Timothy L. Hanson

Chronic, wireless recordings of large-scale brain activity in freely moving rhesus monkeys

Advances in techniques for recording large-scale brain activity contribute to both the elucidation of neurophysiological principles and the development of brain-machine interfaces (BMIs). Here we describe a neurophysiological paradigm for performing tethered and wireless large-scale recordings based on movable volumetric three-dimensional (3D) multielectrode implants. This approach allowed us to isolate up to 1,800 neurons (units) per animal and simultaneously record the extracellular activity of close to 500 cortical neurons, distributed across multiple cortical areas, in freely behaving rhesus monkeys. The method is expandable, in principle, to thousands of simultaneously recorded channels. It also allows increased recording longevity (5 consecutive years) and recording of a broad range of behaviors, such as social interactions, and BMI paradigms in freely moving primates. We propose that wireless large-scale recordings could have a profound impact on basic primate neurophysiology research while providing a framework for the development and testing of clinically relevant neuroprostheses.

A brain-machine interface instructed by direct intracortical microstimulation

Brain–machine interfaces (BMIs) establish direct communication between the brain and artificial actuators. As such, they hold considerable promise for restoring mobility and communication in patients suffering from severe body paralysis. To achieve this end, future BMIs must also provide a means for delivering sensory signals from the actuators back to the brain. Prosthetic sensation is needed so that neuroprostheses can be better perceived and controlled. Here we show that a direct intracortical input can be added to a BMI to instruct rhesus monkeys in choosing the direction of reaching movements generated by the BMI. Somatosensory instructions were provided to two monkeys operating the BMI using either: (a) vibrotactile stimulation of the monkeys hands or (b) multi-channel intracortical microstimulation (ICMS) delivered to the primary somatosensory cortex (S1) in one monkey and posterior parietal cortex (PP) in the other. Stimulus delivery was contingent on the position of the computer cursor: the monkey placed it in the center of the screen to receive machine–brain recursive input. After 2 weeks of training, the same level of proficiency in utilizing somatosensory information was achieved with ICMS of S1 as with the stimulus delivered to the hand skin. ICMS of PP was not effective. These results indicate that direct, bi-directional communication between the brain and neuroprosthetic devices can be achieved through the combination of chronic multi-electrode recording and microstimulation of S1. We propose that in the future, bidirectional BMIs incorporating ICMS may become an effective paradigm for sensorizing neuroprosthetic devices.

Primate Reaching Cued by Multichannel Spatiotemporal Cortical Microstimulation

Both humans and animals can discriminate signals delivered to sensory areas of their brains using electrical microstimulation. This opens the possibility of creating an artificial sensory channel that could be implemented in neuroprosthetic devices. Although microstimulation delivered through multiple implanted electrodes could be beneficial for this purpose, appropriate microstimulation protocols have not been developed. Here, we report a series of experiments in which owl monkeys performed reaching movements guided by spatiotemporal patterns of cortical microstimulation delivered to primary somatosensory cortex through chronically implanted multielectrode arrays. The monkeys learned to discriminate microstimulation patterns, and their ability to learn new patterns and new behavioral rules improved during several months of testing. Significantly, information was conveyed to the brain through the interplay of microstimulation patterns delivered to multiple electrodes and the temporal order in which these electrodes were stimulated. This suggests multichannel microstimulation as a viable means of sensorizing neural prostheses.

Unscented Kalman Filter for Brain-Machine Interfaces

Brain machine interfaces (BMIs) are devices that convert neural signals into commands to directly control artificial actuators, such as limb prostheses. Previous real-time methods applied to decoding behavioral commands from the activity of populations of neurons have generally relied upon linear models of neural tuning and were limited in the way they used the abundant statistical information contained in the movement profiles of motor tasks. Here, we propose an n-th order unscented Kalman filter which implements two key features: (1) use of a non-linear (quadratic) model of neural tuning which describes neural activity significantly better than commonly-used linear tuning models, and (2) augmentation of the movement state variables with a history of n-1 recent states, which improves prediction of the desired command even before incorporating neural activity information and allows the tuning model to capture relationships between neural activity and movement at multiple time offsets simultaneously. This new filter was tested in BMI experiments in which rhesus monkeys used their cortical activity, recorded through chronically implanted multielectrode arrays, to directly control computer cursors. The 10th order unscented Kalman filter outperformed the standard Kalman filter and the Wiener filter in both off-line reconstruction of movement trajectories and real-time, closed-loop BMI operation.

Future developments in brain-machine interface research

Neuroprosthetic devices based on brain-machine interface technology hold promise for the restoration of body mobility in patients suffering from devastating motor deficits caused by brain injury, neurologic diseases and limb loss. During the last decade, considerable progress has been achieved in this multidisciplinary research, mainly in the brain-machine interface that enacts upper-limb functionality. However, a considerable number of problems need to be resolved before fully functional limb neuroprostheses can be built. To move towards developing neuroprosthetic devices for humans, brain-machine interface research has to address a number of issues related to improving the quality of neuronal recordings, achieving stable, long-term performance, and extending the brain-machine interface approach to a broad range of motor and sensory functions. Here, we review the future steps that are part of the strategic plan of the Duke University Center for Neuroengineering, and its partners, the Brazilian National Institute of Brain-Machine Interfaces and the École Polytechnique Fédérale de Lausanne (EPFL) Center for Neuroprosthetics, to bring this new technology to clinical fruition.

The Muscle Activation Method: An Approach to Impedance Control of Brain-Machine Interfaces Through a Musculoskeletal Model of the Arm

Current demonstrations of brain-machine interfaces (BMIs) have shown the potential for controlling neuroprostheses under pure motion control. For interaction with objects, however, pure motion control lacks the information required for versatile manipulation. This paper investigates the idea of applying impedance control in a BMI system. An extraction algorithm incorporating a musculoskeletal arm model was developed for this purpose. The new algorithm, called the muscle activation method (MAM), was tested on cortical recordings from a behaving monkey. The MAM was found to predict motion parameters with as much accuracy as a linear filter. Furthermore, it successfully predicted limb interactions with novel force fields, which is a new and significant capability lacking in other algorithms.

A Large-Scale Interface for Optogenetic Stimulation and Recording in Nonhuman Primates

While optogenetics offers great potential for linking brain function and behavior in nonhuman primates, taking full advantage of that potential will require stable access for optical stimulation and concurrent monitoring of neural activity. Here we present a practical, stable interface for stimulation and recording of large-scale cortical circuits. To obtain optogenetic expression across a broad region, here spanning primary somatosensory (S1) and motor (M1) cortices, we used convection-enhanced delivery of the viral vector, with online guidance from MRI. To record neural activity across this region, we used a custom micro-electrocorticographic (μECoG) array designed to minimally attenuate optical stimuli. Lastly, we demonstrated the use of this interface to measure spatiotemporal responses to optical stimulation across M1 and S1. This interface offers a powerful tool for studying circuit dynamics and connectivity across cortical areas, for long-term studies of neuromodulation and targeted cortical plasticity, and for linking these to behavior.

Subcortical Neuronal Ensembles: An Analysis of Motor Task Association, Tremor, Oscillations, and Synchrony in Human Patients

Deep brain stimulation (DBS) has expanded as an effective treatment for motor disorders, providing a valuable opportunity for intraoperative recording of the spiking activity of subcortical neurons. The properties of these neurons and their potential utility in neuroprosthetic applications are not completely understood. During DBS surgeries in 25 human patients with either essential tremor or Parkinsons disease, we acutely recorded the single-unit activity of 274 ventral intermediate/ventral oralis posterior motor thalamus (Vim/Vop) neurons and 123 subthalamic nucleus (STN) neurons. These subcortical neuronal ensembles (up to 23 neurons sampled simultaneously) were recorded while the patients performed a target-tracking motor task using a cursor controlled by a haptic glove. We observed that modulations in firing rate of a substantial number of neurons in both Vim/Vop and STN represented target onset, movement onset/direction, and hand tremor. Neurons in both areas exhibited rhythmic oscillations and pairwise synchrony. Notably, all tremor-associated neurons exhibited synchrony within the ensemble. The data further indicate that oscillatory (likely pathological) neurons and behaviorally tuned neurons are not distinct but rather form overlapping sets. Whereas previous studies have reported a linear relationship between power spectra of neuronal oscillations and hand tremor, we report a nonlinear relationship suggestive of complex encoding schemes. Even in the presence of this pathological activity, linear models were able to extract motor parameters from ensemble discharges. Based on these findings, we propose that chronic multielectrode recordings from Vim/Vop and STN could prove useful for further studying, monitoring, and even treating motor disorders.

High-Side Digitally Current Controlled Biphasic Bipolar Microstimulator

Electrical stimulation of nervous tissue has been extensively used as both a tool in experimental neuroscience research and as a method for restoring of neural functions in patients suffering from sensory and motor disabilities. In the central nervous system, intracortical microstimulation (ICMS) has been shown to be an effective method for inducing or biasing perception, including visual and tactile sensation. ICMS also holds promise for enabling brain-machine-brain interfaces (BMBIs) by directly writing information into the brain. Here we detail the design of a high-side, digitally current-controlled biphasic, bipolar microstimulator, and describe the validation of the device in vivo. As many applications of this technique, including BMBIs, require recording as well as stimulation, we pay careful attention to isolation of the stimulus channels and parasitic current injection. With the realized device and standard recording hardware - without active artifact rejection - we are able to observe stimulus artifacts of less than 2 ms in duration.