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While motor neuron activity has been studied for many decades, the relative contribution of synaptic and non-synaptic mechanisms underlying this activity during natural behaviors is not well understood. Thus, the goal of this dissertation was to further understand the role of non-synaptic properties of motor neurons during voluntary activity. In particular, I considered three non-synaptic properties: persistent inward currents (PICs) that boost synaptic inputs, spike-threshold accommodation that affects recruitment threshold as excitation rates of rise slow, and spike-frequency adaptation that leads to a decrease in firing rate despite constant excitation levels. Computer simulations were employed to understand the potential effect that these properties could have on firing rate behavior. In particular, the focus was on paired motor unit recordings where a lower threshold motor unit’s firing rate served as a proxy for synaptic drive, and differences in firing rate (ΔF) were compared at a higher threshold unit’s recruitment and derecruitment. While ΔF has been used by others to estimate PIC activation, the simulation results indicated that each of these non-synaptic mechanisms could lead to positive ΔF. Furthermore, by varying contraction speed and duration it seemed possible to determine which property contributes to ΔF in vivo. The results from human experiments indicated that adaptation is most likely the predominant contributor to ΔF during natural behaviors. Additionally, positive ΔF was even observed in the genioglossus muscle of the tongue, where the role of PICs has been debated. These results suggested that ΔF may not the best method to detect PICs during natural behaviors. As such, I also considered whether there might be another metric to infer PIC activation during natural behaviors. Motor unit firing rates tend to plateau, or saturate, despite continued force increase, and one hypothesis is that PICs contribute to this behavior. Indeed, motor unit firing rate saturation was diminished by the addition of inhibition, which should have limited PIC activation. Therefore, this final study provided possible evidence for PIC activation during natural behaviors. Overall, this dissertation highlights that non-synaptic properties of motor neurons are activated during natural behaviors and that they contribute significantly to firing rate output.

This thesis has evaluated the use of Independent Component Analysis (ICA) on Surface Electromyography (sEMG), focusing on the biosignal applications. This research has identified and addressed the following four issues related to the use of ICA for biosignals: • The iterative nature of ICA • The order and magnitude ambiguity problems of ICA • Estimation of number of sources based on dependency and independency nature of the signals • Source separation for non-quadratic ICA (undercomplete and overcomplete) This research first establishes the applicability of ICA for sEMG and also identifies the shortcomings related to order and magnitude ambiguity. It has then developed, a mitigation strategy for these issues by using a single unmixing matrix and neural network weight matrix corresponding to the specific user. The research reports experimental verification of the technique and also the investigation of the impact of inter-subject and inter-experimental variations. The results demonstrate that while using sEMG without separation gives only 60% accuracy, and sEMG separated using traditional ICA gives an accuracy of 65%, this approach gives an accuracy of 99% for the same experimental data. Besides the marked improvement in accuracy, the other advantages of such a system are that it is suitable for real time operations and is easy to train by a lay user. The second part of this thesis reports research conducted to evaluate the use of ICA for the separation of bioelectric signals when the number of active sources may not be known. The work proposes the use of value of the determinant of the Global matrix generated using sparse sub band ICA for identifying the number of active sources. The results indicate that the technique is successful in identifying the number of active muscles for complex hand gestures. The results support the applications such as human computer interface. This thesis has also developed a method of determining the number of independent sources in a given mixture and has also demonstrated that using this information, it is possible to separate the signals in an undercomplete situation and reduce the redundancy in the data using standard ICA methods. The experimental verification has demonstrated that the quality of separation using this method is better than other techniques such as Principal Component Analysis (PCA) and selective PCA. This has number of applications such as audio separation and sensor networks.

Neuromuscular disorders change the underlying structure and function of motor units within a muscle, and are detected using needle electromyography. Currently, inferences about the presence or absence of disease are made subjectively and are largely impression-based. Quantitative electromyography (QEMG) attempts to improve upon the status quo by providing greater levels of precision, objectivity and reproducibility through numeric analysis, however, their results must be transparently presented and explained to be clinically viable. The probabilistic muscle characterization (PMC) model is ideally suited for a clinical decision support system (CDSS) and has many analogues to the subjective analysis currently used. To improve disease characterization performance globally, a hierarchical classification strategy is developed that accounts for the wide range of MUP feature values present at different levels of involvement (LOI) of a disorder. To improve utility, methods for detecting LOI are considered that balance the accuracy in reporting LOI with its clinical utility. Finally, several aggregation methods that represent commonly used human decision-making strategies are considered and evaluated for their suitability in a CDSS. Four aggregation measures (Average, Bayes, Adjusted Bayes, and WMLO) are evaluated, that offer a compromise between two common decision making paradigms: conservativeness (average) and extremeness (Bayes). Standard classification methods have high specificity at a cost of poor sensitivity at low levels of disease involvement, but tend to improve with disease progression. The hierarchical model is able to provide a better balance between low-LOI sensitivity and specificity by providing the classifier with more concise definitions of abnormality due to LOI. Furthermore, a method for detecting two discrete levels of disease involvement (low and high) is accomplished with reasonable accuracy. The average aggregation method offers a conservative decision that is preferred when the quality of the evidence is poor or not known, while the more extreme aggregators such as Bayes rule perform optimally when the evidence is accurate, but underperform otherwise due to outlier values that are incorrect. The methods developed offer several improvements to PMC, by providing a better balance between sensitivity and specificity, through the definition of a clinically useful and accurate measure of LOI, and by understanding conditions for which each of the aggregation measures is better suited. These developments will enhance the quality of decision support offered by QEMG techniques, thus improving the diagnosis, treatment and management of neuromuscular disorders.

Neuromuscular disorders change the underlying structure and function of motor units within a muscle, and are detected using needle electromyography. Currently, inferences about the presence or absence of disease are made subjectively and are largely impression-based. Quantitative electromyography (QEMG) attempts to improve upon the status quo by providing greater levels of precision, objectivity and reproducibility through numeric analysis, however, their results must be transparently presented and explained to be clinically viable. The probabilistic muscle characterization (PMC) model is ideally suited for a clinical decision support system (CDSS) and has many analogues to the subjective analysis currently used. To improve disease characterization performance globally, a hierarchical classification strategy is developed that accounts for the wide range of MUP feature values present at different levels of involvement (LOI) of a disorder. To improve utility, methods for detecting LOI are considered that balance the accuracy in reporting LOI with its clinical utility. Finally, several aggregation methods that represent commonly used human decision-making strategies are considered and evaluated for their suitability in a CDSS. Four aggregation measures (Average, Bayes, Adjusted Bayes, and WMLO) are evaluated, that offer a compromise between two common decision making paradigms: conservativeness (average) and extremeness (Bayes). Standard classification methods have high specificity at a cost of poor sensitivity at low levels of disease involvement, but tend to improve with disease progression. The hierarchical model is able to provide a better balance between low-LOI sensitivity and specificity by providing the classifier with more concise definitions of abnormality due to LOI. Furthermore, a method for detecting two discrete levels of disease involvement (low and high) is accomplished with reasonable accuracy. The average aggregation method offers a conservative decision that is preferred when the quality of the evidence is poor or not known, while the more extreme aggregators such as Bayes rule perform optimally when the evidence is accurate, but underperform otherwise due to outlier values that are incorrect. The methods developed offer several improvements to PMC, by providing a better balance between sensitivity and specificity, through the definition of a clinically useful and accurate measure of LOI, and by understanding conditions for which each of the aggregation measures is better suited. These developments will enhance the quality of decision support offered by QEMG techniques, thus improving the diagnosis, treatment and management of neuromuscular disorders.

L’objectif de cette thèse est de développer des méthodes de décomposition des signaux électromyographiques (EMG) en signaux élémentaires, les trains de potentiels d’action d’unité motrice (TPAUM). Nous avons proposé deux modèles de génération des signaux et nous avons mis en évidence la propriété de cyclostationnarité et de cyclostationnarité floue de ces deux modèles. Dans l’objectif de la décomposition, nous avons enfin proposé une méthode de décomposition aveugle à partir de signaux EMG multi-capteurs en utilisant cette propriété. Nous présentons les limitations théoriques de la méthode, notamment par un seuil limite de la fréquence de décharge. Nous avons effectué une évaluation des performances de la méthode proposée avec comparaison à une méthode classique de séparation à l’ordre 2.Il a été montré que l’exploitation de la propriété de cyclostationnarité apportait de meilleures performances de séparation dans le cas bruité et non bruité, sur le modèle cyclostationnaire et sur le modèle cyclostationnaire flou. Les performances se trouvent dégradées lorsque la fréquence de décharge dépasse le seuil théorique. Cette évaluation a été réalisée au moyen de simulations de Monte-Carlo construites sur des observations réelles. Enfin, la méthode appliquée sur des données réelles a montré de bons résultats sur des signaux EMG intramusculaires. / The aim of this thesis is to develop decomposition methods of electromyographic (EMG) signals into elementary signals, called motor unit action potential trains (MUAPT). We proposed two signal generation models and we have demonstrated the cyclostationary and fuzzy cyclostationary properties of these. We finally proposed a blind decomposition method from multi-sensor EMG signals using these properties. We present the theoretical limitations of the method, in particular the existence of a limiting threshold of the discharge frequency. We conducted a performance evaluation of the proposed method with a comparison with conventional 2nd order separation method. It has been shown that the contribution of cyclostationarity property brings better performance in noisy and noiseless cases and in the cyclostationary and fuzzy cyclostationary model cases. We highlighted a performance degradation when the discharge frequency was beyond the theoretical threshold. This evaluation was performed via Monte Carlo simulations based on real observations. Finally, we presented real EMG signals results. The method has shown good results on intramuscular EMG signals.

Electric muscle stimulation (EMS) has been widely used in the rehabilitation of musculoskeletal injuries. Patterned electrical neuromuscular stimulation (PENS), a specific form of EMS, has been developed to better educate muscles to contract properly. The physiological efficacy of PENS has not been quantifiably identified. OBJECTIVES: The aim of this study is to determine the acute effect of one PENS training session (3 sets of 10 1-sec repetitions) on maximal isometric knee extensor (MVIC) torque production and surface EMG (sEMG) in healthy nonathlete college students. DESIGN: A randomized repeated-measures design was used in this study. METHODS: Twenty-two male college students participated in the study. All participants completed two training sessions, one with PENS and one without, in a randomized crossover design. RESULTS: One bout of PENS training significantly increased MVIC (3.1% ± 1.7%, p = 0.03) which was greater than the change in MVIC of the control group (p = 0.03). Control training did not alter MVIC but resulted in significant decrease in average sEMG amplitude (-7.8% ± 1.6%, p ≤ 0.01) and peak sEMG amplitude (-10.4% ± 2.7%, p ≤ 0.01). These reductions in sEMG following control training were significantly different from the PENS group (p = 0.03 and p ≤ 0.01). CONCLUSIONS: The findings suggest that strength training in conjunction with PENS can enhance torque production after just one bout of training. The increase in torque with no change in sEMG amplitude can be explained by increased motor unit synchronization or decreased cocontraction of antagonist muscles.

Functional recovery following peripheral nerve injuries is poor due to muscle atrophy and fibrosis being major contributing factors. Electrical muscle stimulation has been used for decades in some capacity to treat denervation related muscular changes. The research presented in this thesis explores a new stimulation paradigm and its effects on short and long term muscle denervation. The first part of this work describes the new stimulation paradigm and the design and development of the stimulator used to deliver this paradigm. The paradigm involved daily 1-hour stimulation sessions featuring 600 contractions at high stimulus frequencies (100 Hz) and low pulse durations (200 μs). To test the device and paradigm, a pilot study involving muscle stimulation throughout a one month period of denervation in rat lower limb muscles was carried out. The results showed that this short but intense stimulus session significantly reduced the rate of muscle atrophy compared to animals that did not receive stimulation. Furthermore, muscle weight and consequently muscle force were also significantly greater. The stimulus paradigm was then used to investigate muscle that was denervated and immediately repaired. Ideally, immediate nerve repair following nerve injuries produces the best outcome. One month of electrical muscle stimulation following nerve repair enhanced this outcome through significant increases in muscle weight and force. Additionally, contrary to many previous studies, the stimulus paradigm had no negative effects on reinnervation. Taken together, electrical muscle stimulation can provide significant improvements over the best case scenario of immediate nerve repair. The third part of this work investigated the use of chronic electrical muscle stimulation throughout three months of denervation and the impact on reinnervation. Results showed that reinnervation in chronically stimulated animals were no different than animals that were denervated and immediately repaired. The last part of this work combined the use of electrical muscle stimulation with sensory protection in chronically denervated muscle. Sensory protection involves suturing a sensory nerve to protect a muscle during denervation and was shown in previous studies to reduce muscle atrophy, preserve muscle spindles and the structure of the distal nerve stump. The results showed significantly greater muscle weights and force in the combined treatment compared to the individual treatments alone. Reinnervation in these animals was as good as those that were immediately repaired. This suggests that contractile support combined with sensory protection may provide superior functional outcomes in chronically denervated muscle. The findings presented in this thesis provide new evidence for the use of short duration daily electrical muscle stimulation immediately following nerve repair or throughout long term denervation. Evidence for a new therapy, muscle stimulation with sensory protection, is also presented and shown to provide superior functional outcomes compared to either therapy alone. The contributions made in this body of work may provide clinicians with evidence to pursue clinical use of the outlined strategies and ultimately help patients optimally recover from peripheral nerve injuries. / Doctor of Philosophy (PhD)

La sclérose latérale amyotrophique est une maladie neurodégénérative fatale caractérisée par la dégénérescence progressive des neurones moteurs centraux et périphériques. L’un des premiers signes de la maladie est la dénervation de la jonction neuromusculaire (JNM). Les diverses unités motrices (UM) ne présentent toutefois pas la même vulnérabilité à la dénervation dans la SLA: les UM rapide fatigables sont en fait les plus vulnérables et les UM lentes sont les plus résistantes. Alors que des études précédentes ont démontré dans plusieurs modèles animaux de la SLA de nombreuses variations synaptiques, les découvertes ont été contradictoires. Par ailleurs, le type d’UM n’a pas été tenu en compte dans ces divers travaux. Nous avons donc émis l’hypothèse que la présence de la mutation SOD1 pourrait affecter différemment la transmission synaptique des UM, en accord avec leur vulnérabilité sélective. En effectuant des enregistrements électrophysiologiques et de l’immunohistochimie, nous avons étudié la transmission synaptique des différents types d’UM du muscle à contraction rapide Extensor Digitorum Longus (EDL; rapide fatigable (FF) MU) et du muscle à contraction lente Soleus (SOL; lente (S) and rapide fatigue-résistante (FR) MU) de la souris SOD1G37R et leur congénères WT. Pour identifier le type d’UM, un marquage par immunohistochimie des chaînes de myosine a été effectué. Un triple marquage de la JNM a également été effectué pour vérifier son intégrité aux différents stades de la maladie. À P160, dans la période asymptomatique de la maladie, alors qu’aucune altération morphologique n’était présente, l’activité évoquée était déjà altérée différemment en fonction des UM. Les JNMs FF mutantes ont démontré une diminution de l’amplitude des potentiels de plaque motrice (PPM) et du contenu quantique, alors que les JNMs lentes démontraient pratiquement le contraire. Les JNMs FR montraient quant à elles une force synaptique semblable au WT. À P380, dans la période présymtomatique, de nombreuses altérations morphologiques ont été observées dans le muscle EDL, incluant la dénervation complète, l’innervation partielle et les extensions du nerf. La transmission synaptique évoquée des UM FF étaient toujours réduites, de même que la fréquence des potentiels de plaque motrice miniatures. À P425, à l’apparition des premiers symptômes, l’activité synaptique des JNMs S était redevenue normale alors que les JNMs FR ont montré à ce moment une diminution du contenu quantique par rapport au contrôle. De manière surprenante, aucun changement du ratio de facilitation n’a été observé malgré les changements flagrants de la force synaptique. Ces résultats révèlent que la fonction de la JNM est modifiée différemment en fonction de la susceptibilité des UM dans l’ALS. Cette étude fournit des pistes pour une meilleure compréhension de la physiologie de la JNM durant la pathologie qui est cruciale au développement d’une thérapie adéquate ciblant la JNM dans la SLA. / Amyotrophic lateral sclerosis (ALS) is a fatal late-onset neurodegenerative disease characterized by the progressive loss of upper and lower motor neurons. Denervation of the neuromuscular junction (NMJ) is an early pathological event in various ALS models. Motor units (MU) appear unequally susceptible to denervation, the fast fatigable (FF) MU being the most vulnerable and the slow (S) MU the most resistant. While previous studies in several ALS models have consistently reported alterations in synaptic transmission, their findings have been contradictory. Interestingly, the MU types were not taken into account in these studies, which could explain these discrepancies. We hypothesized that the MU selective vulnerabilities observed in ALS will be associated with MU-specific NMJ alterations throughout the disease course. Using electrophysiology, we studied synaptic transmission of different types of MU in the fast-twitch Extensor Digitorum Longus (EDL; fast fatigable (FF) MU) and the slow-twitch Soleus (SOL; slow (S) and fast fatigue resistant (FR) MU) of the SOD1 mice and their WT littermates. MU types were identified using immunohistochemical labelling of the respective myosine heavy chains. Immunohistochemistry was also performed to assess NMJ integrity by using antibodies against main NMJ components. At a presymptomatic stage (P160), while no morphological alterations of NMJs were seen in both muscles, evoked activity was altered in a MU-specific manner in SOD1 mice. FF MU from SOD1 mice showed a decrease in EPP amplitude and quantal content whereas S MU showed the opposite. Mutant FR MU showed no difference in evoked activity compared to WT. At presymptomatic stage (P380), various morphological alterations were seen in the SOD1 EDL, including denervation, partial innervation and nerve sprouting. Evoked activity was still reduced in FF MU, as well as mEPP frequency. In contrast, at disease onset (P425), synaptic strength of the S MU was now similar to WT MU, whereas FR NMJs showed a decrease in EPP amplitude and quantal content. Surprisingly, paired-pulse facilitation was not altered in any MU type and at any age despite changes in synaptic strength. Taken together, these results reveal that NMJ function is differentially modified according to MU susceptibility in ALS. This study provides insights for a better understanding of NMJ physiology during the illness that is crucial to the development of a proper NMJ-targeted treatment in ALS.