Aaron P. Batista - US grants

DESCRIPTION (provided by applicant): Brain-computer interfaces (BCI) can assist paralyzed individuals and amputees by translating their neural activity into movements of a BCI plant, such as a computer cursor or prosthetic limb. For many years, the field sought offline decoders that could best map neural activity to arm movements. It has become increasingly recognized that designing an effective online, closed-loop decoder is quite a different challenge. A key difference is that, in a closed-loop setting, the subject receives sensory feedback about the state of the BCI plant and can compensate for errors by generating new neural activity patterns. To engineer clinically-viable, closed-loop BCI systems, many fundamental questions about the neural underpinnings of their performance must be answered. Can subjects generate arbitrary neural activity patterns to compensate for errors? Do subjects form an internal model of the BCI plant to achieve proficient control in the presence of noisy, delayed feedback? Do subjects exploit the redundancy inherent in the mapping from neural activity to BCI plant kinematics to maximize control accuracy? A critical roadblock for answering these questions is the lack of an appropriate statistical framework to rigorously analyze closed-loop BCI data on a timestep-by-timestep basis. We propose to develop such a framework inspired by control theory, in close conjunction with novel closed-loop BCI experiments. We will train non-human primates to perform dextrous control of a BCI cursor using neural activity recorded in primary motor cortex with chronic, multi-electrode arrays. We will test the hypothesis that BCI learning depends on constraints imposed by the underlying neural circuitry. In parallel, we will develop and validate algorithms to explain the observed, high-dimensional neural activity at each timestep by accounting for the sensory feedback, subject's internal model of the BCI cursor, and behavioral task goals. We will then leverage the developed algorithms to investigate whether subjects can exploit neural redundancy during BCI control. Broader Impact: We envision five areas of broader impact. First, BCI systems promise to dramatically improve the quality of life for disabled patients. Clinical trials are ongoing, so opportunities exist to translate our research directly and in the near term into clinical practice. Second, our understanding of the neural basis for arm movement control is still incomplete, in large part because the system is so complex. BCIs provide a simplified motor control system, where a well-defined relationship exists between neural activity and movement. As such, BCIs provide a novel experimental testbed to investigate the neural mechanisms of motor control and learning. Third, the statistical framework we develop may be applicable to the study of feedback control systems in other domains. Fourth, with the advent of large-scale neural recordings, systems neuroscience is becoming a far more quantitative field. The next generation of researchers must be well-versed in computational and biological principles. We believe that our collaboration provides an excellent dual-training environment for our students and postdocs. Fifth, our research discoveries can directly feed into our classroom teaching. Yu teaches Neural Signal Processing at CMU and Batista teaches Control Theory in Neuroscience at Pitt;both are annual, graduate-level courses. Intellectual Merit: In the last decade, several groups have demonstrated compelling proof-of- concept laboratory demonstrations of closed-loop BCI control. For clinical translation, one of the major challenges is to improve the performance and robustness of BCI systems. To make this leap, we believe that it is critical to rigorously study existing systems to understand i) why some BCI decoders work better than others, ii) to what extent we can depend on the subjects'ability to learn, and iii) the neural strategies adopted by the subjects for proficient control. There is a long-overdue need for a general statistical framework for dissecting closed-loop BCI data, which we propose to develop. Discoveries enabled by the developed methods will help us and others in the field to design high-performance, clinically-viable BCI systems that allow the subject to quickly reach and maintain a high level of proficiency.

DESCRIPTION (provided by applicant): As we interact with our environment, we perform dextrous, coordinated movements of multiple effectors. I am particularly interested in eye/hand coordination - the processes by which we use vision to guide our arm and hand movements. We study the premotor cortex in Rhesus monkeys trained to perform visuomotor behaviors. Neurons in premotor cortex are active in relation to reaching, and some of them project polysynaptically to the eye muscles, which implicates them in eye/hand coordination. The two overarching goals of this research are complementary: First, we will study the conditions under which sensory-motor transformations can be modified. Our pilot data suggest that training experience and the immediate demands of behavior can shape neural response properties, causing premotor neurons to become more sensitive to the position of the eyes. Inducing a sensitivity to a new sensory modality is an extreme form of neural plasticity, of a type typically only seen after brain injury. The second goal of this research is to explore the role of the premotor cortex in behavior. Our pilot data indicate that premotor cortex may be even more versatile and flexible than is typically assumed. The chief significance of this research is that it will change our understanding of sensory-motor processing by showing it is more flexible and malleable than has been presumed. The chief innovation is in our use of multielectrode array recordings to perform longitudinal studies of learning in multiple brain areas simultaneously. Our main approach is to record high-volume neural data sets from multiple cortical areas while monkeys learn and perform motor behaviors. This will provide a rich and high-impact data set that will yield detailed comparative information about the premotor cortices.

DESCRIPTION (provided by applicant): Brain-computer interfaces (BCI) can assist paralyzed individuals and amputees by translating their neural activity into movements of a BCI plant, such as a computer cursor or prosthetic limb. For many years, the field sought offline decoders that could best map neural activity to arm movements. It has become increasingly recognized that designing an effective online, closed-loop decoder is quite a different challenge. A key difference is that, in a closed-loop setting, the subject receives sensory feedback about the state of the BCI plant and can compensate for errors by generating new neural activity patterns. To engineer clinically-viable, closed-loop BCI systems, many fundamental questions about the neural underpinnings of their performance must be answered. Can subjects generate arbitrary neural activity patterns to compensate for errors? Do subjects form an internal model of the BCI plant to achieve proficient control in the presence of noisy, delayed feedback? Do subjects exploit the redundancy inherent in the mapping from neural activity to BCI plant kinematics to maximize control accuracy? A critical roadblock for answering these questions is the lack of an appropriate statistical framework to rigorously analyze closed-loop BCI data on a timestep-by-timestep basis. We propose to develop such a framework inspired by control theory, in close conjunction with novel closed-loop BCI experiments. We will train non-human primates to perform dextrous control of a BCI cursor using neural activity recorded in primary motor cortex with chronic, multi-electrode arrays. We will test the hypothesis that BCI learning depends on constraints imposed by the underlying neural circuitry. In parallel, we will develop and validate algorithms to explain the observed, high-dimensional neural activity at each timestep by accounting for the sensory feedback, subject's internal model of the BCI cursor, and behavioral task goals. We will then leverage the developed algorithms to investigate whether subjects can exploit neural redundancy during BCI control. Broader Impact: We envision five areas of broader impact. First, BCI systems promise to dramatically improve the quality of life for disabled patients. Clinical trials are ongoing, so opportunities exist to translate our research directly and in the near term into clinical practice. Second, our understanding of the neural basis for arm movement control is still incomplete, in large part because the system is so complex. BCIs provide a simplified motor control system, where a well-defined relationship exists between neural activity and movement. As such, BCIs provide a novel experimental testbed to investigate the neural mechanisms of motor control and learning. Third, the statistical framework we develop may be applicable to the study of feedback control systems in other domains. Fourth, with the advent of large-scale neural recordings, systems neuroscience is becoming a far more quantitative field. The next generation of researchers must be well-versed in computational and biological principles. We believe that our collaboration provides an excellent dual-training environment for our students and postdocs. Fifth, our research discoveries can directly feed into our classroom teaching. Yu teaches Neural Signal Processing at CMU and Batista teaches Control Theory in Neuroscience at Pitt; both are annual, graduate-level courses. Intellectual Merit: In the last decade, several groups have demonstrated compelling proof-of- concept laboratory demonstrations of closed-loop BCI control. For clinical translation, one of the major challenges is to improve the performance and robustness of BCI systems. To make this leap, we believe that it is critical to rigorously study existing systems to understand i) why some BCI decoders work better than others, ii) to what extent we can depend on the subjects' ability to learn, and iii) the neural strategies adopted by the subjects for proficient control. There is a long-overdue need for a general statistical framework for dissecting closed-loop BCI data, which we propose to develop. Discoveries enabled by the developed methods will help us and others in the field to design high-performance, clinically-viable BCI systems that allow the subject to quickly reach and maintain a high level of proficiency.

Cognition and behavior unfold over time. The temporal aspects of thought and action reflect, at least in part, the temporal evolution of the activity of the populations of neurons that control them. It has long been hypothesized that the time course of neural activity arises from dynamical constraints imposed by the underlying neural circuitry. However, this has been difficult to show experimentally because it requires the ability to finely perturb neural activity in varied ways. Here, we propose to employ a brain-computer interface (BCI) paradigm to study neural dynamics. A BCI enables us to perturb neural activity by harnessing the animal's volitional control to drive the activity of a population of neurons into configurations that we specify. In this way, we can perform causal tests of dynamical constraints and their relation to behavior. We will study dynamical constraints imposed by motor preparation using multi-neuronal activity recorded in the motor cortex of macaque monkeys. We hypothesize that dynamical constraints exist in the motor cortex, and that these constraints are shaped during motor preparation to drive arm movements. To test these hypotheses, we will first challenge the animals to violate the putative dynamical constraints. Then, we will test the hypothesis that motor preparation sets up dynamical constraints appropriate for the upcoming arm movement. Finally, we will use the BCI paradigm to perturb neural activity during movement preparation to alter and evoke arm movements. Taken together, our proposed work will likely lead to a richer understanding of how networks of neurons give rise to population dynamics, and how those dynamics relate to neural computation and behavior.

Project Summary: Multisensory processing is vital for daily activities such as walking and manipulating objects, yet much remains unknown about the neural mechanisms by which sensory information is integrated in the central nervous system to influence motor control. We address this knowledge gap by analyzing behavioral and multi-neuronal multi-area recordings in the cerebral cortex of Rhesus monkeys trained to perform a prolonged motor control task (the critical stability task (CST)) that cannot be performed without continuous sensory feedback (visual and/or tactile). Rhesus monkeys will perform the CST using hand movements or a brain-computer interface (BCI) to control a cursor, while we manipulate sensory feedback. Neural activity will be recorded from primary visual (V1), somatosensory (S1) and motor cortices (M1). Our motivating hypothesis is that cortical processing is highly flexible, and can be rapidly reconfigured based on the immediate sensory and motor context. Several specific predictions flow from this perspective. First, we predict that primary motor cortex (M1) will exhibit a strong sensory response during a motor task that requires ongoing sensory feedback. Second, we hypothesize that V1 neurons adopt tactile responses, and S1 adopts visual responses, when both are relevant for ongoing motor control. Third, we expect that altering the signal quality of one sensory modality will shift their relative contribution to neural responses, consistent with Bayesian estimation. Animals will perform the CST using BCI control as a more dramatic test of cortical flexibility. During BCI control, sensory responses should be reduced in M1, since the BCI decoder cannot distinguish sensory responses from motor commands, which would diminish the quality of control. If multisensory integration is reduced in M1 under BCI control, then it must occur elsewhere. We hypothesize that there will be an enhanced cross-modal sensory representation in the primary sensory cortices under BCI control, in comparison to hand control. We approach these questions through a collaboration that combines expertise in sensorimotor neurophysiology with expertise in computational modeling of multisensory integration. The findings of this research will improve the understanding of the neural mechanisms of multimodal sensory integration during continuous motor tasks, and will have clinical implications for BCIs and advanced prostheses design.

Project Summary Behavior and cognition emerge from the coordinated activity of populations of interacting neurons. To change behavior we must change the architecture of the network that drives behavior. What are the rules whereby the activity of populations of neurons can change during learning? If we could observe the neural population dynamics that underlie skill learning, we might be able to facilitate learning. This ability may eventually lead to improvements to neurally- based rehabilitation strategies that can facilitate the recovery from stroke. We will use a neurofeedback paradigm to shape the neural population dynamics that underlie skill learning. To do this, we will record the activity of dozens of neurons in the motor cortex of Rhesus monkey subjects. Animals will control a cursor on a computer screen by generating neural command signals. This is neurofeedback because the animal directly observes a projection of his neural activity, in the form of the movement of the onscreen cursor. This paradigm allows us to study learning simply by perturbing the mapping from neural activity to cursor movement on the screen. Following such a perturbation the animal must discover how to generate new patterns of neural activity that are now appropriate to restore good control of the onscreen cursor. We will ask two linked questions. First, how does the animal learn to generate new patterns of neural activity? And second, can we facilitate that process? Neurofeedback-based learning offers complementary advantages to arm movements for studying the neural population dynamics that accompany learning. Chie?y, only in a neurofeedback paradigm can we be certain that the learning-induced changes in neural activity we observe matter directly for behavior, because only the neurons we record directly impact behavior in this paradigm. We will test classic theories of skill learning, converted into speci?c hypotheses about how neural activity patterns will change throughout the multi-day course of skill learning. If our neurofeedback-based incremental training schemes do facilitate learning, then this research will inform the work of rehabilitation specialists who work with stroke patients. We (and others) believe that if neurofeedback-based therapies are coupled with standard behavioral therapies for stroke, rehabilitation outcomes will improve.

How can a basketball player reliably land his free throw in the basket, and yet still miss occasionally under nominally identical circumstances? While such skills are a paragon of motor expertise, even seemingly mundane actions also require surprising dexterity. When carrying a full cup of coffee, we exhibit motor skill that is far beyond what is typically studied in the laboratory. Specifically, when interacting with objects - the essence of any tool use -, successful actions require fine-grained control of interaction forces that have been beyond the purview of neuroscience to date. The proposed research examines the neural basis of motor expertise by bringing rich interactive tasks into the laboratory. The two PIs combine their long-standing experience in computational motor control and neurophysiology to study novel behavioral paradigms both in humans and non-human primates. Building on conceptual and computational overlap in their respective research, where skill is associated with low-dimensional structure in high-dimensional neural and behavioral redundant spaces, they will test the overall hypothesis that patterns of neural activity exhibit many of the characteristics of the behavior. Two aims will study two examples of motor skill: throwing an object and transporting an object with internal dynamics, both rendered in virtual environments. Parallel experiments in humans and primates will generate rich behavioral data that will be matched with intracortical recordings in the cerebral cortex of non-human primates. To date, non-human primate studies have necessitated that animals perform near-identical repetitions of simple behaviors to facilitate the analysis of neural activity. Now, modern multi-neuronal recording techniques make it possible to embrace more sophisticated real-world behaviors and address core principles of movement discovered in human motor control: high dimensionality, redundancy, and the ever-present variability. This research will develop a suite of computational tools that afford the analysis of behavioral and neural data with commensurate techniques and sophistication. This research will be transformative as it advances the motor challenges examined and brings insights from intracortical neurophysiology closer to understanding of human motor expertise. These scientific insights will channel into a large range of outreach activities to achieve broader impacts for the general public. RELEVANCE (See instructions): Patients with neurological disorders such as stroke face challenges in their daily activities, grasping a cup to bring to their mouths to drink; these actions are essentially interactive tool use. This research seeks insights into neural activation during such skilled actions and interactions to get closer to understand neural activity in tasks relevant in real life. Extending from PI Batista?s experience, neuroprosthetics and brain-machine interfaces are direct clinical application that may benefit from our findings and recovery