The stimulus-response curve and motor unit variability in normal subjects and subjects with amyotrophic lateral sclerosis

The behavior and stability of motor units (MUs) in response to electrical stimulation of different intensities can be assessed with the stimulus-response curve, which is a graphical representation of the size of the compound muscle action potential (CMAP) in relation to stimulus intensity. To examine MU characteristics across the whole stimulus range, the variability of CMAP responses to electrical stimulation, and the differences that occur between normal and disease states, the curve was studied in 11 normal subjects and 16 subjects with amyotrophic lateral sclerosis (ALS). In normal subjects, the curve showed a gradual increase in CMAP size with increasing stimulus intensity, although one or two discrete steps were sometimes observed in the upper half of the curve, indicating the activation of large MUs at higher intensities. In ALS subjects, large discrete steps, due to loss of MUs and collateral sprouting, were frequently present. Variability of the CMAP responses was greater than baseline variability, indicating variability of MU responses, and at certain levels this variability was up to 100 mu Vms. The stimulus-response curve shows differences between normal and ALS subjects and provides information on MU activation and variability throughout the curve.

Differential expression of the NMDA NR2B receptor subunit in motoneuron populations susceptible and resistant to amyotrophic lateral sclerosis

We have compared the expression pattern of NMDA receptor subunits (NR1 and NR2A-D)and NRI splice variants (NR1-1a/1b,-2a/2b,-3a/3b,4a/4b) in motor neuron populations from adult Wistar rats that are vulnerable (hypoglossal, XII) or resistant (oculomotor, III) to death in amyotrophic lateral sclerosis (ALS). The major finding was higher levels of expression of the NR2B subunit in the hypoglossal nucleus. Quantitative real-time PCR showed that NR1 was expressed at a greater level than any of the NR2 subunits (> 15 fold greater, P

Assessment of disease progression in motor neuron disease

Motor neuron disease (MND) is characterised by progressive deterioration of the corticospinal tract, brainstem, and anterior horn cells of the spinal cord. There is no pathognomonic test for the diagnosis of MND, and physicians rely on clinical criteria-upper and lower motor neuron signs-for diagnosis. The presentations, clinical phenotypes, and outcomes of MND are diverse and have not been combined into a marker of disease progression. No single algorithm combines the findings of functional assessments and rating scales, such as those that assess quality of life, with biological markers of disease activity and findings from imaging and neurophysiological assessments. Here, we critically appraise developments in each of these areas and discuss the potential of such measures to be included in the future assessment of disease progression in patients with MND.

Glycinergic and GABAergic synaptic activity differentially regulate motoneuron survival and skeletal muscle innervation

GABAergic and glycinergic synaptic transmission is proposed to promote the maturation and refinement of the developing CNS. Here we provide morphological and functional evidence that glycinergic and GABAergic synapses control motoneuron development in a region-specific manner during programmed cell death. In gephyrin-deficient mice that lack all postsynaptic glycine receptor and some GABA(A) receptor clusters, there was increased spontaneous respiratory motor activity, reduced respiratory motoneuron survival, and decreased innervation of the diaphragm. In contrast, limb-innervating motoneurons showed decreased spontaneous activity, increased survival, and increased innervation of their target muscles. Both GABA and glycine increased limb-innervating motoneuron activity and decreased respiratory motoneuron activity in wild-type mice, but only glycine responses were abolished in gephyrin-deficient mice. Our results provide genetic evidence that the development of glycinergic and GABAergic synaptic inputs onto motoneurons plays an important role in the survival, axonal branching, and spontaneous activity of motoneurons in developing mammalian embryos.

Motor unit number estimation - A Bayesian approach

All muscle contractions are dependent on the functioning of motor units. In diseases such as amyotrophic lateral sclerosis (ALS), progressive loss of motor units leads to gradual paralysis. A major difficulty in the search for a treatment for these diseases has been the lack of a reliable measure of disease progression. One possible measure would be an estimate of the number of surviving motor units. Despite over 30 years of motor unit number estimation (MUNE), all proposed methods have been met with practical and theoretical objections. Our aim is to develop a method of MUNE that overcomes these objections. We record the compound muscle action potential (CMAP) from a selected muscle in response to a graded electrical stimulation applied to the nerve. As the stimulus increases, the threshold of each motor unit is exceeded, and the size of the CMAP increases until a maximum response is obtained. However, the threshold potential required to excite an axon is not a precise value but fluctuates over a small range leading to probabilistic activation of motor units in response to a given stimulus. When the threshold ranges of motor units overlap, there may be alternation where the number of motor units that fire in response to the stimulus is variable. This means that increments in the value of the CMAP correspond to the firing of different combinations of motor units. At a fixed stimulus, variability in the CMAP, measured as variance, can be used to conduct MUNE using the "statistical" or the "Poisson" method. However, this method relies on the assumptions that the numbers of motor units that are firing probabilistically have the Poisson distribution and that all single motor unit action potentials (MUAP) have a fixed and identical size. These assumptions are not necessarily correct. We propose to develop a Bayesian statistical methodology to analyze electrophysiological data to provide an estimate of motor unit numbers. Our method of MUNE incorporates the variability of the threshold, the variability between and within single MUAPs, and baseline variability. Our model not only gives the most probable number of motor units but also provides information about both the population of units and individual units. We use Markov chain Monte Carlo to obtain information about the characteristics of individual motor units and about the population of motor units and the Bayesian information criterion for MUNE. We test our method of MUNE on three subjects. Our method provides a reproducible estimate for a patient with stable but severe ALS. In a serial study, we demonstrate a decline in the number of motor unit numbers with a patient with rapidly advancing disease. Finally, with our last patient, we show that our method has the capacity to estimate a larger number of motor units.