We know very little about how muscles and motor units in one region of the upper airway are impacted by adjustments in an adjacent airway region. In this case, the focus is on regulation of the expiratory airstream by the larynx and how changes in laryngeal aperture impact muscle motor unit activities downstream in the pharynx. We selected sound production as a framework for study as it requires (i) sustained expiratory airflow, (ii) laryngeal airway regulation for production of whisper and voice, and (iii) pharyngeal airway regulation for production of different vowel sounds. We used these features as the means of manipulating expiratory airflow, pharyngeal, and laryngeal airway opening to compare the effect of each on the activation of genioglossus (GG) muscle motor units in the pharynx. We show that some GG muscle motor units (a) discharge stably on expiration associated with production of vowel sounds, (b) are exquisitely sensitive to subtle alterations in laryngeal airflow, and (c) discharge at higher firing rates in high flow vs. low flow conditions even when producing the same vowel sound. Our results reveal subtle changes in GG motor unit discharge rates that correlate with changes imposed at the larynx, and which may contribute to the regulation of the expiratory airstream.

In this study, we used a custom made quadrifilar needle electrode and multichannel electromyography (EMG) software tool to decompose EMG signals and investigate the behavior of motor unit discharge rate (MUDR) of concurrently active motor units in patients with amyotrophic lateral sclerosis (ALS). Decomposition is a technique used to break down the complex EMG signal into its constituent motor units. A motor unit is a single alpha motor neuron and all the muscle fibers it innervates. ALS is a progressive degenerative disorder of both the upper and lower motor neurons. We recorded four differentially amplified EMG signals from the first dorsal interosseous (FDI) muscle of six ALS patients (four with predominant lower motor neuron pathology and two with predominant upper motor neuron pathology) and seven control subjects. Recordings were made from force contractions of 20 and 50% of maximum voluntary contraction (MVC). All control subjects were between the ages of 40 and 70 years and were examined by a practicing physiatrist for exclusion criteria including neuromuscular disorders or any medications that might affect muscle activity. We observed differences in initial firing rates and variability of active motor units between control subjects and ALS patients. Furthermore we observed differences in firing rates and variability of active motor units between ALS patients with predominant upper motor neuron pathology and ALS patients with predominant lower motor neuron pathology. Initial motor unit firing rates for control subjects were 16.22 +/- 2.06 Hz at 20% MVC and 19.79 +/- 1.66 Hz at 50% MVC. As expected, initial motor unit firing rates from patients with predominant lower motor neuron pathology were higher than those of control subjects; 18.87 +/- 4.73 Hz at 20% MVC and 24.28 +/- 5.01 Hz at 50% MVC. ALS patients with predominant upper motor neuron pathology, as expected, had initial motor unit firing rates that were lower than those observed in control subjects; 9.22 +/- 1.68 Hz at 20% MVC and 12.83 +/- 2.26 Hz at 50% MVC. Motor unit firing rate time series in ALS patients with predominant upper motor neuron pathology showed decreased variability, 0.99 +/- 0.17 Hz at 20% MVC and 1.70 +/- 0.52 Hz at 50% MVC, when compared to control subjects, 2.37 +/- 0.67 at 20% MVC and 4.20 +/- 1.00 at 50% MVC. Variability of motor unit firing rate time series in ALS patients with predominant lower motor neuron were high, 3.38 +/- 1.2 Hz at 20% MVC and 4.07 +/- 1.56 Hz at 50% MVC, compared to control subjects. At 50% MVC, motor unit substitution was observed in ALS patients with predominant upper motor neuron pathology despite the contractions lasting just a few seconds. Motor unit action potentials (MUAPs) recorded from patients were polyphasic when compared to those from control subjects, as is characteristically found in practice.

Motor unit number estimation (MUNE) is an electrodiagnostic procedure used to estimate the number of MUs in a muscle. In this thesis, a new MUNE technique, called Automated MPS, has been developed to overcome the shortcomings of two current techniques, namely MPS and MUESA. This method can be summarized as follows. First, a muscle is stimulated with a train of constant intensity current pulses. Depending on various factors, one to three MUs activate probabilistically after each pulse, and several responses are collected. These collected responses should be divided into up to 2^n clusters, such that each cluster represents one possible combination of n Surface-detected Motor Unit Potentials (SMUPs). After clustering the collected responses, the average response of each cluster is calculated, the outliers are excluded, and similar groups are merged together. Then, depending on the number of response set groups, a decomposition technique is applied to the response clusters to obtain the $n$ constituent SMUPs. To estimate the number of MUs, the aforementioned process is repeated several times until enough SMUPs to calculate a reliable mean-SMUP are acquired. The number of MUs can then be determined by dividing the maximal compound muscle action potential (CMAP) size by the mean-SMUP size. The focus of this thesis was on using pattern recognition techniques to detect n SMUPs from a collected set of waveforms. Several experiments were performed using both simulated and real data to evaluate the ability of Automated MPS in finding the constituent SMUPs of a response set. Our experiments showed that performing Automated MPS needs less experience compared with MPS. Moreover, it can deal with more difficult situations and detect more accurate SMUPs compared with MUESA.

Motor unit number estimation (MUNE) is an electrodiagnostic procedure used to estimate the number of MUs in a muscle. In this thesis, a new MUNE technique, called Automated MPS, has been developed to overcome the shortcomings of two current techniques, namely MPS and MUESA. This method can be summarized as follows. First, a muscle is stimulated with a train of constant intensity current pulses. Depending on various factors, one to three MUs activate probabilistically after each pulse, and several responses are collected. These collected responses should be divided into up to 2^n clusters, such that each cluster represents one possible combination of n Surface-detected Motor Unit Potentials (SMUPs). After clustering the collected responses, the average response of each cluster is calculated, the outliers are excluded, and similar groups are merged together. Then, depending on the number of response set groups, a decomposition technique is applied to the response clusters to obtain the $n$ constituent SMUPs. To estimate the number of MUs, the aforementioned process is repeated several times until enough SMUPs to calculate a reliable mean-SMUP are acquired. The number of MUs can then be determined by dividing the maximal compound muscle action potential (CMAP) size by the mean-SMUP size. The focus of this thesis was on using pattern recognition techniques to detect n SMUPs from a collected set of waveforms. Several experiments were performed using both simulated and real data to evaluate the ability of Automated MPS in finding the constituent SMUPs of a response set. Our experiments showed that performing Automated MPS needs less experience compared with MPS. Moreover, it can deal with more difficult situations and detect more accurate SMUPs compared with MUESA.

Spinal motoneurons (MNs) relay neural commands from the brain to the muscles to produce functional movement. However, MNs are more than passive conduits of neural commands; they also shape motor output through alterations in their intrinsic excitability. These alterations allow MNs to modify (e.g., amplify and/or prolong) motor output even in the absence of descending motor commands. How MNs respond to this modulation, under various conditions, is not fully understood. In the scope of this dissertation, we leverage high-density electromyography and motor unit decomposition algorithms to investigate how human MNs behave in (Aim 1) different muscles under similar task demands; (Aim 2) the same muscle under different task demands; and (Aim 3) in response to exogenous neuromodulation. First, in Aim 1 we demonstrate that MN excitability varies across motor pools and, thus, may be functionally tuned to the task and its muscle-specific demands. The results indicate that the MN discharge rates were significantly higher in the first dorsal interosseous, a small hand muscle used for fine motor control. Conversely, higher MN excitability was observed within the tibialis anterior, a lower leg muscle involved in balance and locomotion. Next, in Aim 2 we show that a muscle (i.e., the biceps brachii) with multiple biomechanical functions (e.g., supination and flexion) receives differential synaptic input to perform each action while the MN discharge characteristics remain the same. Finally, in Aim 3 we demonstrate that a single cup of coffee can alter fundamental motor control mechanisms by increasing discharge rate, inter-pulse variability, and excitability through caffeine-induced neuromodulation. Collectively, findings from this dissertation demonstrate the human motor system’s tremendous ability to adapt to internal and external states. / Public Health

To effect movement, the central nervous system must appropriately coordinate the activities of pools of motoneurons (MNs), the cells which control muscle fibers. Sources of neural drive are often distributed to many MNs of a pool, and thus can synchronize the activities of targeted MNs. In this thesis, synchronization among MNs is used to investigate the strength, temporal progression, and anatomical distribution of neural drive to the hypoglossal motor nucleus (HMN), which controls muscles of the tongue. The HMN is an ideal target for such an investigation because it processes a host of functionally diverse inputs, such as those related to breathing, speaking, and swallowing. Study 1 characterizes motor unit (MU) synchronization within and across bellies of the human genioglossus (GG) muscle when MUs are activated by cortical drive (during voluntary tongue protrusion) or by automatic, brainstem-mediated drive (during rest breathing). We show that voluntary tongue protrusion synchronizes MU spike timing and firing rates within but not across bellies of the GG, whereas during rest breathing, MU firing rates are moderately synchronized both within and across muscle bellies. Study 2 documents respiratory-related synchronization of MU activities in muscles of the tongue and respiratory pump using an anesthetized rat model. The results of this study indicate that upper airway and respiratory pump MN pools share a low frequency respiratory-related drive, but that higher frequency (>8 Hz) synchronization is strongest in MU pairs of the chest-wall. Finally, Study 3 examines the potential for GG multi-unit and single MU activities to be entrained by cortical input. We show that during voluntary tongue protrusion, cortical oscillations in the 15-40 Hz range weakly synchronize MU population activity, and that EEG oscillations in this range intermittently influence the spike timing of individual GG MUs. These studies are the first to characterize MU synchronization by different sources of neural input to the HMN and establish a broad foundation for further investigation of hypoglossal motor control.

Electromyographic (EMG) signal decomposition is the process of resolving an EMG signal into its component motor unit potential trains (MUPTs). The extracted MUPTs can aid in the diagnosis of neuromuscular disorders and the study of the neural control of movement, but only if they are valid trains. Before using decomposition results and the motor unit potential (MUP) shape and motor unit (MU) firing pattern information related to each active MU for either clinical or research purposes the fact that the extracted MUPTs are valid needs to be confirmed. The existing MUPT validation methods are either time consuming or related to operator experience and skill. More importantly, they cannot be executed during automatic decomposition of EMG signals to assist with improving decomposition results. To overcome these issues, in this thesis the possibility of developing automatic MUPT validation algorithms has been explored. Several methods based on a combination of feature extraction techniques, cluster validation methods, supervised classification algorithms, and multiple classifier fusion techniques were developed. The developed methods, in general, use either the MU firing pattern or MUP-shape consistency of a MUPT, or both, to estimate its overall validity. The performance of the developed systems was evaluated using a variety of MUPTs obtained from the decomposition of several simulated and real intramuscular EMG signals. Based on the results achieved, the methods that use only shape or only firing pattern information had higher generalization error than the systems that use both types of information. For the classifiers that use MU firing pattern information of a MUPT to determine its validity, the accuracy for invalid trains decreases as the number of missed-classification errors in trains increases. Likewise, for the methods that use MUP-shape information of a MUPT to determine its validity, the classification accuracy for invalid trains decreases as the within-train similarity of the invalid trains increase. Of the systems that use both shape and firing pattern information, those that separately estimate MU firing pattern validity and MUP-shape validity and then estimate the overall validity of a train by fusing these two indices using trainable fusion methods performed better than the single classifier scheme that estimates MUPT validity using a single classifier, especially for the real data used. Overall, the multi-classifier constructed using trainable logistic regression to aggregate base classifier outputs had the best performance with overall accuracy of 99.4% and 98.8% for simulated and real data, respectively. The possibility of formulating an algorithm for automated editing MUPTs contaminated with a high number of false-classification errors (FCEs) during decomposition was also investigated. Ultimately, a robust method was developed for this purpose. Using a supervised classifier and MU firing pattern information provided by each MUPT, the developed algorithm first determines whether a given train is contaminated by a high number of FCEs and needs to be edited. For contaminated MUPTs, the method uses both MU firing pattern and MUP shape information to detect MUPs that were erroneously assigned to the train. Evaluation based on simulated and real MU firing patterns, shows that contaminated MUPTs could be detected with 84% and 81% accuracy for simulated and real data, respectively. For a given contaminated MUPT, the algorithm on average correctly classified around 92.1% of the MUPs of the MUPT. The effectiveness of using the developed MUPT validation systems and the MUPT editing methods during EMG signal decomposition was investigated by integrating these algorithms into a certainty-based EMG signal decomposition algorithm. Overall, the decomposition accuracy for 32 simulated and 30 real EMG signals was improved by 7.5% (from 86.7% to 94.2%) and 3.4% (from 95.7% to 99.1%), respectively. A significant improvement was also achieved in correctly estimating the number of MUPTs represented in a set of detected MUPs. The simulated and real EMG signals used were comprised of 3–11 and 3–15 MUPTs, respectively.

The main goal of this thesis was to discover the relationships between MU characteristics and MUP features. To reach this goal, several features explaining the anatomical structure of the muscle were introduced. Additionally, features representing specific properties of the EMG signal detected from that muscle, were defined. Since information regarding the underlying anatomy was not available from real data, a physiologically based muscle model was used to extract the required features. This muscle model stands out from others, by providing similar acquisition schemes as the ones utilized by physicians in real clinical settings and by modelling the interactions among different volume conductor factors and the collection of MUs in the muscle in a realistic way. Having the features ready, several relationship discovery techniques were used, to reveal relationships between MU features and MUP features. To interpret the results obtained from the correlation analysis and pattern discovery techniques properly, several algorithms and new statistics were defined. The results obtained from correlation analysis and pattern discovery technique were similar to each other, and suggested that to maximize the inter-relationships between MUP features and MU features, MUPs could be filtered based on their slope values, specifically MUPs with slopes lower than 0. 6 v/s could be excluded. Additionally PDT results showed that high slope MUPs were not as informative about the underlying MU and could be excluded to maximize the relationships between MUP features and MU characteristics. Certain MUP features were determined to be highly related to certain MU characteristics. MUP <em>area</em> and <em>duration</em> were shown to be the best representative feature for the MU size and <em>average fiber density</em>, respectively. For the distribution of fiber diameter in the MU, <em>duration</em> and <em>number of turns</em> were determined to reflect <em>mean fiber diameter</em> and <em>stdv of fiber diameter</em> the best, correspondingly.

The main goal of this thesis was to discover the relationships between MU characteristics and MUP features. To reach this goal, several features explaining the anatomical structure of the muscle were introduced. Additionally, features representing specific properties of the EMG signal detected from that muscle, were defined. Since information regarding the underlying anatomy was not available from real data, a physiologically based muscle model was used to extract the required features. This muscle model stands out from others, by providing similar acquisition schemes as the ones utilized by physicians in real clinical settings and by modelling the interactions among different volume conductor factors and the collection of MUs in the muscle in a realistic way. Having the features ready, several relationship discovery techniques were used, to reveal relationships between MU features and MUP features. To interpret the results obtained from the correlation analysis and pattern discovery techniques properly, several algorithms and new statistics were defined. The results obtained from correlation analysis and pattern discovery technique were similar to each other, and suggested that to maximize the inter-relationships between MUP features and MU features, MUPs could be filtered based on their slope values, specifically MUPs with slopes lower than 0. 6 v/s could be excluded. Additionally PDT results showed that high slope MUPs were not as informative about the underlying MU and could be excluded to maximize the relationships between MUP features and MU characteristics. Certain MUP features were determined to be highly related to certain MU characteristics. MUP <em>area</em> and <em>duration</em> were shown to be the best representative feature for the MU size and <em>average fiber density</em>, respectively. For the distribution of fiber diameter in the MU, <em>duration</em> and <em>number of turns</em> were determined to reflect <em>mean fiber diameter</em> and <em>stdv of fiber diameter</em> the best, correspondingly.

The delayed occurrence of fatigue while maintaining submaximal force output is a function that could be driven by the central nervous system (CNS). It has been found previously that mean EMG amplitude increases with fatigue. Endurance time has also been found to increase over repeated testing. The purpose of this study was to compare the muscle activation patterns and endurance times after training of the AdP muscle. This study analyzed surface EMG of the adductor pollicis (AdP) muscle in young, healthy adults during a sustained submaximal isometric fatiguing contraction before and after 4 weeks of muscular endurance task training. Eight participants (training group: n = 4 and control group: n = 4) carried out maximal voluntary contractions (MVCs) while sustaining isometric force of 20% MVC of thumb adduction before and after the four weeks of endurance training. EMG, recorded through surface electrodes, was measured before and after training in an effort to detect a possible CNS training effect. The endurance training group trained the AdP muscle at 20% MVC every other day for 4 weeks. Average force was calculated over 5 second time bins every 5% of endurance time (20 time bins total). A significant increase in endurance time was seen in the training group of this study. A significant effect of change for pre and post-training mean EMG amplitude across the two groups was found (p < .001). A significant interaction effect between pre and post training and control groups was also found (p = .016). There was also a significant deficit in increases of mean amplitude between the first and last time bins of the endurance task (pre and post) after training. This indicates that there is an effect of training on increasing endurance time which can be exhibited through changes in mean EMG amplitude. / text