Biomarkers of Motor Neurone Disease: Neurophysiological studies of lower motor neuron degeneration

Motor neurone disease (MND) is a fatal disease characterized by relentless degeneration of both upper motor neurones (UMN) and lower motor neurones (LMN) leading to progressive muscular paralysis, with death usually occurring 1 to 5 years after the onset. Despite the recent advances in research, the cause of motor neuron death and the pathological determinants of disease progression in MND still remain poorly understood. There is no efficient treatment of the disease and further clinical trials are required to test potential therapies. Clinical heterogeneity is a recognized feature of MND and is reflected in differences in survival time and rate of progression. From a pathophysiological point of view, this raises the question of whether MND is a single disease entity or a syndrome of different clinical entities arising from distinct biologic mechanisms. Clinical assessment by way of the traditional clinical examination remains the best way to assess neurodegeneration and to follow the process in real time but is not able to detect subclinical UMN or LMN involvement. Therefore, objective biomarkers of UMN and LMN dysfunction are useful not only as endpoint measures sensitive to early therapeutic effects but have the potential to shed light on the disease pathogenesis in MND and might also resolve complexities of clinical heterogeneity in clinical trials. We hypothesized that Bayesian motor unit number estimation (MUNE) is a sensitive biomarker of lower motor neuron degeneration in MND. We performed serial studies in a large cohort of MND patients to evaluate motor unit (MU) loss in different regions of the spinal cord and among clinical subtypes of MND The half life of MUs determined by serial Bayesian MUNE was able to demonstrate selective vulnerability of different motor neuron pools during the disease process and the spread of disease from the site of onset. It also distinguished the clinical subtypes with different survival time. Our findings were supported by previous reports using clinical and neuropathological methods in human subjects and animal models of MND. Therefore Bayesian MUNE has the potential to be a sensitive biomarker of disease process in MND subjects. Furthermore, since both the MU number and size have not been considered in previous MUNE methods, Bayesian MUNE provides useful information about MU sprouting in MND. Our results of serial studies of Bayesian MUNE have confirmed its potential as a biomarker of disease progression and as a reliable, reproducible, practical and tolerable method