Browsing by Subject "Neuroprosthetics"

Brain-machine interfaces (BMIs) offer the potential to assist millions of people worldwide suffering from immobility due to loss of limbs, paralysis, and neurodegenerative diseases. BMIs function by decoding neural activity from intact cortical brain regions in order to control external devices in real-time. While there has been exciting progress in the field over the past 15 years, the vast majority of the work has focused on restoring of motor function of a single limb. In the work presented in this thesis, I first investigate the expanded role of primary sensory (S1) and motor (M1) cortex during reaching movements. By varying target size during reaching movements, I discovered the cortical correlates of the speed-accuracy tradeoff known as Fitts' law. Similarly, I analyzed cortical motor processing during tasks where the motor plan is quickly reprogrammed. In each study, I found that parameters relevant to the reach, such as target size or alternative movement plans, could be extracted by neural decoders in addition to simple kinematic parameters such as velocity and position. As such, future BMI functionality could expand to account for relevant sensory information and reliably decode intended reach trajectories, even amidst transiently considered alternatives.

The second portion of my thesis work was the successful development of the first bimanual brain-machine interface. To reach this goal, I expanded the neural recordings system to enable bilateral, multi-site recordings from approximately 500 neurons simultaneously. In addition, I upgraded the experiment to feature a realistic virtual reality end effector, customized primate chair, and eye tracking system. Thirdly, I modified the tuning function of the unscented Kalman filter (UKF) to conjointly represent both arms in a single 4D model. As a result of widespread cortical plasticity in M1, S1, supplementary motor area (SMA), and posterior parietal cortex (PPC), the bimanual BMI enabled rhesus monkeys to simultaneously control two virtual limbs without any movement of their own body. I demonstrate the efficacy of the bimanual BMI in both a subject with prior task training using joysticks and a subject naïve to the task altogether, which simulates a common clinical scenario. The neural decoding algorithm was selected as a result of a methodical comparison between various neural decoders and decoder settings. I lastly introduce a two-stage switching model with a classify step and predict step which was designed and tested to generalize decoding strategies to include both unimanual and bimanual movements.

Sensory neuroprosthetics offer a revolutionary approach to studying as well as treating patients suffering from sensory dysfunction resulting from neurological impairments. Devices, such as cochlear implants, which restore the functionality of defective peripheral sensory organs, have become increasingly more prevalent and provide greater autonomy and independence to patients. For those with damage to the sensory neural circuits themselves as a result of disease or injury, alternative treatment options must be implemented. Cortical prostheses that bypass the damaged circuitry and deliver sensory information directly to the brain offer an alternative option for these patients. This approach could be used to provide tactile sensation for a prosthetic limb, restore a sense of sight in those with cortical visual impairment, or recruit intact cortex to take on the lost functionality of damaged regions of the brain. Importantly, developing devices that best serve these patient populations requires deepening our understanding of the mechanisms underlying the brain’s ability to incorporate information from a sensory prosthesis. Much of the current literature, however, focuses on behavioral and perceptual endpoints rather than changes in the brain at the mesoscopic level.

To that end, this dissertation aims to address that gap by characterizing the emergence of distributed representations of artificial sensation following the use of a cortical sensory prosthesis. Prior research has shown that adult rats could use a microstimulation-based sensory neuroprosthesis that projected information about the infrared (IR) environment onto the barrel fields of primary somatosensory cortex (S1). Equipped with this prosthesis, rats quickly learned to perform a four-choice IR discrimination task with proficiency comparable to that attained in an analogous visual discrimination task. This research established a useful paradigm for studying how the brain adapts to incorporate new sensory information projected directly onto cortex. The original research presented in this dissertation thus utilizes this paradigm for investigating how brain regions distal to the site of stimulation represent the stimulation patterns delivered by the prosthesis.

For this dissertation, I first discuss the response of two areas directly coupled to S1: the ventral posteromedial nucleus of the thalamus (VPM) - the main input nucleus to S1 and recipient of extensive corticothalamic feedback from S1 - and the posteromedial nucleus of the thalamus (POm) - a modulatory nucleus in the paralemniscal whisker pathway. Specifically, I quantify the stimulation induced response in S1, VPM, and POm. Using recordings from hundreds of multi-units from each region, the proportion of units found to have post-stimulus responses statistically distinguishable from their corresponding baseline activities was 97%, 97%, and 99% for POm, VPM, and S1, respectively. This indicates that the region of the brain affected by electrical stimulation is not constrained to the site of stimulation, but in fact downstream correlates interconnected to the stimulated region of cortex show significant responses as well.

Next I compare the presence of IR receptive field maps and the relative distribution of preferred stimulus orientations. Previously, it has been demonstrated that S1 units develop preferred stimulation patterns. That is, individual units showed variable firing rates depending on the direction to the IR source. This work replicated that finding, but more importantly I found that emergent IR receptive field maps are found in VPM and POm as well. This shows that not only do the thalamic units respond to ICMS, but undergo experience-dependent plasticity that allows the thalamic nuclei to encode the stimulus and participate in the sensory processing of the artificial sensation. A mutual information analysis was preformed to quantify the degree to which these subcortical regions represent the pattern of stimulation delivered to the cortex. The proportion of units found to have significant mutual information values was 57%, 74%, and 69% for POm, VPM, and S1, respectively. These results indicate that the artificial sensory information is readily encoded in native sensory processing circuits. Furthermore it suggests that the cortex can impose significant influence over the receptive field characteristics of thalamic nuclei even in the adult rodent brain.

Finally, I discuss the implementation of graph convolutional neural network (GCN) models to decode the stimulus features from the neural activity recorded during prosthetic use. The best performing GCN model was able to achieve a peak classification performance of 73.5% on a modified ordinal regression performance metric. Additionally, by allowing the model to learn the adjacency matrix for the neural graph data, the adjacency matrix inferred was found to provide a better representation of the underlying neural circuitry encoding the artificial sensation compared to standard techniques (i.e. cross correlation and mutual information). This further demonstrated the observation that thalamic units participated in the processing of the new sense. Because the adjacency matrix derived from training the GCNs reflects the nodes that best improve the predictions of the stimulation patterns, the adjacency matrix also serves as a method of deriving connectivity measures for the recorded units. The interpretation of these results represents a novel approach to determining functional interactions and the effective circuits involved in processing a new sensory modality.

In recent years, large-scale brain recordings in nonhuman primates have been a driving force for both fundamental neuroscience and the field of brain-machine interfaces (BMIs). This required monkey implants connected to external amplifiers and computers with ever increasing number of cables. As shown with our recent demonstration of 2,000 neurons recorded in one monkey, a tethered recording system begins to get bulky and complex, particularly for our BMI and neurophysiological research. To address this problem, we developed a multichannel wireless recording framework. The system was been tested in freely moving rhesus monkey by integrating wireless neural recordings with external computers performing BMI decoding, behavioral manipulanda and optical tracking. This technology can be applied to primate behavior research and, in the near future, wireless, fully implantable human neuroprosthetics, which is of great significance to those suffering from locomotor deficiencies, such as those brought on by spinal cord injury and stroke. Aided with these advances, I was able to study monkeys in unrestrained locomotion while their cortical activity was continuously monitored. I also explored unrestrained behaviors and how they showed distinct transitions in neural dynamics as monkeys engaged in different behavioral activities or learned new motor skills, such as bipedal walking. I was able to decode them many of these behavioral states from cortical activity with neural classifiers. Lastly, monkeys were able to perform BMI tasks continuously for many hours, allowing us to prove the relevance of unrestrained noise in BMI performance. Lastly, I present my role in developing two brain actuated movement platforms, a robotic exoskeleton under the guise of the WalkAgain project, and a microelectrode BMI enabled wheelchair. This body of work should assist those on the path to the next generation of clinical neuroprostheses and neural communication systems.