Krishna V. Shenoy, Ph.D. - US grants

computational neuroscience; psychophysics; health science research support; vision; postgraduate education; smooth pursuit eye movement; vestibular apparatus; head movements; Primates;

0423076
Smith
The goal of this project is the design of a novel implantable brain imaging device called an IOS sensor. Based on a microscale integrated array of intermingled GaAs NIR emitters and detectors, this device will be optimized to image the intrinsic optical signal (IOS), a diffuse optical reflectance correlate of brain electrical activity rich in detail about sensory and motor information processing in mammalian cortex. The neurobiological utility of IOS imaging is already very well established, but all previous IOS imaging has used bulky, benchtop-scale instruments that require subjects to be immobilized and, almost always, anesthetized. Because of its small size, the IOS sensor will allow unprecedented imaging of cortical activity patterns in unanaesthetized and freely behaving subjects. Applications of the implantable IOS sensor will include neuroscience research, prosthetics for neurological injury patients, and drug discovery.

DESCRIPTION (provided by applicant): Spiking activity from neurophysiological experiments often exhibits dynamics beyond that driven by external stimulation, presumably reflecting the extensive recurrence of neural circuitry. Characterizing these dynamics may reveal important features of neural computation, particularly during internally-driven "cognitive" operations. For example, neurons in premotor cortex (PMd) are active during a "planning" period separating movement-target specification and a movement-initiation cue. Recent evidence suggests that PMd neural activity settles to a movement-specific state during this period. Can trial-to-trial variation in behavior be predicted from the dynamics of settling? Current methods to characterize recurrent neural dynamics on a trial-by-trial basis, and thus answer this and related questions, are limited. Standard methods average activity from different trials or different cells, and so cannot express variable dynamics. The proposed research will test the hypothesis that the dynamics underlying PMd plan activity can be described by a low-dimensional hidden non-linear dynamical systems (HNLDS) model, with underlying recurrent structure and stochastic point-process output. Such a model is capable of expressing rich dynamics, but the task of learning the model parameters from spike data is challenging. The proposed research will develop and validate algorithms for parameter estimation, and then characterize the dynamics in PMd data recorded from an electrode array while monkeys perform delayed-reach tasks. Single trial estimates of underlying dynamics can then be used to predict variation in details of reaching motor behavior. The proposed research program will directly inform cortically-controlled neural prosthesis research in our laboratory and elsewhere. Such motor and communication prostheses could dramatically improve the quality of life for patients with upper spinal cord injuries, amputations, ALS and other neuro-degenerative diseases. The proposed research program will increase our understanding of how PMd rapidly prepares movements, and thereby help increase the speed and accuracy of prosthetic systems.

This Integrative Graduate Education and Research Training (IGERT) grant supports the creation of a new interdisciplinary graduate training program in Emergent Functions of Neural Systems within the Center for Mind Brain and Computation at Stanford University. The effort to understand human mental abilities such as perception, decision making, learning and memory, and motor planning and action as emergent consequences of brain activity remains a major challenge of science, and meeting this challenge requires scientists who combine both quantitative and experimental research methods. Quantitative methods include computational modeling, applied mathematics, and statistics; experimental methods involve recording brain activity while the brain is engaged in mental activity. This program will train the next generation of scientists who will address this challenge by combining quantitative and experimental methods. Trainees may come from Computer Science, Electrical Engineering, Neuroscience, or Psychology at Stanford. Each trainee will formulate an individualized training plan that complements the home department doctoral program, and will pursue research combining quantitative and experimental methods. The program will develop new courses in quantitative and computational neuroscience, and will provide opportunities to bridge across disciplinary boundaries. This IGERT will strengthen the use of quantitative and computational methods that are crucial for breakthrough progress in research aimed at understanding how mental abilities arise from neural processes, and it will strengthen bridges between the disciplines of psychology and neuroscience and the disciplines of computer science, mathematics, and engineering. Trainees will go on to careers in which they will enhance expertise in quantitative and computational methods in the behavioral and neural sciences and pass their expertise on to others. The program will recruit women and underrepresented groups through a variety of outreach and networking activities to pursue careers combining quantitative and computational approaches to understand the relationship between mental and neural processes. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries.

DESCRIPTION Abstract I have trained as an electrical engineer and as a systems neurobiologist. Over the past ten years I have investigated how motor cortices guide arm movements and how this neural activity can be used to control prosthetic arms and computer cursors. I now recognize that a major new research direction is imperative if we are to truly understand the neural control of movement in the "real-world" and design neural prostheses for large numbers of people. The fundamental assumption which I call into question, and aim to address, is that cortical-motor activity is the same across behavioral contexts, contexts as different as sitting quietly in a dark room without moving ones eyes or head (conventional experimental setting) and actively moving around in a large space with lights, sounds and body posture constantly varying (the proposed "real world" setting). We must begin conducting electrophysiological experiments in monkeys able to freely move around in large spaces, which will serve as an animal model for freely moving humans. This is a dramatically new and different direction, which is only now plausible due to tremendous advances in microelectronics technology and the major new technological and experimental paradigms proposed here. I proposed to build a new suite of recording, wireless telemetery and behavioral monitoring technology to enable this new brand of basic and applied neuroscience investigation. We will also conduct a definitive set of neurophysiological and neural prosthetic experiments, to highlight the power and broad applicability of this new paradigm in addition to documenting initial, and likely highly surprising, discoveries. If successful, I anticipate this new experimental paradigm and results to greatly change basic neuroscience investigations of motor control, applied neuroscience/neuroengineering aimed at designing robust and high-performance neural prostheses, and large patient populations whose quality of life will be dramatically improved

Our overarching goal is to establish an animal model of freely moving humans. We choose to do so in order to directly measure the context-dependency of motor cortical activity and, ultimately, other activity reliant upon free movement such as social interaction among animals. Achieving this major technological challenge requires a complete system that includes (Specific Aim 1) wireless transmission of neural data from electrode arrays chronically implanted in monkeys, (Specific Aim 2) computer-vision algorithms to automatically extract body and limb orientation during free movement, and (Specific Aim 3) new mathematical and computational models to represent and extract information from high-dimensional neural and behavioral activity. This technology will enable an animal model of freely moving humans that will advance the development of cortical neural prostheses by providing models of the context-dependant nature of motor cortical control. Unlike traditional laboratory environments used to study animal movement, human amputees and tetraplegics operate in a variety of contexts that involve their movement in the world. Understanding the motor control of complex movement in these natural settings is absolutely critical for future advances in cortically- controlled prostheses. Given our overarching goal, our hypothesis is that motor cortical activity (e.g., directional tuning curves, absolute firing rates, correlations among units, etc.) will be different in important ways when rhesus monkeys perform the same reaching arm movements in an un-constrained context (e.g., not sitting quietly, not head restrained, not in dark and quiet room, etc.) as in a traditional, highly constrained context. Our three Specific Aims will put in place the electronic, computational and mathematical technology necessary to address this hypothesis, and also to make such studies of free behavior in rhesus monkeys possible. The innovative integration of neural engineering, neuroscience, computer vision, mathematics and neural modeling will provide new tools to enable the unprecedented study of motor control during natural, unconstrained behavior.

PROJECT SUMMARY While current augmentative and alternative communication (AAC) devices present a variety of access methods for message generation to benefit people with complex communication needs, there still exists a group of literate adults with severe speech and motor impairments (SSMI) who cannot identify a functional means for typing, which is an important tool for computer-based communication. In prior NIDCD- supported research, our research team developed a high performance intracortical brain-computer interface (iBCI) that decodes movement intentions directly from brain activity. This technology has allowed people to control a cursor on a computer screen for communication simply by imagining movements of their own arm. The proposed R01 clinical research will extend this prior work on improving the performance of iBCI systems, as part of the multi-site BrainGate consortium, and utilizing a new fully- implantable, wireless system being developed under separate NIH BRAIN Initiative funded project. The goals of the project are to leverage the discovery of new motor and cognitive signals in human motor cortex to implement and evaluate three new methods for iBCI typing and general purpose computer use: (1) an automatic Error Detect and Undo (EDU) system that uses error-related signals from motor cortex, (2) decoding techniques that create continuous high degree-of-freedom control signals from motor cortex to increase rates of point-and-click iBCI typing in 3D and 4D as compared with 2D, and (3) decoding techniques that classify multiple different ?click? signals from motor cortex. A rigorous uniform experimental procedure with clear evaluation metrics will be utilized across all three Specific Aims, in all three research participants, and at each clinical site using a standardized suite of iBCI tasks, assuring consistency across sessions and participants. Upon completion, this project will advance both the capabilities of iBCIs for communication and our understanding of the function of human motor cortex.

PROJECT SUMMARY Augmentative and alternative communication (AAC) technology for people with severe speech and motor impairment (SSMI) continues to improve, with recent advances being made in the neural control of communication devices. In prior NIDCD-supported research, our research team developed a high-performance intracortical brain-computer interface (iBCI) that decodes arm movement intentions directly from brain activity. This technology has allowed people with SSMI to control a computer cursor with sufficient speed and accuracy to type at up to 8 words/min and has enabled full control of unmodified consumer devices using only decoded motor cortical activity. In the proposed U01 clinical research, performed as part of the multi-site BrainGate consortium, we will build upon decades of experience in studying the motor system in humans and non-human primates, with the end goal of advancing iBCI technology. The goals of this project are to study how speech is prepared and produced at the level of ensembles of single neurons in speech-related motor areas of the brain in people with amyotrophic lateral sclerosis (ALS), and to create a speech prosthesis that will allow communication at rates approaching conversational speech (120-150 words per minute). We will approach these investigations with a suite of advanced methods, including (1) newly-developed dynamical systems computational approaches that have provided fundamental insights into the function of the motor system, and (2) machine learning algorithms for decoding of movement intention and language modeling that have formed the basis of the fastest communication prosthesis yet reported. Finally, we will continue to evaluate the safety profile of Utah-array based iBCIs through the ongoing BrainGate2 pilot clinical trial. Upon completion, this project will advance both the capabilities of iBCIs for communication and our understanding of the detailed neural mechanisms of speech production.