Tracking motor units longitudinally across experimental sessions with high-density surface electromyography
E Martinez-Valdes, F Negro, C M Laine, D Falla, F Mayer, D Farina, E Martinez-Valdes, F Negro, C M Laine, D Falla, F Mayer, D Farina
Abstract
Key points: Classic motor unit (MU) recording and analysis methods do not allow the same MUs to be tracked across different experimental sessions, and therefore, there is limited experimental evidence on the adjustments in MU properties following training or during the progression of neuromuscular disorders. We propose a new processing method to track the same MUs across experimental sessions (separated by weeks) by using high-density surface electromyography. The application of the proposed method in two experiments showed that individual MUs can be identified reliably in measurements separated by weeks and that changes in properties of the tracked MUs across experimental sessions can be identified with high sensitivity. These results indicate that the behaviour and properties of the same MUs can be monitored across multiple testing sessions. The proposed method opens new possibilities in the understanding of adjustments in motor unit properties due to training interventions or the progression of pathologies.
Abstract: A new method is proposed for tracking individual motor units (MUs) across multiple experimental sessions on different days. The technique is based on a novel decomposition approach for high-density surface electromyography and was tested with two experimental studies for reliability and sensitivity. Experiment I (reliability): ten participants performed isometric knee extensions at 10, 30, 50 and 70% of their maximum voluntary contraction (MVC) force in three sessions, each separated by 1 week. Experiment II (sensitivity): seven participants performed 2 weeks of endurance training (cycling) and were tested pre-post intervention during isometric knee extensions at 10 and 30% MVC. The reliability (Experiment I) and sensitivity (Experiment II) of the measured MU properties were compared for the MUs tracked across sessions, with respect to all MUs identified in each session. In Experiment I, on average 38.3% and 40.1% of the identified MUs could be tracked across two sessions (1 and 2 weeks apart), for the vastus medialis and vastus lateralis, respectively. Moreover, the properties of the tracked MUs were more reliable across sessions than those of the full set of identified MUs (intra-class correlation coefficients ranged between 0.63-0.99 and 0.39-0.95, respectively). In Experiment II, ∼40% of the MUs could be tracked before and after the training intervention and training-induced changes in MU conduction velocity had an effect size of 2.1 (tracked MUs) and 1.5 (group of all identified motor units). These results show the possibility of monitoring MU properties longitudinally to document the effect of interventions or the progression of neuromuscular disorders.
© 2016 The Authors. The Journal of Physiology published by John Wiley & Sons Ltd on behalf of The Physiological Society.
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References
- Aagaard P (2003). Training‐induced changes in neural function. Exerc Sport Sci Rev 31, 61–67.
- Aagaard P, Andersen JL, Dyhre‐Poulsen P, Leffers AM, Wagner A, Magnusson SP, Halkjaer‐Kristensen J & Simonsen EB (2001). A mechanism for increased contractile strength of human pennate muscle in response to strength training: changes in muscle architecture. J Physiol 534, 613–623.
- Adam A & De Luca CJ (2005). Firing rates of motor units in human vastus lateralis muscle during fatiguing isometric contractions. J Appl Physiol (1985) 99, 268–280.
- Adkins DL, Boychuk J, Remple MS & Kleim JA (2006). Motor training induces experience‐specific patterns of plasticity across motor cortex and spinal cord. J Appl Physiol (1985) 101, 1776–1782.
- Andreassen S & Arendt‐Nielsen L (1987). Muscle fibre conduction velocity in motor units of the human anterior tibial muscle: a new size principle parameter. J Physiol 391, 561–571.
- Barbero M, Merletti R & Rainoldi A (2012). Atlas of Muscle Innervation Zones: Understanding Surface Electromyography and its Applications. Springer, Milan, New York.
- Bartko JJ (1966). The intraclass correlation coefficient as a measure of reliability. Psychol Rep 19, 3–11.
- Blok JH, van Dijk JP, Drost G, Zwarts MJ & Stegeman DF (2002). A high‐density multichannel surface electromyography system for the characterization of single motor units. Rev Sci Instrum 73, 1887–1897.
- Button KS, Ioannidis JP, Mokrysz C, Nosek BA, Flint J, Robinson ES & Munafo MR (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nat Rev Neurosci 14, 365–376.
- Carroll TJ, Selvanayagam VS, Riek S & Semmler JG (2011). Neural adaptations to strength training: moving beyond transcranial magnetic stimulation and reflex studies. Acta Physiol 202, 119–140.
- Castronovo AM, Negro F, Conforto S & Farina D (2015). The proportion of common synaptic input to motor neurons increases with an increase in net excitatory input. J Appl Physiol (1985) 119, 1337–1346.
- Cescon C & Gazzoni M (2010). Short term bed‐rest reduces conduction velocity of individual motor units in leg muscles. J Electromyogr Kinesiol 20, 860–867.
- Cohen J (1988). Statistical Power Analysis for the Behavioral Sciences. L. Erlbaum Associates, Hillsdale, NJ, USA.
- Dideriksen JL, Gallego JA, Holobar A, Rocon E, Pons JL & Farina D (2015). One central oscillatory drive is compatible with experimental motor unit behaviour in essential and Parkinsonian tremor. J Neural Eng 12, 046019.
- Doherty TJ & Brown WF (1994). A method for the longitudinal study of human thenar motor units. Muscle Nerve 17, 1029–1036.
- Duchateau J, Semmler JG & Enoka RM (2006). Training adaptations in the behavior of human motor units. J Appl Physiol (1985) 101, 1766–1775.
- Enoka RM (1995). Morphological features and activation patterns of motor units. J Clin Neurophysiol 12, 538–559.
- Farina D, Holobar A, Gazzoni M, Zazula D, Merletti R & Enoka RM (2009). Adjustments differ among low‐threshold motor units during intermittent, isometric contractions. J Neurophysiol 101, 350–359.
- Farina D, Merletti R & Enoka RM (2004). The extraction of neural strategies from the surface EMG. J Appl Physiol (1985) 96, 1486–1495.
- Farina D & Mesin L (2005). Sensitivity of surface EMG‐based conduction velocity estimates to local tissue in‐homogeneities –influence of the number of channels and inter‐channel distance. J Neurosci Methods 142, 83–89.
- Farina D, Muhammad W, Fortunato E, Meste O, Merletti R & Rix H (2001). Estimation of single motor unit conduction velocity from surface electromyogram signals detected with linear electrode arrays. Med Biol Eng Comput 39, 225–236.
- Farina D, Negro F, Gazzoni M & Enoka RM (2008). Detecting the unique representation of motor‐unit action potentials in the surface electromyogram. J Neurophysiol 100, 1223–1233.
- Farina D, Negro F, Muceli S & Enoka RM (2016). Principles of motor unit physiology evolve with advances in technology. Physiology 31, 83–94.
- Folland JP & Williams AG (2007). The adaptations to strength training: morphological and neurological contributions to increased strength. Sports Med 37, 145–168.
- Gibala MJ, Little JP, van Essen M, Wilkin GP, Burgomaster KA, Safdar A, Raha S & Tarnopolsky MA (2006). Short‐term sprint interval versus traditional endurance training: similar initial adaptations in human skeletal muscle and exercise performance. J Physiol 575, 901–911.
- Gooch CL & Harati Y (1997). Longitudinal tracking of the same single motor unit in amyotrophic lateral sclerosis. Muscle Nerve 20, 511–513.
- Green HJ, Barr DJ, Fowles JR, Sandiford SD & Ouyang J (2004). Malleability of human skeletal muscle Na+‐K+‐ATPase pump with short‐term training. J Appl Physiol (1985) 97, 143–148.
- Heroux ME & Gandevia SC (2013). Human muscle fatigue, eccentric damage and coherence in the EMG. Acta Physiol 208, 294–295.
- Holobar A, Glaser V, Gallego JA, Dideriksen JL & Farina D (2012). Non‐invasive characterization of motor unit behaviour in pathological tremor. J Neural Eng 9, 056011.
- Holobar A, Minetto MA & Farina D (2014). Accurate identification of motor unit discharge patterns from high‐density surface EMG and validation with a novel signal‐based performance metric. J Neural Eng 11, 016008.
- Holobar A & Zazula D (2007). Multichannel blind source separation using convolution kernel compensation. IEEE Trans Signal Process 55, 4487–4496.
- Hyvarinen A & Oja E (2000). Independent component analysis: algorithms and applications. Neural Netw 13, 411–430.
- Kamen G & Knight CA (2004). Training‐related adaptations in motor unit discharge rate in young and older adults. J Gerontol A Biol Sci Med Sci 59, 1334–1338.
- Laine CM, Martinez‐Valdes E, Falla D, Mayer F & Farina D (2015). Motor neuron pools of synergistic thigh muscles share most of their synaptic input. J Neurosci 35, 12207–12216.
- Li X, Holobar A, Gazzoni M, Merletti R, Rymer WZ & Zhou P (2015). Examination of poststroke alteration in motor unit firing behavior using high‐density surface EMG decomposition. IEEE Trans Biomed Eng 62, 1242–1252.
- McCarthy JP, Pozniak MA & Agre JC (2002). Neuromuscular adaptations to concurrent strength and endurance training. Med Sci Sports Exerc 34, 511–519.
- Martinez‐Valdes E, Laine CM, Falla D, Mayer F & Farina D (2016). High‐density surface electromyography provides reliable estimates of motor unit behavior. Clin Neurophysiol 127, 2534–2541.
- Masuda T, Miyano H & Sadoyama T (1985). The position of innervation zones in the biceps brachii investigated by surface electromyography. IEEE Trans Biomed Eng 32, 36–42.
- Muceli S, Poppendieck W, Negro F, Yoshida K, Hoffmann KP, Butler JE, Gandevia SC & Farina D (2015). Accurate and representative decoding of the neural drive to muscles in humans with multi‐channel intramuscular thin‐film electrodes. J Physiol 593, 3789–3804.
- Narici MV, Hoppeler H, Kayser B, Landoni L, Claassen H, Gavardi C, Conti M & Cerretelli P (1996). Human quadriceps cross‐sectional area, torque and neural activation during 6 months strength training. Acta Physiol Scand 157, 175–186.
- Natora M & Obermayer K (2011). An unsupervised and drift‐adaptive spike detection algorithm based on hybrid blind beamforming. Eurasip J Adv Sig Pr 2011.
- Negro F, Muceli S, Castronovo AM, Holobar A & Farina D (2016). Multi‐channel intramuscular and surface EMG decomposition by convolutive blind source separation. J Neural Eng 13, 026027.
- Piitulainen H, Holobar A & Avela J (2012). Changes in motor unit characteristics after eccentric elbow flexor exercise. Scand J Med Sci Sports 22, 418–429.
- Pucci AR, Griffin L & Cafarelli E (2006). Maximal motor unit firing rates during isometric resistance training in men. Exp Physiol 91, 171–178.
- Rich C & Cafarelli E (2000). Submaximal motor unit firing rates after 8 wk of isometric resistance training. Med Sci Sports Exerc 32, 190–196.
- Selvanayagam VS, Riek S & Carroll TJ (2011). Early neural responses to strength training. J Appl Physiol (1985) 111, 367–375.
- Tenan MS, Marti CN & Griffin L (2014). Motor unit discharge rate is correlated within individuals: a case for multilevel model statistical analysis. J Electromyogr Kinesiol 24, 917–922.
- van Dijk JP, Schelhaas HJ, Van Schaik IN, Janssen HM, Stegeman DF & Zwarts MJ (2010). Monitoring disease progression using high‐density motor unit number estimation in amyotrophic lateral sclerosis. Muscle Nerve 42, 239–244.
- Vila‐Cha C, Falla D, Correia MV & Farina D (2012). Adjustments in motor unit properties during fatiguing contractions after training. Med Sci Sports Exerc 44, 616–624.
- Vila‐Cha C, Falla D & Farina D (2010). Motor unit behavior during submaximal contractions following six weeks of either endurance or strength training. J Appl Physiol (1985) 109, 1455–1466.
- Watanabe K, Gazzoni M, Holobar A, Miyamoto T, Fukuda K, Merletti R & Moritani T (2013). Motor unit firing pattern of vastus lateralis muscle in type 2 diabetes mellitus patients. Muscle Nerve 48, 806–813.
- Weibull A, Flondell M, Rosen B & Bjorkman A (2011). Cerebral and clinical effects of short‐term hand immobilisation. Eur J Neurosci 33, 699–704.
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