectively (Doherty and Stashuk, 2003; Boe et al., 2004; Ives and Doherty, 2012, 2014). Surface EMG signals were sampled at 4.8 kHz; intramuscular EMG signals were sampled at 48 kHz. For EMG data collection, the skin was cleansed with isopropyl alcohol, and then the surface electrodes were positioned. The surface electrodes were cut into 1 cm  3.5 cm strips for the active and reference electrodes, and full-sized (2.5 cm  3.5 cm) electrodes were used for the ground. For the TA, the active electrode was positioned over the motor point of the muscle, approximately 7 cm distal from the tibial tuberosity and 2 cm lateral to the anterior tibial border. The reference electrode was positioned over the distal tendon of the TA muscle, and the ground electrode was placed over the patella (McNeil et al., 2005b). For the VM, the active electrode was positioned over the mid-belly of the muscle, the reference was placed over the patellar tendon, and the ground was placed over the patella (Berger et al., 2011). For the TA muscle, a handheld bipolar stimulator was used to stimulate the common fibular nerve posterior to the fibular head in order to elicit a maximum compound muscle action potential (CMAP). The active electrode was moved in small increments in order to ensure optimum positioning of the active electrode, which was indicated by maximized CMAP negative peak amplitude with a minimal rise time. The stimulus intensity was gradually increased until the CMAP negative peak amplitude reached a plateau. Automatically positioned markers according to the onset, negative peak, positive peak, and end of the CMAP were manually adjusted if necessary, with size related parameters calculated automatically by the software (Boe et al., 2004; Doherty and Stashuk, 2003). Next, a concentric needle electrode was inserted into the belly of the TA muscle between 2 and 10 mm proximal or distal to the active surface electrode. Subjects were asked to perform minimal isometric ankle dorsiflexion contractions while an optimal needle position was determined that minimized the rise times of the MUPs generated by the first few recruited MUs. When an optimal needle position was located, subjects were asked to increase their contraction intensity to 40–60 pulses per second (pps). Each submaximal contraction was held for 30 s. During each contraction, the subject received auditory and visual feedback from the EMG signal, auditory feedback and encouragement from the examiner, and information regarding the intensity of the EMG signal (pps) was displayed on the computer screen to aid subjects in

The algorithms associated with DQEMG have been previously described (Stashuk, 1999a; Doherty and Stashuk, 2003). DQEMG automatically decomposes the intramuscular (needle) EMG signal into individual MUP trains. Individual firings from each MUP train are then used as triggers to isolate the time-locked S-MUPs from the recorded surface EMG signal, a process referred to as spike-triggered averaging. This information is then used to derive an ensemble-averaged MUP and S-MUP template associated with each sampled MU (Stashuk, 1999a; Doherty and Stashuk, 2003; Ives and Doherty, 2012, 2014). In addition, the MUP template generated by DQEMG was high-pass filtered using a second order low-pass differentiator (Stashuk, 1999b) to create a NF MUP template (which is the acceleration of the MUP template) comprised primarily of the contributions of fibers that are close (within 350 lm) to the selective detection surface of the concentric needle electrode. The NF MUP template was then used to analyze NF count, in addition to other NF MUP parameters that will not be reported here. As in previous DQEMG-based studies, the acceptability of the extracted MUP trains and derived MUP templates and S-MUP templates were then examined according to specific criteria, and excluded from further analysis if the inclusion criteria were not met (Boe et al., 2004, 2005; Ives and Doherty, 2012, 2014). MUP trains were required to have at least 51 MUPs, and to represent a consistent and physiological MU firing rate. The latter requirement was assumed to be met if the inter-discharge-interval histogram had a Gaussian-shaped main peak, with a coefficient of variation of <0.3 and the instantaneous firing rate plot had a physiologically consistent mean value and was free of abrupt firing rate changes. MUP templates that appeared to be primarily composed of cannula recorded potentials based on their shape were excluded, however, the corresponding S-MUP template was still accepted (cannula potentials still serve as accurate triggers for spike-triggered averaging). Two MUP trains identified by the DQEMG program as being ‘disparate’ meant that their potentials never fired concurrently, and are suspected to be derived from the same MU. In this case, the MUP train raster plots were visually examined to see if they were in fact from the same MU, in which case the MUP train with fewer discharges was excluded. The MUP template and S-MUP template markers were then visually inspected and manually adjusted if necessary. If the onset of each MUP template and its corresponding S-MUP template did not occur within 10 ms of each other, the S-MUP template was excluded from further analysis. Finally, S-MUP templates were required to have a signal-to-noise ratio of P10:1 in order to be accepted (Stashuk, 1999a; Doherty and Stashuk, 2003; Boe et al., 2004, 2005; Ives and Doherty, 2012, 2014). Various parameters regarding the shape, size, and complexity of all accepted MUP templates and S-MUP templates, as well as MU

2. Methods 2.1. Participants

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002

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M.L. Hourigan et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

firing patterns were automatically calculated by DQEMG. For the TA, a MUNE was automatically calculated by dividing the negative peak amplitude of the CMAP by the negative peak amplitude of the mean S-MUP (obtained by averaging all accepted S-MUP templates) (Boe et al., 2004). 2.4. Near fiber count and near fiber jiggle analysis As previously described, the MUP template generated by DQEMG was high-pass filtered using a second order low-pass differentiator in order to create a NF MUP template (Stashuk, 1999b). For the NF count, a distinct near fiber contribution to the NF MUP was identified if there was a positive turn with sufficient symmetry and amplitude within the NF MUP signal. Jiggle is a statistic, which is intended to measure the shape variability of the MUPs produced by a single MU. As such, successful MUP shape variability (jiggle) measurement requires the extraction of sets of ‘‘isolated’’ MUPs created by a single MU. MUPs detected using traditional bandpass filtering have significant contributions from a wide spatial area around the needle detection surface, and therefore are relatively more likely to be contaminated by the activity of other MUs than MUPs detected using high-pass filtering (i.e. NF MUPs). The contributions of distant fibers to NF MUPs are greatly reduced. Therefore, NF MUPs are less likely to be contaminated by the activity of other MUs. The stability of the activity of MU fibers close to the needle detection surface can therefore more easily be measured compared to the stability of the activity of the larger sets of MU fibers contributing to MUPs detected using traditional filtering bandwidths. Measurement of the shape variability of the MUPs in a MUP train requires several criteria to be met: 1. The MUP train must accurately reflect the activity of a single MU and it must not represent the combined activity of two or more MUs (i.e., it should not be a merged train). However, it does not need to contain a MUP related to every firing of the MU. Therefore, superimposed MUPs do not need to be resolved into their individual MUP composition. They can simply be excluded. 2. The MUPs of an extracted MUP train that are significantly contaminated by the activity of other MUs (i.e., technically are partially superimposed) need to be excluded and only isolated MUPs need be selected for shape variability analysis. 3. The statistic used to measure MUP shape variability must be able to adapt to signal nonstationarity caused by needle movement. In this work, these criteria were met by: 1. Using DQEMG to perform the EMG signal decompositions. DQEMG has been developed to accurately extract MUP trains, which represent the activity of single MUs. It uses both MUP shape and MU firing pattern information extracted from the EMG signal being decomposed. It does not try to resolve superimposed MUPs. MUP trains are examined to increase the likelihood that they represent the activity of a single MU. 2. ‘‘Isolated’’ NF MUPs (NF MUPs which are not significantly contaminated by the activity of other MUs) were selected using statistically-based pattern recognition methods. In addition, an operator viewed rasters of the NF MUPs of each MUP train analyzed and excluded contaminated NF MUPs. (The ‘‘true’’ shape variability of the NF MUPs of a MUP train is impossible to measure with complete accuracy. It is impossible to be certain when changes in MUP shapes are caused by MUP shape variability or contamination from other MUs. However, a quantitative

algorithm and consistent operator review were used to improve the accuracy with which isolated NF MUPs were selected and in turn improve the accuracy of the measures of NF MUP shape variability (NF jiggle) obtained. 3. The jiggle statistic, introduced by Stalberg and Sonoo (1994), is based on measuring differences between MUPs detected during consecutive MU discharges. This allowed slow trends in MUP size, due to needle movement, to be successfully tracked. Fig. 1a and b show MUPT analysis results from a younger and older subject, respectively.

2.5. Statistics Group mean MUP parameters (not including NF jiggle) were compared between age groups for each muscle. For the TA, the MUP peak-to-peak voltage, MUP duration, MUP area, NF count, and traditional jiggle were compared using a one-way analysis of variance. The S-MUP amplitude, CMAP amplitude, and MUNE did not have homogeneous variances, and were thus compared using a Welch analysis of variance. For the VM, the S-MUP amplitude, MUP peak-to-peak voltage, MUP duration, MUP area, NF count, and traditional jiggle were compared using a one-way analysis of variance. Group mean NF jiggle values were compared using a two-way analysis of variance to establish the effect of age, muscle group, and a combined age and muscle group effect on NF jiggle. Correlations between age and NF jiggle for each muscle were determined by calculating the Pearson product-moment correlation coefficient (Pearson’s r). Correlations between NF jiggle and other MUP parameters were measured by calculating the Pearson product-moment correlation coefficient (Pearson’s r). For all statistics, significance was P < 0.05.

3. Results Group means ± standard deviations for all parameters are presented in Table 1. For TA, S-MUP amplitude, MUP peak-to-peak voltage, and MUP area were significantly higher in the older age group compared to younger controls (P < 0.05). In addition, MUNE was significantly reduced in the older age group by 260% (P < 0.05). For VM, MUP peak-to-peak voltage and MUP area were significantly higher in the older age group compared to younger controls.

3.1. Comparison of motor unit potential shape variability between young and old men NF jiggle was significantly increased for both the TA and VM in the old age group relative to the younger controls (P < 0.05). For the TA, the mean NF jiggle was 26.5% ± 0.3 for the young men compared to 36.0% ± 7.1 for the older men. For the VM, the mean NF jiggle was 23.9% ± 4.2 for the young men compared to 31.3% ± 5.5 for the older men. Traditional jiggle was not significantly different in the old age group relative to young controls for both the TA and VM (P > 0.05).

3.2. Comparison of motor unit potential shape variability between the tibialis anterior and vastus medialis muscles NF jiggle was overall significantly higher in the TA compared to VM (P < 0.05). The correlation between NF jiggle and age was slightly higher for the TA (r = 0.68) compared to VM (r = 0.58), but this difference was not statistically significant.

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002

M.L. Hourigan et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

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Fig. 1. Panels a and b show MUPT analysis results from a younger and an older subject, respectively. The MUP template is shown in the upper left panel. In the middle left panel, the portion of the MUP template corresponding to the NF MUP duration is shown. In the lower left panel, the NF MUP template, scaled to have unit area, is shown. The middle panels show a raster plot of isolated NF MUPs. The right panel shows a shimmer plot of isolated NF MUPs.

3.3. Relationship between motor unit potential stability and other motor unit potential parameters Results regarding correlations between NF jiggle and other MUP parameters are shown in Figs. 2 and 3. For TA (Fig. 2), NF jiggle was negatively correlated with MUNE, and positively correlated with SMUP amplitude, NF count, MUP duration, MUP peak-to-peak voltage, and MUP area (P < 0.05). For VM (Fig. 3), NF jiggle was positively correlated with NF count and MUP area (P < 0.05). No

significant correlations were found between NF jiggle and S-MUP amplitude, MUP duration, or MUP peak-to-peak voltage (MUNE was not calculated for VM, so no correlation could be made).

4. Discussion As hypothesized, in this study, it was shown that: (i) NF jiggle was significantly higher in healthy older men compared to younger

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002

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Table 1 NF jiggle and other MUP parameters. Parameter

CMAP amplitude (mV) S-MUP amplitude (lV) MUNE MUP peak-to-peak voltage (lV) MUP duration (ms) MUP area (lV ms) NF count NF jiggle (%) Traditional jiggle (%)

Tibialis anterior (18)

Vastus medialis (18)

Young (9)

Old (9)

Young (9)

Old (9)

5.1 ± 2.0 36 ± 10 187 ± 69 435 ± 90 12.2 ± 1.3 888 ± 269 2.1 ± 0.3 26.5 ± 0.3 10.9 ± 1.7

4.0 ± 0.6 101 ± 30* 52 ± 21* 605 ± 140* 13.1 ± 2.0 1401 ± 551* 2.2 ± 0.3 36.0 ± 7.1* 10.7 ± 2.3

– 76 ± 27 – 516 ± 90 12.1 ± 1.1 1093 ± 302 2.0 ± 0.52 23.9 ± 4.2 6.42 ± 1.8

– 66 ± 24 – 665 ± 115* 12.6 ± 1.0 1491 ± 214* 2.5 ± 0.8 31.3 ± 5.5* 6.81 ± 1.4

CMAP, compound muscle action potential; S-MUP, surface motor unit potential; MUNE, motor unit number estimation; MUP, motor unit potential; NF, near fiber. * Denotes significance between young and old (P < 0.05).

Fig. 2. Significant relationships between near fiber (NF) jiggle and other motor unit potential (MUP) parameters for the tibialis anterior (TA) muscle (p < 0.05). (A) Positive correlation with MUP duration (r = 0.55); (B) Positive correlation with MUP peak-to-peak voltage (r = 0.86); (C) Positive correlation with MUP area (r = 0.82); (D) Positive correlation with NF count (r = 0.54); (F) Positive correlation with surface motor unit potential (S-MUP) amplitude (r = 0.78); (F) Negative correlation with motor unit number estimation (MUNE) (r = 0.72).

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002

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Fig. 3. Significant relationships between near fiber (NF) jiggle and other motor unit potential (MUP) parameters for the vastus medialis (VM) muscle (p < 0.05). (A) Positive correlation with MUP area (r = 0.47); (B) Positive correlation with NF count (r = 0.56).

controls; (ii) NF jiggle was significantly higher in the TA compared to the VM; (iii) for the TA, NF jiggle values were negatively correlated with MUNE values; (iv) NF jiggle was positively correlated with increased values of MUP parameters indicative of collateral reinnervation including surface MUP (S-MUP) amplitude, as well as MUP duration, peak-to-peak voltage, and area; (v) NF jiggle was positively correlated with NF count, a measure similar to fiber density that reflects the number of muscle fibers close to the selective detection surface of a needle contributing to a recorded MUP, and therefore related to reinnervation; and (vi) traditional jiggle was lower than NF jiggle and exhibited non-significant age-related changes. MUP shape variability is frequently observed during clinical EMG examination; however, this assessment is usually subjective and based largely on visual examination of free running or triggered, time-locked EMG. Using advancements in EMG decomposition and quantitative analysis, this study has expanded on the work of Stalberg and Sonoo (1994). The findings of increased NF jiggle in older men correspond with previous reports of increased jitter with age using single fiber EMG (SFEMG) (Stalberg and Thiele, 1975; Lange, 1992; Bromberg and Scott, 1994; Trontelj and Stalberg, 1995; Stalberg and Trontelj, 1997; Sanders, 2002). However, Sanders (2002) indicated that SFEMG is only used in major medical centers due to the limited clinical utility to justify the need for the equipment and the time needed to master this technique. Additionally, despite the fact that needle electrodes with larger detection surfaces (concentric and monopolar) have been used in recent years to measure jitter (Tutkavul and Baslo, 2010; Stalberg, 2012), reference values are important to establish through multicenter analysis to standardize protocols (Sanders, 2013). Therefore, quantification of NF jiggle may be more clinically efficient, and provide information complementary to that provided by the more traditional measure of jitter. NF jiggle was higher in the older men potentially due to MU loss (260% reduction in MUNE for TA), resulting in denervation and subsequent collateral reinnervation. During the process of collateral reinnervation, contributions from newly reinnervated muscle fibers may not fire as synchronously as those from younger, healthy MUs. This happens for a number of reasons including muscle fiber atrophy leading to decreased muscle fiber action potential conduction velocity in addition to slower action potential conduction in new nerve sprouts or across nascent neuromuscular junctions (Stalberg et al., 1996). This ultimately results in greater variability in the relative timings of muscle fiber contributions to

a detected MUP, and therefore, increased MUP shape variability across consecutive MU discharges (increased jiggle). Over time, these newly reinnervated fibers become assimilated into the MUP and the timings of their contributions to a detected MUP become more stable, which in turn decreases MUP jiggle (Stalberg and Sonoo, 1994; Stalberg et al., 1996). Therefore, quantitative MUP shape variability parameters may provide useful information regarding MU health at various stages of MU loss and collateral reinnervation. Both human and animal studies have demonstrated that the structural morphology of a neuromuscular junction and the functional stability of synaptic transmission are altered as a result of aging (Jang and Van Remmen, 2011; Manini et al., 2013). Increases in quantal release of neurotransmitter, as demonstrated by increased evoked endplate potential amplitudes, has been shown in mice (Kelly and Robbins, 1983). These changes are correlated with studies reporting a decrease in the number of synaptic vesicles, nerve terminal area, acetylcholine receptors, and number of post-synaptic folds with aging (Courtney and Steinbach, 1981; Fahim and Robbins, 1982, 1986; Banker et al., 1983; Fahim et al., 1983; Kelly and Robbins, 1983; Jang and Van Remmen, 2011). While this increase in neurotransmitter release may seem like an effective compensatory strategy for age-related morphological changes, it has been suggested this actually results in an increased rate of neuromuscular transmission failure (Manini et al., 2013). Additionally, it has been shown in rats that aging differentially affects adenosine receptor pre-synaptic modulation of neuromuscular transmission. Specifically, the excitatory influence on neuromuscular transmission seems to decrease with age, while the inhibitory effects remain unchanged (Pousinha et al., 2012). The protocol used in this study is based on an intensity measure, and is designed to be used routinely in a clinical setting. It is the same protocol as used by Allen et al. (2014). The absolute torque of the %MVC level of contraction for the young subjects was most likely lower than for the older subjects. Therefore, relatively larger MUs may have been recruited by the older subjects. The jiggle statistic however, measures the variability of the MUP shape relative to the size of the MUP (i.e., it measures a relative MUP shape variability and is expressed as a fraction of MUP area). Therefore, while the MUPs of the recruited MUs in the older subjects were larger, relative to their size, they were more variable. The relationship between NF jiggle with other MUP parameters was also studied. For the TA, a negative correlation between NF jig-

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002

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M.L. Hourigan et al. / Clinical Neurophysiology xxx (2015) xxx–xxx

gle and MUNE was observed. An association between greater MU loss (lower MUNE) and higher NF jiggle is likely due to the variability in the generation and propagation of individual muscle fiber action potentials in muscle fibers that have undergone collateral reinnervation by surviving MUs. In addition, NF jiggle was positively correlated with S-MUP amplitude, which reflects MU loss and collateral reinnervation resulting in larger surface detected signals from surviving MUs. There was a positive correlation between NF jiggle and NF count, which also reflects the process of collateral reinnervation giving rise to fewer but larger MUs. As previously discussed, NF count is similar to fiber density and describes the number of MU muscle fibers close to the selective detection surface of the needle electrode that are contributing to a specific MUP. A positive correlation between NF jiggle and MUP durations was observed, reflecting the contributions from newly reinnervated muscle fibers contributing later to MUP waveforms. Finally, a positive correlation between NF jiggle and MUP peak-to-peak voltage and MUP area was reported. This again corresponds to MU loss and collateral reinnervation resulting in larger MUPs. For the VM, only significant positive correlations between NF jiggle and MUP area and NF count were observed. Potential reasons for this are discussed below. This study also assessed the effect of muscle type on NF jiggle in relation to age. There has been evidence to suggest that increased axonal path length leads to a decreased ability to maintain functional connections distally with age (Taylor, 1993). This claim has been substantiated by significantly greater age-related MU loss in distal versus proximal upper limb muscles (Galea, 1996). In this study, the increased NF jiggle in the TA versus the VM muscle suggests that neurogenic changes to a muscle with aging may be greater in more distal muscles. However, this needs to be further studied in other muscles and with larger sample sizes. Additionally, a slightly stronger correlation between age and NF jiggle in the TA versus the VM was observed, but this difference was not statistically significant. This could explain why a weaker linear correlation between NF jiggle and other MUP parameters for the VM was observed. Surprisingly, decreased S-MUP amplitudes for the older age group in the VM, which contradicts the mechanism of collateral reinnervation with aging, were observed. One possible reason for this could be the increased amounts of adipose tissue present with age in the medial thigh resulting in decreased surface-detected signals. This also could be explained by S-MUPs being related to the entire MU, compared to needle-detected MUP parameters, which primarily represent the contributions of the MU fibers close to the concentric needle electrode. In a large muscle like the VM, the S-MUP amplitude may not always be a valid indicator of MU size due to the spatial distribution of contributing muscle fibers. For example, Doherty and Stashuk (2003) observed larger S-MUPs for the first dorsal interosseous (159 lV ± 64) compared to larger muscles including biceps brachii (51 lV ± 18), VM (87 lV ± 43), and TA (86 lV ± 35). If it were possible to obtain a valid and reliable CMAP for this muscle, calculating a MUNE would have normalized the S-MUP to the size of the muscle itself. This theory is supported by the expected larger areas and peak-to-peak amplitudes, as well as longer durations obtained from needle-detected signals in the older group. In addition, this study has demonstrated the clinical utility of NF jiggle compared to traditional jiggle. As previously discussed, the contributions of distant fibers to NF MUPs are greatly reduced. Therefore, NF MUPs are less likely to be contaminated by the activity of other MUs, and stability can be more easily measured. In this study, traditional jiggle values were consistently smaller than NF jiggle values, and no significant age-related changes in traditional jiggle were observed. Consequentially, we conclude that NF jiggle, as measured with this set of algorithms, may have a greater ability

to elucidate the changes in MUP shape variability compared to traditional jiggle. Therefore, NF jiggle may be a useful predictor of neuromuscular transmission abnormality in the earliest stages of disease prior to significant MU loss. For example, fasciculation potentials in addition to increased qualitative MUP shape variability and jitter in the earliest stages of ALS have been observed (de Carvalho and Swash, 2013; de Carvalho et al., 2014). There were a few potential limitations of this study. First, the small sample size of the current study warrants further research to increase the power and generalizability of the findings. In addition, it is known that MU loss is not a linear process with respect to aging. It has been shown that MU loss is gradual until approximately 60 years of age, and then accelerates significantly (Brown, 1973; Campbell et al., 1973; Sica et al., 1974; Brown et al., 1988; Gordon et al., 2004). Since the age of our older adult group was 77 ± 5 years, this warrants the need for additional studies to assess MUP shape variability in younger age groups (i.e., middle aged) just prior to significant MU loss. We hypothesize that there may be significant MU instability present preceding accelerated MU loss. Another important limitation is the invasiveness of the needle EMG data collection protocol itself; however, the concentric needle only resulted in minimal subject discomfort, and did not lead to discontinuation of the study by any participant. Despite the fact that the EMG data collection protocol required participant cooperation and effort to maintain steady voluntary contractions, each participant successfully performed the protocol. 5. Conclusion It has been demonstrated that NF jiggle can be a valuable measure reflective of age-related changes at the MU level. This study showed that healthy older males had increased NF jiggle values compared to young controls, and that NF jiggle was correlated with other important MUP parameters. Additionally, it was found that NF jiggle was significantly higher and exhibited a slightly stronger positive correlation with age in the distal (TA) lower limb muscle compared to the proximal muscle (VM). This evidence suggests that NF jiggle may provide useful information regarding the stage of MU loss and collateral reinnervation, and may be particularly helpful in revealing age-related changes in the health of a MU pool prior to significant MU loss. Future studies should also examine NF jiggle in patients with motor neuron disease, peripheral neuropathies, or other neuromuscular disturbances. Conflict of interest The authors have no conflicts of interest to disclose. References Allen MD, Choi IH, Kimpinski K, Doherty TJ, Rice CL. Motor unit loss and weakness in association with diabetic neuropathy in humans. Muscle Nerve 2013;48:298–300. Allen MD, Stashuk DW, Kimpinski K, Doherty TJ, Hourigan ML, Rice CL. Increased neuromuscular transmission instability and motor unit remodelling with diabetic neuropathy as assessed using novel near fibre motor unit potential parameters. Clin Neurophysiol 2014. http://dx.doi.org/10.1016/ j.clinph.2014.07.018. Andersen JL, Terzis G, Kryger A. Increase in the degree of coexpression of myosin heavy chain isoforms in skeletal muscle fibers of the very old. Muscle Nerve 1999;22:449–54. Banker BQ, Kelly SS, Robbins N. Neuromuscular transmission and correlative morphology in young and old mice. J Physiol 1983;339:355–77. Baumgartner RN, Koehler KM, Gallagher D, Romero L, Heymsfield SB, Ross RR, et al. Epidemiology of sarcopenia among the elderly in New Mexico. Am J Epidemiol 1998;147:755–63. Benatar M, Hammad M, Doss-Riney H. Concentric-needle single-fiber electromyography for the diagnosis of myasthenia gravis. Muscle Nerve 2006;34:163–8.

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002

M.L. Hourigan et al. / Clinical Neurophysiology xxx (2015) xxx–xxx Berger MJ, Chess DG, Doherty TJ. Vastus medialis motor unit properties in knee osteoarthritis. BMC Musculoskelet Disord 2011;12:199. Boe SG, Antonowicz NM, Leung VW, Shea SM, Zimmerman TC, Doherty TJ. High inter-rater reliability in analyzing results of decomposition-based quantitative electromyography in subjects with or without neuromuscular disorder. J Neurosci Methods 2010;192:138–45. Boe SG, Dalton BH, Harwood B, Doherty TJ, Rice CL. Inter-rater reliability of motor unit number estimates and quantitative motor unit analysis in the tibialis anterior muscle. Clin Neurophysiol 2009;120:947–52. Boe SG, Stashuk DW, Brown WF, Doherty TJ. Decomposition-based quantitative electromyography: effect of force on motor unit potentials and motor unit number estimates. Muscle Nerve 2005;31:365–73. Boe SG, Stashuk DW, Doherty TJ. Motor unit number estimation by decompositionenhanced spike-triggered averaging: control data, test-retest reliability, and contractile level effects. Muscle Nerve 2004;29:693–9. Boe SG, Stashuk DW, Doherty TJ. Within-subject reliability of motor unit number estimates and quantitative motor unit analysis in a distal and proximal upper limb muscle. Clin Neurophysiol 2006;117:596–603. Bromberg MB, Scott DM. Single fiber EMG reference values: reformatted in tabular form. AD HOC Committee of the AAEM Single Fiber Special Interest Group. Muscle Nerve 1994;17:820–1. Brown WF. A method for estimating the number of motor units in thenar muscles and the changes in motor unit count with ageing. J Neurol Neurosurg Psychiatry 1972;35:845–52. Brown WF. Functional compensation of human motor units in health and disease. J Neurol Sci 1973;20:199–209. Brown WF, Strong MJ, Snow R. Methods for estimating numbers of motor units in biceps-brachialis muscles and losses of motor units with aging. Muscle Nerve 1988;11:423–32. Calder KM, Agnew MJ, Stashuk DW, McLean L. Reliability of quantitative EMG analysis of the extensor carpi radialis muscle. J Neurosci Methods 2008;168:483–93. Campbell MJ, McComas AJ, Petito F. Physiological changes in ageing muscles. J Neurol Neurosurg Psychiatry 1973;36:174–82. Campos C, Malanda A, Gila L, Segura V, Lasanta I, Artieda J. Quantification of jiggle in real electromyographic signals. Muscle Nerve 2000;23:1022–34. Courtney J, Steinbach JH. Age changes in neuromuscular junction morphology and acetylcholine receptor distribution on rat skeletal muscle fibres. J Physiol 1981;320:435–47. de Carvalho M, Swash M. Fasciculation potentials and earliest changes in motor unit physiology in ALS. J Neurol Neurosurg Psychiatry 2013;84:963–8. de Carvalho M, Turkman A, Swash M. Sensitivity of MUP parameters in detecting change in early ALS. Clin Neurophysiol 2014;125:166–9. Deschenes MR. Motor unit and neuromuscular junction remodeling with aging. Curr Aging Sci 2011;4:209–20. Doherty TJ. Invited review: aging and sarcopenia. J Appl Physiol 2003;95:1717–27. Doherty TJ, Brown WF. The estimated numbers and relative sizes of thenar motor units as selected by multiple point stimulation in young and older adults. Muscle Nerve 1993;16:355–66. Doherty TJ, Brown WF. Age-related changes in the twitch contractile properties of human thenar motor units. J Appl Physiol (1985) 1997;82:93–101. Doherty TJ, Stashuk DW. Decomposition-based quantitative electromyography: methods and initial normative data in five muscles. Muscle Nerve 2003;28:204–11. Doherty TJ, Vandervoort AA, Taylor AW, Brown WF. Effects of motor unit losses on strength in older men and women. J Appl Physiol (1985) 1993;74:868–74. Essen-Gustavsson B, Borges O. Histochemical and metabolic characteristics of human skeletal muscle in relation to age. Acta Physiol Scand 1986;126:107–14. Fahim MA, Holley JA, Robbins N. Scanning and light microscopic study of age changes at a neuromuscular junction in the mouse. J Neurocytol 1983;12:13–25. Fahim MA, Robbins N. Ultrastructural studies of young and old mouse neuromuscular junctions. J Neurocytol 1982;11:641–56. Fahim MA, Robbins N. Remodelling of the neuromuscular junction after subtotal disuse. Brain Res 1986;383:353–6. Galea V. Changes in motor unit estimates with aging. J Clin Neurophysiol 1996;13:253–60. Gordon T, Hegedus J, Tam SL. Adaptive and maladaptive motor axonal sprouting in aging and motoneuron disease. Neurol Res 2004;26:174–85. Ives CT, Doherty TJ. Intra- and inter-rater reliability of motor unit number estimation and quantitative motor unit analysis in the upper trapezius. Clin Neurophysiol 2012;123:200–5. Ives CT, Doherty TJ. Intra-rater reliability of motor unit number estimation and quantitative motor unit analysis in subjects with amyotrophic lateral sclerosis. Clin Neurophysiol 2014;125:170–8. Jang YC, Van Remmen H. Age-associated alterations of the neuromuscular junction. Exp Gerontol 2011;46:193–8. Janssen I, Heymsfield SB, Ross R. Low relative skeletal muscle mass (sarcopenia) in older persons is associated with functional impairment and physical disability. J Am Geriatr Soc 2002;50:889–96. Kawamura Y, O’Brien P, Okazaki H, Dyck PJ. Lumbar motoneurons of man II: the number and diameter distribution of large- and intermediate-diameter cytons

9

in ‘‘motoneuron columns’’ of spinal cord of man. J Neuropathol Exp Neurol 1977a;36:861–70. Kawamura Y, Okazaki H, O’Brien PC, Dych PJ. Lumbar motoneurons of man: I) number and diameter histogram of alpha and gamma axons of ventral root. J Neuropathol Exp Neurol 1977b;36:853–60. Kelly SS, Robbins N. Progression of age changes in synaptic transmission at mouse neuromuscular junctions. J Physiol 1983;343:375–83. Kovacic U, Sketelj J, Bajrovic FF. Chapter 26: age-related differences in the reinnervation after peripheral nerve injury. Int Rev Neurobiol 2009;87:465–82. Krutki P, Ciechanowicz-Kowalczyk I, Lochynski D, Celichowski J. Effect of aging on properties of motor unit action potentials in the rat medial gastrocnemius muscle. J Electromyogr Kinesiol 2013;23:1150–7. Lange DJ. Single fiber electromyography in normal subjects: reproducibility, variability, and technical considerations. Electromyogr Clin Neurophysiol 1992;32:397–402. Larsson L, Ansved T. Effects of ageing on the motor unit. Prog Neurobiol 1995;45:397–458. Li Y, Lee Y, Thompson WJ. Changes in aging mouse neuromuscular junctions are explained by degeneration and regeneration of muscle fiber segments at the synapse. J Neurosci 2011;31:14910–9. Manini TM, Hong SL, Clark BC. Aging and muscle: a neuron’s perspective. Curr Opin Clin Nutr Metab Care 2013;16:21–6. McNeil CJ, Doherty TJ, Stashuk DW, Rice CL. The effect of contraction intensity on motor unit number estimates of the tibialis anterior. Clin Neurophysiol 2005a;116:1342–7. McNeil CJ, Doherty TJ, Stashuk DW, Rice CL. Motor unit number estimates in the tibialis anterior muscle of young, old, and very old men. Muscle Nerve 2005b;31:461–7. Misgeld T. Lost in elimination: mechanisms of axon loss. e-Neuroforum 2011;2:21–34. Mittal KR, Logmani FH. Age-related reduction in 8th cervical ventral nerve root myelinated fiber diameters and numbers in man. J Gerontol 1987;42:8–10. Murga Oporto L, Menendez-de Leon C, Bauzano Poley E, Nunez-Castain MJ. [Statistical (Poisson) motor unit number estimation. Methodological aspects and normal results in the extensor digitorum brevis muscle of healthy subjects]. Rev Neurol 2003;36:601–4. Oertel G. Changes in human skeletal muscles due to ageing. Histological and histochemical observations on autopsy material. Acta Neuropathol 1986;69:309–13. Opalach K, Rangaraju S, Madorsky I, Leeuwenburgh C, Notterpek L. Lifelong calorie restriction alleviates age-related oxidative damage in peripheral nerves. Rejuvenation Res 2010;13:65–74. Pousinha PA, Correia AM, Sebastiao AM, Ribeiro JA. Neuromuscular transmission modulation by adenosine upon aging. Neurobiol Aging 2012;33:2869–80. Roos MR, Rice CL, Vandervoort AA. Age-related changes in motor unit function. Muscle Nerve 1997;20:679–90. Roubenoff R. Origins and clinical relevance of sarcopenia. Can J Appl Physiol 2001;26:78–89. Sanders DB. Clinical impact of single-fiber electromyography. Muscle Nerve Suppl 2002;11:S15–20. Sanders DB. Measuring jitter with concentric needle electrodes. Muscle Nerve 2013;47:317–8. Sica RE, McComas AJ, Upton AR, Longmire D. Motor unit estimations in small muscles of the hand. J Neurol Neurosurg Psychiatry 1974;37:55–67. Stalberg E. Jitter analysis with concentric needle electrodes. Ann N Y Acad Sci 2012;1274:77–85. Stalberg E, Nandedkar SD, Sanders DB, Falck B. Quantitative motor unit potential analysis. J Clin Neurophysiol 1996;13:401–22. Stalberg E, Sonoo M. Assessment of variability in the shape of the motor unit action potential, the ‘‘jiggle’’, at consecutive discharges. Muscle Nerve 1994;17:1135–44. Stalberg E, Thiele B. Motor unit fibre density in the extensor digitorum communis muscle. Single fibre electromyographic study in normal subjects at different ages. J Neurol Neurosurg Psychiatry 1975;38:874–80. Stalberg E, Trontelj JV. The study of normal and abnormal neuromuscular transmission with single fibre electromyography. J Neurosci Methods 1997;74:145–54. Stashuk DW. Decomposition and quantitative analysis of clinical electromyographic signals. Med Eng Phys 1999a;21:389–404. Stashuk DW. Detecting single fiber contributions to motor unit action potentials. Muscle Nerve 1999b;22:218–29. Taylor PK. CMAP dispersion, amplitude decay, and area decay in a normal population. Muscle Nerve 1993;16:1181–7. Tomlinson BE, Irving D. The numbers of limb motor neurons in the human lumbosacral cord throughout life. J Neurol Sci 1977;34:213–9. Trontelj JV, Stalberg E. Single fiber electromyography in studies of neuromuscular function. Adv Exp Med Biol 1995;384:109–19. Tutkavul K, Baslo MB. Reference voluntary jitter values using disposable monopolar needle electrodes in the extensor digitorum communis muscle. Clin Neurophysiol 2010;121:887–9. Vandervoort AA, McComas AJ. Contractile changes in opposing muscles of the human ankle joint with aging. J Appl Physiol (1985) 1986;61:361–7.

Please cite this article in press as: Hourigan ML et al. Increased motor unit potential shape variability across consecutive motor unit discharges in the tibialis anterior and vastus medialis muscles of healthy older subjects. Clin Neurophysiol (2015), http://dx.doi.org/10.1016/j.clinph.2015.02.002