Browsing by Subject "Microstimulation"

The loss of the ability to walk as the result of neurological injury or disease critically impacts the mobility and everyday lifestyle of millions. The World Heath Organization (WHO) estimates that approximately 1% of the world's population needs the use of a wheelchair to assist their personal mobility. Advances in the field of brain-machine interfaces (BMIs) have recently demonstrated the feasibility of using neuroprosthetics to extract motor information from cortical ensembles for more effective control of upper-limb replacements. However, the promise of BMIs has not yet been brought to bear on the challenge of restoring the ability to walk. A future neuroprosthesis designed to restore walking would need two streams of information flowing between the user's brain and the device. First, the motor control signals would have to be extracted from the brain, allowing the robotic prosthesis to behave in the manner intended by the user. Second, and equally important would be the flow of sensory and proprioceptive information back to the user from the neuroprosthesis. Here, I contribute to the foundation of such a bi-directional brain machine interface for the restoration of walking in a series of experiments in two animal models, designed to show the feasibility of (1) extracting locomotor information from neuronal ensemble activity and (2) sending information back into the brain via cortical microstimulation.

In a set of experiments designed to investigate the extraction of locomotor parameters, I chronically recorded from ensembles of neurons in primary motor (M1) and primary somatosensory (S1) cortices in two adult female rhesus macaques as they walked bipedally, at various speeds, both forward and backward on a custom treadmill. For these experiments, rhesus monkeys were suitable because of their ability to walk bipedally in a naturalistic manner with training. I demonstrate that the kinematics of bipedal walking in rhesus macaques can be extracted from neuronal ensemble recordings, both offline and in real-time. The activity of hundreds of neurons was processed by a series of linear decoders to extract accurate predictions of leg joints in three dimensional space, as well as leg muscle electromyograms (EMGs). Using a multi-layered switching model allowed us to achieve increased extraction accuracy by segregating different behavioral modes of walking.

In a second set of experiments designed to investigate the usage of microstimulation as a potential artificial sensory channel, I instructed two adult female Aotus trivirgatus (owl monkeys) about the location of a hidden food reward using a series of cortical microstimulation patterns delivered to primary somatosensory (S1) cortex. The owl monkeys discriminated these microstimulation patterns and used them to guide reaching movements to one of two targets. Here, owl monkeys were used which were previously implanted with electrode arrays of high longevity and stability. These monkeys were previously trained on a somatosensory cued task, which allowed a quick transition to microstimulation cueing. The owl monkeys learned to interpret microstimulation patterns, and their skill and speed of learning new patterns improved over several months. Additionally, neuronal activity recorded on non-stimulated electrodes in motor (M1), premotor (PMD) and posterior parietal (PP) cortices allowed us to examine the immediate neural responses to single biphasic stimulation pulses as well as overall responses to the spatiotemporal pattern. Using this recorded neuronal activity, I showed the efficacy of several linear classification algorithms during microstimulation.

These results demonstrate that locomotor kinematic parameters can be accurately decoded from the activity of neuronal ensembles, that multichannel microstimulation is a viable information channel for sensorized prosthetics, and that the technical limitations of combining these techniques can be overcome. I propose that bi-directional BMIs integrating these techniques will one day restore the ability to walk to severely paralyzed patients.

Brain Machine Interfaces (BMI) strive to restore motor and sensory functions lost due to paralysis, amputation, and neurological diseases by interfacing brain circuitry to external actuators in form of a cursor on a computer screen or a robotic limb. There is a strong clinical need for sensory restoration as lack of somatosensory feedback leads to loss of fine motor control and one of the most common preferences for improvements according to individuals with upper-limb loss is the ability to require less visual attention to perform certain functions and to have a better control of wrist movement. One way to restore sensory functions is using electrical microstimulation of brain sensory areas as an artificial sensory channel; however, the ways of creating such artificial sensory inputs are poorly understood.

This dissertation presents the use of intracortical microstimulation (ICMS) to the primary somatosensory cortex (S1) to guide exploratory arm movements without visual feedback. Two rhesus monkeys were chronically implanted with multielectrode arrays in S1 and primary motor cortex (M1). The monkeys used a hand-held joystick to reach targets with a cursor on a computer screen. ICMS patterns were delivered to S1 when the cursor was placed over the target, mimicking the sense of touch. After the target or the cursor was made invisible, monkeys relied on ICMS feedback instead of vision to perform the task. For an invisible cursor, a random offset was added to the position of the invisible cursor to rule out the possibility that monkeys relied on joystick position felt through proprioception. Learning to perform these tasks was accompanied by changes in both the parameters of arm movements and representation of those parameters by M1 and S1 neurons at a population and individual neuronal levels.

Offline decoding of single neurons and population of neurons showed that overlapping, but not identical subpopulations of neurons represented movements when ICMS provided feedback instead of vision.

These results suggest that ICMS could be used as an essential source of sensation from prosthetic limbs.

Brain-machine interfaces (BMIs) use recordings from the nervous system to extract volitional and motor parameters for controlling external actuators, such as prosthetics, thereby bypassing or replacing injured tissue. As such, they show enormous promise for restoring mobility, dexterity, or communication in paralyzed patients or amputees. Recent advancements to the BMI paradigm have made the brain -- machine communication channel bidirectional, enabling the prosthetic to inform the user about touch, temperature, strain, or other sensory information; these devices are hence called brain-machine-brain interfaces (BMBIs).

In the first chapter an intraoperative BMI is investigated in human patients undergoing surgery for implantation of a deep brain stimulation (DBS) treatment electrodes. While the BMI was marginally effective, we found high levels of behavioral and tremor tuning among cells recorded from the surgical targets, the subthalamic nucleus (STN) and ventral intermediate nucleus (VIM) of the thalamus. Notably, this tremor or behavior tuning was not mutually exclusive with oscillatory behavior, suggesting that physiological tuning persists even in the face of pathological oscillations. We then used nonlinear means for extracting tremor tuning, and found a significant population, consistent with double-frequency or co-modulation to tremor within the basal ganglia. Synchrony was then assessed over long and short timescales between pairs of neurons, and it was found that tremor tuning implies synchrony: all units exhibiting tremor tuning showed synchrony to at least one other unit.

BMBIs rely on a host of both scientific knowledge and technology for effective function, and this technology is currently in intensive research. In this dissertation two technologies for BMBIs, corresponding to the two directions of communication, are designed, described, and tested. The first one is a high compliance, digitally controlled, high-side current-regulated microstimulator for intracortical microstimulation (ICMS). The device is validated on the bench, tested in monkeys, and used for multiple experimental setups. Due to careful control of parasitic charge injection, the microstimulator is ideally suited for interleaving stimulation and recording as employed in some BMBIs.

The second technology described is a wireless, scalable, 128 channel neural recording system. The device features aggressive digital filtering to maximize signal quality, has spike sorting and compression on the transceiver, can be fully configured over the air through a custom wireless bridge and client software, and can run for over 30 hours on one battery. This system has been tested in a monkey while in its home cage, where the wireless system permitted unfettered, continuous recording and continuous access to a simplified BMI. A full description of the development and device is described, as well as results showing convincing 1D and suggestive 2D BMI control.