Characterizing the short-latency evoked response to intracortical microstimulation across a multi-electrode array

Abstract

ObjectivePersons with tetraplegia can use brain-machine interfaces to make visually guided reaches with robotic arms. Without somatosensory feedback, these movements will likely be slow and imprecise, like those of persons who retain movement but have lost proprioception. Intracortical microstimulation (ICMS) has promise for providing artificial somatosensory feedback. If ICMS can mimic naturally occurring neural activity, afferent interfaces may be more informative and easier to learn than interfaces that evoke unnaturalistic activity. To develop such biomimetic stimulation patterns, it is important to characterize the responses of neurons to ICMS. ApproachUsing a Utah multi-electrode array, we recorded activity evoked by single pulses, and short (~0.2 s) and long (~4 s) trains of ICMS at a wide range of amplitudes and frequencies. As the electrical artifact caused by ICMS typically prevents recording for many milliseconds, we deployed a custom rapid-recovery amplifier with nonlinear gain to limit signal saturation on the stimulated electrode. Across all electrodes after stimulation, we removed the remaining slow return to baseline with acausal high-pass filtering of time-reversed recordings. With these techniques, we could record ~0.7 ms after stimulation offset even on the stimulated electrode. Main resultsWe recorded likely transsynaptically-evoked activity as early as ~0.7 ms after single pulses of stimulation that was immediately followed by suppressed neural activity lasting 10-150 ms. Instead of this long-lasting inhibition, neurons increased their firing rates for ~100 ms after trains. During long trains, the evoked response on the stimulated electrode decayed rapidly while the response was maintained on non-stimulated channels. SignificanceThe detailed description of the spatial and temporal response to ICMS can be used to better interpret results from experiments that probe circuit connectivity or function of cortical areas. These results can also contribute to the design of stimulation patterns to improve afferent interfaces for artificial sensory feedback.