Ongoing Projects in Single Motor Unit Lab
Origins of Enhanced Motoneuron Excitability in Hemiparetic Stroke
Carol J. Mottram, Nina L. Suresh, Charles J. Heckman, Constantin Chikando and William Z. Rymer
Individuals who have suffered a stroke may exhibit profound spasticity that limits daily function. Clinically, spasticity is characterized by increased resistance of a joint to an externally imposed limb motion and is an expression of abnormal motoneuron excitability. The potential contribution of altered intrinsic motoneuron properties to spasticity in stroke survivors is being examined by comparing motor unit discharge properties in spastic muscle with those of healthy control subjects during voluntary ramp contractions with the biceps brahcii muscle. Preliminary data suggests that the contribution from intrinsic motoneuron properties to motoneuron excitability is greater for stroke survivors compared with healthy individuals.
Identification of Motor Unit Firing Patterns from an Intramuscular EMG Signal
Zeynep Erim and Winsean Lin
Investigation of the discharge activities of motor units presents a window into the organization and operation of the central nervous system. It is relatively easy to identify the firings of one or two motor units from the intramuscular EMG at low contraction levels. However, the desire to observe the discharge patterns of multiple motor units at higher forces while minimizing the number of intramuscular electrodes leads to the necessity of signal processing algorithms that are capable of identifying the action potentials of multiple motor units from a single recording comprising the simultaneous discharge activity of a group of motor units. This project employs an expert systems approach to the problem of motor unit decomposition, where the reasoning employed by an experienced operator in manually decomposing an intramuscular recording is incorporated in a fuzzy inference process.
Motor Unit Coupling across Muscles Involved in Abnormal Synergies Following Stroke
Zeynep Erim, Winsean Lin and Heidi Roth
One of the most debilitating sequelae of stroke is the inability to activate muscles outside of stereotypical synergies and consequently, the failure to control joints independently. The abnormal torque patterns and the overall muscle activity associated with abnormal synergies in hemiparetic subjects have been well described in the literature. However, the role of motor unit discharge patterns in the generation of these abnormal activation patterns has not been addressed. The goal of this project is to investigate whether abnormal coupling of motor units underlies this loss of independence in the activation of muscles following stroke. We hope that in addition to enhancing the insight into the stroke-induced loss of independent functioning of muscles, the investigation of the role of abnormal motor unit coupling in the evolution of abnormal coactivation patterns may present a means for identifying individuals who are likely to develop abnormal muscle synergies thus facilitating early intervention.
Coherence between EEG and Motor Unit Discharges
Zeynep Erim and Bernard A. Conway3
Since the original report of coherence between the EEG and surface EMG in the beta band (13-35 Hz), corticomuscular coherence has received much attention from many labs around the world. It has been investigated in different tasks, under varying levels of load compliance, in isometric contractions versus movements and the results have been interpreted as indicating the coupling between the motor cortex and the motorneuron pool. However, rarely have studies recorded the activity of isolated motor units and almost exclusively rely on inferences drawn from the compound surface EMG signal. The work undertaken in this project represents the first comprehensive study to directly compare motor unit firing patterns to the electrical activity of the motor cortex as reflected by EEG recordings.
A Model of the Motoneuron Pool
Madeleine M. Lowery1 and Zeynep Erim
A model of the motoneuron pool and muscle force output was developed to allow experimentation on inputs to motorneurons which are otherwise inaccessible. The model was based on the first dorsal interosseous muscle and was comprised of 100 motoneurons. Individual motoneurons are simulated using a single compartment threshold crossing model. Motor unit twitch force is modeled as a critically damped, second-order system. Each motoneuron receives three inputs: a common activation current that dictated the level of activation of the muscle a common oscillatory input and an independent random membrane noise component. The model has been employed to characterize the behavior of two commonly used measures of correlated behavior: low-frequency common modulations (common drive) and synchronization of motor unit firings. (Read article (opens in new window)). Future applications of the model include hypothesis development for the effects of different paradigms such as aging, exercise or stroke on motor unit discharge behavior.
Peak of the magnitude squared coherence as a function of the frequency of the common oscillatory input, IM, at different amplitudes of IM (as a percentage of activation current IA). The average value of the peak coherence was estimated for all pair combinations of 11 simultaneously active motor units. The means and standard deviations of ten simulations are presented.
Coherence between Motor Unit Discharges in Response to Shared Neural Inputs
Madeleine M. Lowery1, Lance J. Myers2and Zeynep Erim
The observation of significant coherence between motor unit firing patterns has motivated increasing use of coherence analysis in hopes of a better understanding of the origin and/or functional significance of the coupling between motor units. While coherence analysis is well-described for linear systems, it is not clear how the known nonlinearities involved in the generation of motor unit firing patterns affect coherence estimation. The aim of this study is to examine how well motor unit coherence quantifies the characteristics of shared motoneuron inputs and to compare coherence and synchronization-based measures. Motor unit discharge patterns were generated using the above model of the motoneuron pool during the application of common oscillatory inputs across a range of frequencies and amplitudes. Analysis of the simulated data revealed that coherence provides information on the frequency content of shared neural inputs and appears less sensitive to motor unit firing rates than synchronization-based measures. On the other hand, synchronization may detect weak correlations between motor unit discharge times that can not be detected using coherence alone.
1 School of Electrical, Electronic & Mechanical Engineering, University College Dublin, Dublin, Ireland
2 VivoMetrics Inc., Ventura, California, USA
3 Bioengineering Unit, University of Strathclyde, Glasgow, Scotland