<p>Myasthenia gravis (MG) is an autoimmune neuromuscular disorder that causes fluctuating muscle weakness. MG may be divided into an ocular form and a generalized form based on the involved muscles. Treatment differs between these different MG forms. The majority (80%) of patients with generalized MG are seropositive for antibodies against the acetylcholine receptor (AChR-Ab). Recently a new antibody was detected against muscle specific tyrosine kinase (MuSK) in about 40% of patients who are AChR-Ab seronegative. A few patients with MuSK-Abs have muscular atrophies, as well as electrophysiological myopathy.</p><p>In this thesis we have characterized MuSK-Ab seropositive [MuSK(+)] patients using clinical parameters, including health-related quality of life (hrQoL), neurophysiology and muscle morphology, and compared them to patients with and without AChR-Abs. The question concerned which factors contribute to their muscle weakness. Additionally, we wanted to determine if single-fiber electromyography (SFEMG) in a limb muscle has any predictive value for generalization of ocular MG. </p><p>Our results suggest that MuSK(+) patients more often have a myopathic electromyography pattern, although this pattern is found also in other immunological subtypes of MG. The myopathic pattern may be associated with the frequently found mitochondrial abnormalities. However, disturbed neuromuscular transmission is the primary cause of muscle weakness in the majority of MuSK(+) patients, as well as in AChR-Ab seropositive patients. The disease-specific hrQoL MG questionnaire was successfully validated into Swedish and these scores correlated with disturbed neuromuscular transmission in a proximal arm muscle. Abnormal SFEMG findings occur also in muscles outside of the facial area in ocular MG, although this is not predictive of subsequent generalization. </p><p>MuSK (+) patients have little or no beneficial effect of acetylcholine esterase inhibitors (AChEI). On the contrary AChEI may produce profound adverse effects. We present the hypothesis that this effect of AChEI is due to abnormal receptor morphology in MuSK(+) patients.</p>

Myasthenia gravis (MG) is an autoimmune neuromuscular disorder that causes fluctuating muscle weakness. MG may be divided into an ocular form and a generalized form based on the involved muscles. Treatment differs between these different MG forms. The majority (80%) of patients with generalized MG are seropositive for antibodies against the acetylcholine receptor (AChR-Ab). Recently a new antibody was detected against muscle specific tyrosine kinase (MuSK) in about 40% of patients who are AChR-Ab seronegative. A few patients with MuSK-Abs have muscular atrophies, as well as electrophysiological myopathy. In this thesis we have characterized MuSK-Ab seropositive [MuSK(+)] patients using clinical parameters, including health-related quality of life (hrQoL), neurophysiology and muscle morphology, and compared them to patients with and without AChR-Abs. The question concerned which factors contribute to their muscle weakness. Additionally, we wanted to determine if single-fiber electromyography (SFEMG) in a limb muscle has any predictive value for generalization of ocular MG. Our results suggest that MuSK(+) patients more often have a myopathic electromyography pattern, although this pattern is found also in other immunological subtypes of MG. The myopathic pattern may be associated with the frequently found mitochondrial abnormalities. However, disturbed neuromuscular transmission is the primary cause of muscle weakness in the majority of MuSK(+) patients, as well as in AChR-Ab seropositive patients. The disease-specific hrQoL MG questionnaire was successfully validated into Swedish and these scores correlated with disturbed neuromuscular transmission in a proximal arm muscle. Abnormal SFEMG findings occur also in muscles outside of the facial area in ocular MG, although this is not predictive of subsequent generalization. MuSK (+) patients have little or no beneficial effect of acetylcholine esterase inhibitors (AChEI). On the contrary AChEI may produce profound adverse effects. We present the hypothesis that this effect of AChEI is due to abnormal receptor morphology in MuSK(+) patients.

The quantitative analysis of decomposed electromyographic (EMG) signals reveals information for diagnosing and characterizing neuromuscular disorders. Neuromuscular jitter is an important measure that reflects the stability of the operation of a neuromuscular junction. It is conventionally measured using single fiber electromyographic (SFEMG) techniques. SFEMG techniques require substantial physician dexterity and subject cooperation. Furthermore, SFEMG needles are expensive, and their re-use increases the risk of possible transmission of infectious agents. Using disposable concentric needle (CN) electrodes and automating the measurment of neuromuscular jitter would greatly facilitate the study of neuromuscular disorders. An improved automated jitter measurment system based on the decomposition of CN detected EMG signals is developed and evaluated in this thesis. Neuromuscular jitter is defined as the variability of time intervals between two muscle fiber potentials (MFPs). Given the candidate motor unit potentials (MUPs) of a decomposed EMG signal, which is represented by a motor unit potential train (MUPT), the automated jitter measurement system designed in this thesis can be summarized as a three-step procedure: 1) identify isolated motor unit potentials in a MUPT, 2) detect the significant MFPs of each isolated MUP, 3) track significant MFPs generated by the same muscle fiber across all isolated MUPs, select typical MFP pairs, and calculate jitter. In Step one, a minimal spanning tree-based 2-phase clustering algorithm was developed for identifying isolated MUPs in a train. For the second step, a pattern recognition system was designed to classify detected MFP peaks. At last, the neuromuscular jitter is calculated based on the tracked and selected MFP pairs in the third step. These three steps were simulated and evaluated using synthetic EMG signals independently, and the whole system is preliminary implemented and evaluated using a small simulated data base. Compared to previous work in this area, the algorithms in this thesis showed better performance and great robustness across a variety of EMG signals, so that they can be applied widely to similar scenarios. The whole system developed in this thesis can be implemented in a large EMG signal decomposition system and validated using real data.

The quantitative analysis of decomposed electromyographic (EMG) signals reveals information for diagnosing and characterizing neuromuscular disorders. Neuromuscular jitter is an important measure that reflects the stability of the operation of a neuromuscular junction. It is conventionally measured using single fiber electromyographic (SFEMG) techniques. SFEMG techniques require substantial physician dexterity and subject cooperation. Furthermore, SFEMG needles are expensive, and their re-use increases the risk of possible transmission of infectious agents. Using disposable concentric needle (CN) electrodes and automating the measurment of neuromuscular jitter would greatly facilitate the study of neuromuscular disorders. An improved automated jitter measurment system based on the decomposition of CN detected EMG signals is developed and evaluated in this thesis. Neuromuscular jitter is defined as the variability of time intervals between two muscle fiber potentials (MFPs). Given the candidate motor unit potentials (MUPs) of a decomposed EMG signal, which is represented by a motor unit potential train (MUPT), the automated jitter measurement system designed in this thesis can be summarized as a three-step procedure: 1) identify isolated motor unit potentials in a MUPT, 2) detect the significant MFPs of each isolated MUP, 3) track significant MFPs generated by the same muscle fiber across all isolated MUPs, select typical MFP pairs, and calculate jitter. In Step one, a minimal spanning tree-based 2-phase clustering algorithm was developed for identifying isolated MUPs in a train. For the second step, a pattern recognition system was designed to classify detected MFP peaks. At last, the neuromuscular jitter is calculated based on the tracked and selected MFP pairs in the third step. These three steps were simulated and evaluated using synthetic EMG signals independently, and the whole system is preliminary implemented and evaluated using a small simulated data base. Compared to previous work in this area, the algorithms in this thesis showed better performance and great robustness across a variety of EMG signals, so that they can be applied widely to similar scenarios. The whole system developed in this thesis can be implemented in a large EMG signal decomposition system and validated using real data.