Cortical Control of Virtual Self-Motion Using Task-Specific Subspaces.

Brain-machine interfaces (BMIs) for reaching have enjoyed continued performance improvements, yet there remains significant need for BMIs that control other movement classes. Recent scientific findings suggest that the intrinsic covariance structure of neural activity depends strongly on movement class, potentially necessitating different decode algorithms across classes. To address this possibility, we developed a self-motion BMI based on cortical activity as monkeys cycled a hand-held pedal to progress along a virtual track.

Neural Dynamics in Primate Cortex during Exposure to Subanesthetic Concentrations of Nitrous Oxide.

Nitrous oxide (NO) is a hypnotic gas with antidepressant and psychedelic properties at subanesthetic concentrations. Despite long-standing clinical use, there is insufficient understanding of its effect on neural dynamics and cortical processing, which is important for mechanistic understanding of its therapeutic effects. We administered subanesthetic (70%), inhaled NO and studied the dynamic changes of spiking rate, spectral content, and somatosensory information representation in primary motor cortex (M1) in two male rhesus macaques implanted with Utah microelectrode arrays in the hand area of M1.

A simple, inexpensive method for subcortical stereotactic targeting in nonhuman primates.

Background: Many current neuroscience studies in large animal models have focused on recordings from cortical structures. While sufficient for analyzing sensorimotor systems, many processes are modulated by subcortical nuclei. Large animal models, such as nonhuman primates (NHP), provide an optimal model for studying these circuits, but the ability to target subcortical structures has been hampered by lack of a straightforward approach to targeting.

Robust tactile sensory responses in finger area of primate motor cortex relevant to prosthetic control.

Objective: Challenges in improving the performance of dexterous upper-limb brain-machine interfaces (BMIs) have prompted renewed interest in quantifying the amount and type of sensory information naturally encoded in the primary motor cortex (M1). Previous single unit studies in monkeys showed M1 is responsive to tactile stimulation, as well as passive and active movement of the limbs. However, recent work in this area has focused primarily on proprioception.

Intracortical Brain-Machine Interfaces Advance Sensorimotor Neuroscience.

Brain-machine interfaces (BMIs) decode brain activity to control external devices. Over the past two decades, the BMI community has grown tremendously and reached some impressive milestones, including the first human clinical trials using chronically implanted intracortical electrodes. It has also contributed experimental paradigms and important findings to basic neuroscience.

Disruption of corticocortical information transfer during ketamine anesthesia in the primate brain.

The neural mechanisms of anesthetic-induced unconsciousness have yet to be fully elucidated, in part because of the diverse molecular targets of anesthetic agents. We demonstrate, using intracortical recordings in macaque monkeys, that information transfer between structurally connected cortical regions is disrupted during ketamine anesthesia, despite preserved primary sensory representation. Furthermore, transfer entropy, an information-theoretic measure of directed connectivity, decreases significantly between neuronal units in the anesthetized state.

Data-driven model comparing the effects of glial scarring and interface interactions on chronic neural recordings in non-human primates.

Objective: We characterized electrode stability over twelve weeks of impedance and neural recording data from four chronically-implanted Utah arrays in two rhesus macaques, and investigated the effects of glial scarring and interface interactions at the electrode recording site on signal quality using a computational model.

Approach: A finite-element model of a Utah array microelectrode in neural tissue was coupled with a multi-compartmental model of a neuron to quantify the effects of encapsulation thickness, encapsulation resistivity, and interface resistivity on electrode impedance and waveform amplitude. The coupled model was then reconciled with the in vivo data.

Enabling Low-Power, Multi-Modal Neural Interfaces Through a Common, Low-Bandwidth Feature Space.

Brain-Machine Interfaces (BMIs) have shown great potential for generating prosthetic control signals. Translating BMIs into the clinic requires fully implantable, wireless systems; however, current solutions have high power requirements which limit their usability. Lowering this power consumption typically limits the system to a single neural modality, or signal type, and thus to a relatively small clinical market.