For decades, neurophysiologists have worked on elucidating the function of the cortical sensorimotor control system from the standpoint of kinematics or dynamics. Recently, computational neuroscientists have developed models that can emulate changes seen in the primary motor cortex during learning. However, these simulations rely on the existence of a reward-like signal in the primary sensorimotor cortex. Reward modulation of the primary sensorimotor cortex has yet to be characterized at the level of neural units. Here we demonstrate that single units/multiunits and local field potentials in the primary motor (M1) cortex of nonhuman primates (Macaca radiata) are modulated by reward expectation during reaching movements and that this modulation is present even while subjects passively view cursor motions that are predictive of either reward or nonreward. After establishing this reward modulation, we set out to determine whether we could correctly classify rewarding versus nonrewarding trials, on a moment-to-moment basis. This reward information could then be used in collaboration with reinforcement learning principles toward an autonomous brain-machine interface. The autonomous brain-machine interface would use M1 for both decoding movement intention and extraction of reward expectation information as evaluative feedback, which would then update the decoding algorithm as necessary. In the work presented here, we show that this, in theory, is possible.

We are developing an autonomously updating brain machine interface (BMI) utilizing reinforcement learning principles. One aspect of this system is a neural critic that determines reward expectations from neural activity. This critic is then used to update a BMI decoder towards an improved performance from the user's perspective. Here we demonstrate the ability of a neural critic to classify trial reward value given activity from the primary motor cortex (M1), using neural features from single/multi units (SU/MU), and local field potentials (LFPs) with prediction accuracies up to 97% correct. A nonhuman primate subject conducted a cued center out reaching task, either manually, or observationally. The cue indicated the reward value of a trial. Features such as power spectral density (PSD) of the LFPs and spike-field coherence (SFC) between SU/MU and corresponding LFPs were calculated and used as inputs to several classifiers. We conclude that hybrid features of PSD and SFC show higher classification performance than PSD or SFC alone (accuracy was 92% for manual tasks, and 97% for observational). In the future, we will employ these hybrid features towards our autonomously updating BMI.

Objective: To report the use of multi-frequency intra-cochlear electrocochleography (ECOG) in monitoring and optimizing electrode placement during cochlear implant surgery. An acoustic pure tone complex comprising of 250, 500, 1000, and 2000 Hz was used to elicit ECOG, or more specifically cochlear microphonics (CMs), responses from various locations in the cochlea. The most apical cochlear implant electrode was used as the recording electrode. Study Design: Clinical capsule report. Setting: Tertiary academic referral center. Results: ECOG measurements were performed during cochlear implant surgery in an adult patient with significant residual acoustic hearing. The 500, 1000, and 2000 Hz CM tracings from the most apical electrode showed an amplitude peak at three different instances during the early phase of cochlear implant electrode insertion. These results are consistent with the tonotopic organization of the cochlea. During final electrode placement a slight advancement of the electrode array resulted in a correlated decrease in 250, 500, and/or 1000 Hz CM amplitude. The electrode array was retracted and repositioned which resulted in a recovery of CM amplitude. Intraoperative CM thresholds revealed a correlation of r = 0.87 with preoperative audiometric thresholds. Conclusion: We present a report on simultaneous multi-frequency ECOG monitoring during cochlear implant surgery. Multi-frequency ECOG can be used to differentiate between electrode trauma and the advancement of the apical electrode beyond the CM source in the cochlea.