Optimizing selective stimulation of peripheral nerves with arrays of coils or surface electrodes using a linear peripheral nerve stimulation metric

摘要

Objective. We present a PNS oracle, which solves these computation time and linearity problems andis, therefore, well-suited for fast optimization of voltage distributions in contact electrode arraysand current drive patterns in non-contact magnetic coil arrays. Approach. The PNS oracle metricfor a nerve fiber is computed from an electric field map using only linear operations (projection,differentiation, convolution, scaling). Due to its linearity, this PNS metric can be precomputed for aset of coil or electrode segments, allowing rapid PNS prediction and comparison of any possible coilor electrode stimulation configuration constructed from this set. The PNS oracle is closely relatedto the classical activating function and modified driving functions but is adjusted to better correlatewith full neurodynamic modeling of myelinated mammalian nerves. Main results. We validated thePNS oracle in three MRI gradient coils and two body models and found good correlation between thePNS oracle and the full neurodynamic modeling approach (R~2 > 0.995). Finally, we demonstratedits potential utility by optimizing the driving currents and voltages of arrays of 108 magnetic coils or108 contact electrodes to selectively stimulate target nerves in the lower leg. Significance. Peripheralnerve stimulation (PNS) by electromagnetic fields can be accurately simulated using coupledelectromagnetic and neurodynamic modeling. Such simulations are slow and non-linear in theelectric field, which makes it difficult to iteratively optimize coil and electrode configurations or drivepatterns aiming to avoid PNS or to initiate it for therapeutic purposes.