Collaborative Research: Using a Fully Autonomous Brain-Body Interface to Study the Cortical Dynamics of Learning

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Abstract

1159652/1159695 Brown/Bizzi The brain controls movements of the body by means of neural signals transmitted through the spinal cord. Researchers in the field of Motor Neural Prosthetics attempt to tap into these neural signals and use them to control artificial actuators, such as a robotic arm or computer cursor, or native limbs that have been paralyzed. The long-term goal of such research is to help restore function to a variety of patient populations for whom the normal spinal pathways of movement control have been disrupted due to neurological disease, brain and or spinal cord injury. At present, however, the movements generated by neuroprosthetic devices lack the smoothness, and fluidity of natural movement. Furthermore, although Brain-Machine Interfaces would appear to provide a unique opportunity for studying brain function, the technology of neural prosthetics has not yet made major contributions to an improved understanding of how the circuits in the brain that control movement work. This grant proposes the construction of a novel Brain-Body interface in animals for which elbow function is reversibly paralyzed. By connecting the output of recording electrodes placed in the brain to stimulating electrodes placed near the paralyzed elbow muscles, a pathway is created to re-establish control of the lost motor function. Unlike most research in the field, this research requires that all of the learning takes place within the brain and none of the learning takes place through a "Decoder" - that is, a computer-based machine-learning algorithm that attempts to read the mind of the user and extract the desired signals to control the movement. Thus, our Brain-Body interface is fully autonomous and requires no outside intervention. Initially, performance may be inadequate. Just as an infant requires considerable time to establish control over motor pathways, so too will a subject require time to learn to control this entirely new pathway. However, given adequate learning time, this architecture is likely to lead to superior performance, because all of the control resides within the brain and because the brain has a remarkable adapt and learn. There are two key intellectual merits to the proposed research. The first is to use this novel preparation to the study natural systems-level mechanisms of learning present in the brain, so that how our brains self-organize during development and how our brains adapt to injury or disease are better understood. The second is to transform the fields of Brain-Machine and Brain-Body Interfacing by exploring the level of control attainable when the native brain does all of the learning rather than computer algorithms. The research has additional broader significance. In particular, through this work it is hoped that a better-performing Brain-Body interface can be developed to help restore movement function in those suffering from neurological disorders or brain injury. Moreover, this work further cultivates the methodology of Direct Brain Control (a particular form of biofeedback) for the design of rehabilitative devices. In so doing, it enhances the existing research infrastructure by bringing together clinical anesthesiologists, neurophysiologists, and engineers to forge a cross-disciplinary solution to this important problem.