Blind source separation of nerve cuff recordings

Fascicular perineurium thickness, size, and position affect model predictions of neural excitation

The number of applications using neural prosthetic interfaces is expanding. Computer models are a valuable tool to evaluate stimulation techniques and electrode designs. Although our understanding of neural anatomy has improved, its impact on the effects of neural stimulation is not well understood. This study evaluated the effects of fascicle perineurial thickness, diameter, and position on axonal excitation thresholds and population recruitment using finite element models and NEURON simulations. The perineurial thickness of human fascicles was found to be 3.0% +/- 1.0% of the fascicle diameter. Increased perineurial thickness and fascicle diameter increased activation thresholds. The presence of a large neighboring fascicle caused a significant change in activation of a smaller target fascicle by as much as 80% +/- 11% of the total axon population. Smaller fascicles were recruited at lower amplitudes than neighboring larger fascicles. These effects were further illustrated in a realistic model of a human femoral nerve surrounded by a nerve cuff electrode. The data suggest that fascicular selectivity is strongly dependent upon the anatomy of the nerve being stimulated. Therefore, accurate representations of nerve anatomy are required to develop more accurate computer models to evaluate and optimize nerve electrode designs for neural prosthesis applications.

Solution space reduction in the peripheral nerve source localization problem using forward field similarities

Improving our ability to localize bioelectric sources within a peripheral nerve would help us to monitor the control signals flowing to and from any limb or organ. This technology would provide a useful neuroscience tool, and could perhaps be incorporated into a neuroprosthesis interface. We propose to use measurements from a multi-contact nerve cuff to solve an inverse problem of bioelectric source localization within the peripheral nerve. Before the inverse problem can be addressed, the forward problem is solved using finite element modeling. A fine mesh improves the accuracy of the forward problem solution, but increases the number of variables to be solved for in the inverse problem. To alleviate this problem, variables corresponding to mesh elements that are not distinguishable by the measurement setup are grouped together, thus reducing the dimension of the inverse problem without impacting on the forward problem accuracy. A quantitative criterion for element distinguishability is derived using the columns of the leadfield matrix and information about the uncertainty in the measurements. Our results indicate that the number of variables in the inverse problem can be reduced by more than half using the proposed method, without having a detrimental impact on the quality of the localization.