Wasim Q. Malik, Ph.D. - US grants

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.

This project addresses a question that has vexed scientists for more than a century: how does the motor cortex, i.e. the part of the brain where nerve impulses initiate voluntary muscular activity, represent and coordinate multiple muscles in order to produce a vast range of movements? To answer this question, this project will harness the unique strengths of non-invasive, navigated, transcranial magnetic stimulation (TMS) mapping to establish causal links between brain physiology and behavior. TMS is achieved by placing a coil of wires near the scalp, which when activated with an electrical current will create a magnetic field across the scalp and skull. TMS is the only non-invasive method available to stimulate the brain as effectively as invasive stimulation for mapping the motor cortex. However, innovations in three areas are critically needed to use TMS-based motor mapping to understand multi-muscle physiology and control: 1) drastically improving the efficiency, efficacy and reliability of the TMS-based motor cortex mapping processes, 2) characterizing and validating TMS-based mapping as a probe for understanding the relationship between multi-muscle activation and voluntary movement, and 3) applying a neural network computational method to improve understanding of motor control and organization. Enhanced understanding of motor cortex physiology through TMS mapping of motor representations has the potential to better map the brain in applications such as surgical removal of tumors, assessing brain injury due to concussions or stroke, and identifying cortical networks needed for successful brain-machine interactions for controlling prostheses. Students involved with this project will be trained to address multidisciplinary challenges at the intersection of neuroscience, non-invasive brain stimulation, software design, control theory, machine-learning, statistical signal processing, data dimensionality reduction and visualization. Partnership with Boston-based leaders in the technology industry will provide state-of-the-art training to undergraduate, graduate, and post-graduate trainees. Through cooperative educational programming at Northeastern University and internships with Mass General Hospital, STEM-based learning opportunities will be provided for middle- and high-school students, inspiring a diverse body of students to pursue STEM careers. To promote STEM careers and demonstrate impact, the team will reach out to local venues that promote public awareness and appreciation of science, such as science fairs and the Boston Museum of Science.

The goal of this collaborative project is to develop a deeper mechanistic understanding of the role of the motor cortex (M1) in controlling single muscles and synergies in producing complex movements. This will be accomplished by developing several innovations in the use of non-invasive transcranial magnetic stimulation (TMS) to map the spatial distribution of synergies and single muscles. Transformative computational advances will be used to extract more accurate information about brain interaction with other physiological systems outside the motor domain and increase the rigor of analysis and data visualization to enhance interpretability, and repeatability. An enhanced understanding of corticomotor organization of complex movement will pave the way to studying motor system development across the lifespan, the basis of human performance enhancement, and the basis and characterization of neuromotor diseases. The research plan is organized under 3 aims. AIM 1 is to accelerate acquisition of TMS-based maps by developing an active learning process based on a Gaussian Process Model (GPM) of Muscle Evoked Potentials (MEPs) as a function of 2D spatial coordinates on the scalp. The developed Active-GMP learning algorithm is expected to speed up the mapping process by diverting time spent on loci with null data to loci where the model needs more samples to improve certainty. The efficacy and the accuracy of the new algorithm will be compared to three existing alternatives. AIM 2 is to test the behavioral relevance of synergies derived from human multi-muscle TMS mapping, i.e., to biologically validate the technical methods developed in Aim 1. Specifically, TMS and Voluntary (VOL) EMG data will be collected from 16 hand-arm muscles in healthy participants while subjects mimic hand postures for static letters and numbers of the American Sign Language alphabet. Non-negative matrix factorization-extracted synergies from VOL data and TMS data will be compared to determine if the TMS-elicited synergies match those utilized during movement production and if the adaptive Active-GMP and user-guided approaches more closely match synergies derived from VOL data compared to other approaches. AIM 3 is to develop generative and inverse topographic imaging models that allow forward modeling of M1 control and reverse mapping of M1 organization, respectively, of muscles and synergies. Hybrid models combining subject-specific FE modeling of TMS-induced cortical electric fields with neural network models trained to predict evoked muscle responses will be used to answer key questions: Q1) Are synergies dominant features of motor control? Q2) Do direct M1 motorneuron projections augment a synergy model of control? and Q3) Are muscles and synergies discretely organized in M1?

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.