Motor Nerve Conduction Block Estimation in Demyelinating Neuropathies by Deconvolution

A deconvolution method is proposed for conduction block (CB) estimation based on two compound muscle action potentials (CMAPs) elicited by stimulating a nerve proximal and distal to the region in which the block is suspected. It estimates the time delay distributions by CMAPs deconvolution, from which CB is computed. The slow afterwave (SAW) is included to describe the motor unit potential, as it gives an important contribution in case of the large temporal dispersion (TD) often found in patients. The method is tested on experimental signals obtained from both healthy subjects and pathological patients, with either Chronic Inflammatory Demyelinating Polyneuropathy (CIDP) or Multifocal Motor Neuropathy (MMN). The new technique outperforms the clinical methods (based on amplitude and area of CMAPs) and a previous state-of-the-art deconvolution approach. It compensates phase cancellations, allowing to discriminate among CB and TD: estimated by the methods of amplitude, area and deconvolution, CB showed a correlation with TD equal to 39.3%, 29.5% and 8.2%, respectively. Moreover, a significant decrease of percentage reconstruction errors of the CMAPs with respect to the previous deconvolution approach is obtained (from a mean/median of 19.1%/16.7% to 11.7%/11.2%). Therefore, the new method is able to discriminate between CB and TD (overcoming the important limitation of clinical approaches) and can approximate patients’ CMAPs better than the previous deconvolution algorithm. Then, it appears to be promising for the diagnosis of demyelinating polyneuropathies, to be further tested in the future in a prospective clinical tria

A Novel Method for Characterization of Peripheral Nerve Fiber Size Distributions by Group Delay Measurements and Simulated Annealing Optimization

The ability to determine the characteristics of peripheral nerve fiber size distributions would provide additional information to clinicians for the diagnosis of specific pathologies of the peripheral nervous system. Investigation of these conditions, using electro-diagnostic techniques, is advantageous in the sense that such techniques tend to be minimally invasive yet provide valuable diagnostic information. One of the principal electro-diagnostic tools available to the clinician is the nerve conduction velocity test. While the peripheral nerve conduction velocity test can provide useful information to the clinician regarding the viability of the nerve under study, it is a single parameter test that yields no detailed information about the characteristics of the functioning nerve fibers within the nerve trunk. In this study we present a technique based on a decomposition of the maximal compound evoked potential and subsequent determination of the group delay of the contributing nerve fibers. The fiber group delay is then utilized as an initial estimation of the nerve fiber size distribution and the concomitant temporal propagation delays of the associated single fiber evoked potentials to a reference electrode. Subsequently the estimated single fiber evoked potentials are optimized against the template maximal compound evoked potential using a simulated annealing algorithm. Simulation studies, based on deterministic single fiber action potential functions, are used to demonstrate the robustness of the proposed technique in the presence of noise associated with variations in distance between the nerve fibers and the recording electrodes between the two recording sites

Motor unit discharges from multi-kernel deconvolution of single channel surface electromyogram

Surface electromyogram (EMG) finds many applications in the non-invasive characterization of muscles. Extracting information on the control of motor units (MU) is difficult when using single channels, e.g., due to the low selectivity and large phase cancellations of MU action potentials (MUAPs). In this paper, we propose a new method to face this problem in the case of a single differential channel. The signal is approximated as a sum of convolutions of different kernels (adapted to the signal) and firing patterns, whose sum is the estimation of the cumulative MU firings. Three simulators were used for testing: muscles of parallel fibres with either two innervation zones (IZs, thus, with MUAPs of different phases) or one IZ and a model with fibres inclined with respect to the skin. Simulations were prepared for different fat thicknesses, distributions of conduction velocity, maximal firing rates, synchronizations of MU discharges, and variability of the inter-spike interval. The performances were measured in terms of cross-correlations of the estimated and simulated cumulative MU firings in the range of 0–50 Hz and compared with those of a state-of-the-art single-kernel algorithm. The median cross-correlations for multi-kernel/single-kernel approaches were 92.2%/82.4%, 98.1%/97.6%, and 95.0%/91.0% for the models with two IZs, one IZ (parallel fibres), and inclined fibres, respectively (all statistically significant differences, which were larger when the MUAP shapes were of greater difference)

THE EFFECT OF FIBER DEPTH ON THE ESTIMATION OF PERIPHERAL NERVE FIBER DIAMETER USING GROUP DELAY AND SIMULATED ANNEALING OPTIMIZATION

Peripheral neuropathy refers to diseases of or injuries to the peripheral nerves in the human body. The damage can interfere with the vital connection between the central nervous system and other parts of the body, and can significantly reduce the quality of life of those affected. In the US, approximately between 15 and 20 million people over the age of 40 have some forms of peripheral neuropathy. The diagnosis of peripheral neuropathy often requires an invasive operation such as a biopsy because different forms of peripheral neuropathy can affect different types of nerve fibers. There are non-invasive methods available to diagnose peripheral neuropathy such as the nerve conduction velocity test (NCV). Although the NCV is useful to test the viability of an entire nerve trunk, it does not provide adequate information about the individual functioning nerve fibers in the nerve trunk to differentiate between the different forms of peripheral neuropathy. A novel technique was proposed to estimate the individual nerve fiber diameters using group delay and simulated annealing optimization. However, this technique assumed that the fiber depth is always constant at 1 mm and the fiber activation due to a stimulus is depth independent. This study aims to incorporate the effect of fiber depth into the fiber diameter estimation technique and to make the simulation more realistic, as well as to move a step closer to making this technique a viable diagnostic tool. From the simulation data, this study found that changing the assumption of the fiber depth significantly impacts the accuracy of the fiber diameter estimation. The results suggest that the accuracy of the fiber diameter estimation is dependent on whether the type of activation function is depth dependent or not, and whether the template fiber diameter distribution contains mostly large fibers or both small and large fibers, but not dependent on whether the fiber depth is constant or variable

NERVE FIBER DIAMETER MEASUREMENTS USING HEMATOXYLIN AND EOSIN STAINING AND BRIGHTFIELD MICROSCOPY TO ASSESS THE NOVEL METHOD OF CHARACTERIZING PERIPHERAL NERVE FIBER DISTRIBUTIONS BY GROUP DELAY

Peripheral neuropathies are a set of common diseases that affect the peripheral nervous system, causing damage to vital connections between various parts of the body and the brain and spinal cord. Different clinical conditions are known to selectively impact various size nerve fibers, which often makes it difficult to diagnose which peripheral neuropathy a patient might have. The nerve conduction velocity diagnostic test provides clinically useful information in the diagnosis of some peripheral neuropathies. This method is advantageous because it tends to be minimally invasive yet it provides valuable diagnostic information. However, this test does not determine characteristics of peripheral nerve fiber size distributions, and therefore does not show any detailed information regarding the nerve fibers within the nerve trunk. Being able to determine which nerve fibers are contributing to the evoked potential within a nerve trunk could provide additional information to clinicians for the diagnosis of specific pathologies of the peripheral nervous system, such as chronic inflammatory demyelinating polyneuropathy or early diabetic peripheral neuropathy. In this study, three rat sciatic nerves are sectioned and stained with hematoxylin and eosin in order to measure the nerve fiber diameters within the nerve trunk. Stained samples are viewed using brightfield microscopy and images are analyzed using ImageJ. Histograms were created to show the frequency of various nerve fiber diameters. The nerve fiber diameters measured during this research are consistent with the range of previously published diameter values and will be used to support continuing research for a novel method to characterize peripheral nerve fiber size distributions using group delay