- Email: [email protected]
Estimating the value of estimation
Clinical Neurophysiology 123 (2012) 1904–1905
Contents lists available at SciVerse ScienceDirect
Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph
Editorial
Estimating the value of estimation See Article, pages 2080–2091
The concept of motor unit was first recognized by Sir Charles Sherrington (Liddell and Sherrington, 1925) by defining it as the spinal motor neuron and its axon together with the muscle fibers it activates. The possibility to investigate the number of motor units in a muscle is of major interest for a number of reasons: it gives information about the number and function of lower motor neurons; it permits to understand the physiology and plasticity (reinnervation potential) of the peripheral axon and neuromuscular junction (Bromberg, 2007); it is a tool for following the number of lower motor neurons in degenerative disorders, like in amyotrophic lateral sclerosis (ALS), as well as to test the potential benefit of therapeutic interventions (de Carvalho et al., 2005). Over the years a number of techniques have been developed to estimate the number of motor units in human and animal model muscles (Bromberg, 2007). Recently, MUNIX and Bayesian motor unit number estimation (MUNE) methods have been explored as the most promising approaches. Typically, the calculated single motor unit potential amplitude (or area) is divided into the compound muscle action potential amplitude (or area) to estimate the number of motor units in the target muscle. Both MUNIX and Bayesian MUNE follow a somewhat different strategy. In MUNIX (just an index without a necessary direct correlation with the number of lower motor neurons) the power of the compound muscle action potential is computed with the power of electrical signal obtained by different degree of voluntary muscle contraction recorded by surface electrodes. The method is grounded on a mathematical model but seems simple and reliable (Neuwirth et al., 2011). Bayesian MUNE incorporates the variability of the threshold, the variability between and within single motor unit action potentials, and baseline variability in the model (Ridall et al., 2006). This method is reliable and shows motor unit decrease in ALS over disease progression (Henderson et al., 2007). For all the devised techniques the correlation between the MUNE value and the real number of lower motor neurons has not been established. In order to address this problem, Ngo and collaborators (in this issue of Clinical Neurophysiology) compared the numbers of motor units estimated by MUNE with the numbers of motor neurons in the spinal cords of the same wild-type mice and SOD1G93A mice (Ngo et al., 2012). Groups of these mice were studied before the onset of overt signs of motor dysfunction, at the onset of overt symptoms, and at the terminal stages of disease. The results
were compared with the ones obtained by investigating wild-type mice. Their MUNE studies found a significant decrease in motor unit number in SOD1G93A mice at all age points in comparison to wild-type animals: affected mice had a progressive decrease in motor unit numbers. Histological assessment of motor neurons in SOD1G93A animals was significantly less than those in wild-type age-matched controls at all stages of disease, with motor neuron numbers declining as disease progressed. Nonetheless, even presymptomatic SOD1G93A animals had significantly lower numbers of motor neurons than wild-type age-matched controls. The average MUNE to histology ratio was about 0.6 in wild-type mice. The ratio MUNE to histology was similar in pre-symptomatic animals, but it decreased significantly over disease progression. The authors suggested that this might represent non-functional motor neurons in the spinal cord, as confirmed by the lower percentage of innervated endplates in end-stage SOD1G93A animals (Ngo et al., 2012). It is possible to acknowledge a number of limitations in this study: the potential inaccuracy in defining the right number of lower motor neuron pool in the spinal cord, as the histological studies could not differentiate accurately between alpha motor neurons and other large neurons, as the gamma motor neurons; the impossibility to define the lower motor neurons innervating the specific muscle in which MUNE was calculated; and the use of needle electrodes and very high low-pass filter to record motor responses, which probably precluded the recording of all motor units from investigated muscles. Taking this into account, it is not surprising that the authors found a discrepancy between the histological counts and the physiological counts from the MUNE technique. However, this report gives very relevant information about the Bayesian MUNE and ALS pathophysiology. They showed a solid correlation between MUNE as evaluated by this method and the lower motor neuron pool, supporting MUNE as a recommendable endpoint in clinical trials. In addition, we have more data to support that the neuro-muscular junction is severely affected in ALS, more critically in end-stages of muscle involvement. This implies that repairing motor neurons and axons may not be enough to keep function, in ALS. Moreover, in the future, a revision of the Awaji criteria should upgrade neuro-muscular instability as a key marker of the electrophysiological changes in ALS, in addition to fasciculation potentials (de Carvalho et al., 2008).
1388-2457/$36.00 Ó 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinph.2012.03.003
Editorial / Clinical Neurophysiology 123 (2012) 1904–1905
Acknowledgment Our present scientific work is supported by ‘‘Fundação para a Ciência e Tecnologia’’ – PIC/IC/82765/2007. References Bromberg MB. Updating motor unit number estimation (MUNE). Clin Neurophysiol 2007;118:1–18. de Carvalho M, Chio A, Dengler R, Hecht M, Weber M, Swash M. Neurophysiologic measures in amyotrophic lateral sclerosis: markers of progression in clinical trials. Amyotroph Lateral Scler 2005;6:17–28. de Carvalho M, Dengler R, Eisen A, England JD, Kaji R, Kimura R, et al. Electrodiagnostic criteria for diagnosis of ALS: consensus of an international symposium sponsored by IFCN. Clin Neurophysiol 2008;119:497–503. Henderson RD, Ridall PG, Hutchinson NM, Pettitt AN, McCombe PA. Bayesian statistical MUNE method. Muscle Nerve 2007;36:206–13. Liddell EGT, Sherrington CS. Recruitment and some other features of inhibition. Proc R Soc Lond Ser B 1925;97:488–518. Neuwirth C, Nandedkar S, Stålberg E, Barkhaus PE, de Carvalho M, Furtula J, et al. Motor unit number index (MUNIX): a novel neurophysiological marker for neuromuscular disorders test-retest reliability in healthy volunteers. Clin Neurophysiol 2011;122:1867–72. Ngo ST, Baumann F, Ridall PG, Pettitt AN, Henderson RD, Bellingham MC, et al. The relationship between Bayesian motor unit number estimation and histological measurements of motor neurons in wild-type and SOD1G93A mice. Clin Neurophysiol 2012;123:2080–91.
1905
Ridall PG, Pettitt AN, Henderson RD, McCombe PA. Motor unit number estimation – a Bayesian approach. Biometrics 2006;62:1235–50.
⇑
Mamede de Carvalho Clinical and Translational Physiology Unit, Physiology Institute, Lisbon, Portugal Faculty of Medicine, Instituto de Medicina Molecular, Lisbon, Portugal Department of Neurosciences, Santa Maria Hospital, Lisbon, Portugal * Address: Department of Neurosciences, Santa Maria Hospital, Lisbon 1649-028, Portugal. Tel.: +351 21 7805219; fax: +351 21 7520801. E-mail address: [email protected] Available online 18 April 2012
Contents lists available at SciVerse ScienceDirect
Clinical Neurophysiology journal homepage: www.elsevier.com/locate/clinph
Editorial
Estimating the value of estimation See Article, pages 2080–2091
The concept of motor unit was first recognized by Sir Charles Sherrington (Liddell and Sherrington, 1925) by defining it as the spinal motor neuron and its axon together with the muscle fibers it activates. The possibility to investigate the number of motor units in a muscle is of major interest for a number of reasons: it gives information about the number and function of lower motor neurons; it permits to understand the physiology and plasticity (reinnervation potential) of the peripheral axon and neuromuscular junction (Bromberg, 2007); it is a tool for following the number of lower motor neurons in degenerative disorders, like in amyotrophic lateral sclerosis (ALS), as well as to test the potential benefit of therapeutic interventions (de Carvalho et al., 2005). Over the years a number of techniques have been developed to estimate the number of motor units in human and animal model muscles (Bromberg, 2007). Recently, MUNIX and Bayesian motor unit number estimation (MUNE) methods have been explored as the most promising approaches. Typically, the calculated single motor unit potential amplitude (or area) is divided into the compound muscle action potential amplitude (or area) to estimate the number of motor units in the target muscle. Both MUNIX and Bayesian MUNE follow a somewhat different strategy. In MUNIX (just an index without a necessary direct correlation with the number of lower motor neurons) the power of the compound muscle action potential is computed with the power of electrical signal obtained by different degree of voluntary muscle contraction recorded by surface electrodes. The method is grounded on a mathematical model but seems simple and reliable (Neuwirth et al., 2011). Bayesian MUNE incorporates the variability of the threshold, the variability between and within single motor unit action potentials, and baseline variability in the model (Ridall et al., 2006). This method is reliable and shows motor unit decrease in ALS over disease progression (Henderson et al., 2007). For all the devised techniques the correlation between the MUNE value and the real number of lower motor neurons has not been established. In order to address this problem, Ngo and collaborators (in this issue of Clinical Neurophysiology) compared the numbers of motor units estimated by MUNE with the numbers of motor neurons in the spinal cords of the same wild-type mice and SOD1G93A mice (Ngo et al., 2012). Groups of these mice were studied before the onset of overt signs of motor dysfunction, at the onset of overt symptoms, and at the terminal stages of disease. The results
were compared with the ones obtained by investigating wild-type mice. Their MUNE studies found a significant decrease in motor unit number in SOD1G93A mice at all age points in comparison to wild-type animals: affected mice had a progressive decrease in motor unit numbers. Histological assessment of motor neurons in SOD1G93A animals was significantly less than those in wild-type age-matched controls at all stages of disease, with motor neuron numbers declining as disease progressed. Nonetheless, even presymptomatic SOD1G93A animals had significantly lower numbers of motor neurons than wild-type age-matched controls. The average MUNE to histology ratio was about 0.6 in wild-type mice. The ratio MUNE to histology was similar in pre-symptomatic animals, but it decreased significantly over disease progression. The authors suggested that this might represent non-functional motor neurons in the spinal cord, as confirmed by the lower percentage of innervated endplates in end-stage SOD1G93A animals (Ngo et al., 2012). It is possible to acknowledge a number of limitations in this study: the potential inaccuracy in defining the right number of lower motor neuron pool in the spinal cord, as the histological studies could not differentiate accurately between alpha motor neurons and other large neurons, as the gamma motor neurons; the impossibility to define the lower motor neurons innervating the specific muscle in which MUNE was calculated; and the use of needle electrodes and very high low-pass filter to record motor responses, which probably precluded the recording of all motor units from investigated muscles. Taking this into account, it is not surprising that the authors found a discrepancy between the histological counts and the physiological counts from the MUNE technique. However, this report gives very relevant information about the Bayesian MUNE and ALS pathophysiology. They showed a solid correlation between MUNE as evaluated by this method and the lower motor neuron pool, supporting MUNE as a recommendable endpoint in clinical trials. In addition, we have more data to support that the neuro-muscular junction is severely affected in ALS, more critically in end-stages of muscle involvement. This implies that repairing motor neurons and axons may not be enough to keep function, in ALS. Moreover, in the future, a revision of the Awaji criteria should upgrade neuro-muscular instability as a key marker of the electrophysiological changes in ALS, in addition to fasciculation potentials (de Carvalho et al., 2008).
1388-2457/$36.00 Ó 2012 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. http://dx.doi.org/10.1016/j.clinph.2012.03.003
Editorial / Clinical Neurophysiology 123 (2012) 1904–1905
Acknowledgment Our present scientific work is supported by ‘‘Fundação para a Ciência e Tecnologia’’ – PIC/IC/82765/2007. References Bromberg MB. Updating motor unit number estimation (MUNE). Clin Neurophysiol 2007;118:1–18. de Carvalho M, Chio A, Dengler R, Hecht M, Weber M, Swash M. Neurophysiologic measures in amyotrophic lateral sclerosis: markers of progression in clinical trials. Amyotroph Lateral Scler 2005;6:17–28. de Carvalho M, Dengler R, Eisen A, England JD, Kaji R, Kimura R, et al. Electrodiagnostic criteria for diagnosis of ALS: consensus of an international symposium sponsored by IFCN. Clin Neurophysiol 2008;119:497–503. Henderson RD, Ridall PG, Hutchinson NM, Pettitt AN, McCombe PA. Bayesian statistical MUNE method. Muscle Nerve 2007;36:206–13. Liddell EGT, Sherrington CS. Recruitment and some other features of inhibition. Proc R Soc Lond Ser B 1925;97:488–518. Neuwirth C, Nandedkar S, Stålberg E, Barkhaus PE, de Carvalho M, Furtula J, et al. Motor unit number index (MUNIX): a novel neurophysiological marker for neuromuscular disorders test-retest reliability in healthy volunteers. Clin Neurophysiol 2011;122:1867–72. Ngo ST, Baumann F, Ridall PG, Pettitt AN, Henderson RD, Bellingham MC, et al. The relationship between Bayesian motor unit number estimation and histological measurements of motor neurons in wild-type and SOD1G93A mice. Clin Neurophysiol 2012;123:2080–91.
1905
Ridall PG, Pettitt AN, Henderson RD, McCombe PA. Motor unit number estimation – a Bayesian approach. Biometrics 2006;62:1235–50.
⇑
Mamede de Carvalho Clinical and Translational Physiology Unit, Physiology Institute, Lisbon, Portugal Faculty of Medicine, Instituto de Medicina Molecular, Lisbon, Portugal Department of Neurosciences, Santa Maria Hospital, Lisbon, Portugal * Address: Department of Neurosciences, Santa Maria Hospital, Lisbon 1649-028, Portugal. Tel.: +351 21 7805219; fax: +351 21 7520801. E-mail address: [email protected] Available online 18 April 2012