Evaluating the motor unit number index (MUNIX) as a measure for motor unit loss

AIM: With the increased number of potential therapeutic agents, monitoring of motor neuron loss in patients is of great importance. Functional measures are masked by motor neuron loss because of collateral reinnervation. A more direct measure is required. In this study, we evaluate a recent technique called MUNIX to determine motor neuron loss. METHODS: A model was constructed to study the effect of denervation and collateral reinnervation on surface motor unit potentials (MUPs). A small muscle containing 200 motor units was simulated. The process of motor neuron denervation is simulated by removing one motor neuron at a time leaving all its fibres orphaned. As muscle fibre loss after denervation is counteracted by reinnervation, the orphaned fibres could be reinnervated by a motor unit (MU) with a fibre adjacent to this fibre. MUNIX is calculated from the surface interference pattern (SIP) together with the maximal compound muscle action potential (CMAP) as described in by Nandedkar et al. (2003). Briefly, SIP area and SIP power are calculated for five levels of contractions. The motor unit count (ICMUC) is defined as ICMUC=(CMAPpowerSIParea)/(CMAPareaSIPpower). The ICMUC provides the real number of MUs if all MUs would be equal in size and no phase cancellation would occur. Since this is not the case, the relation between ICMUC and SIP area is determined as ICMUC=B*SIParea)^alpha, where B and alpha are determined by means of nonlinear regression and MUNIX is calculated as MUNIX=B(20)^alpha (inset figure 1). To test how well this method can follow motor neuron loss, we used the above denervation model and a MU firing pattern generated based on the model of Matthews et al. (1996). The size principle was applied creating the surface interference patterns, so that small MUs (i.e. small number of fibres) are recruited before and had a lower firing rate than large MUs. RESULTS: The MUNIX and CMAP results were evaluated in steps of 5% MU loss. We used different firing patterns and slightly different contraction levels (i.e. number of MUs) per run to obtain a measure of variability. Figure 1 shows that the MUNIX (blue) does not significantly decline until at least 40% of all MUs are lost and seems strongly related to the change in the CMAP amplitude (red). CONCLUSION: This preliminary simulation study shows that MUNIX follows motor neuron loss only slightly better than CMAP amplitude. The results after severe denervation should be interpreted with care as it is likely that the model is incorrect at this level.