The goal of this project is to develop a primate model of an upper extremity neuromuscular stimulation system controlled by means of intra-cortical recording electrodes. Individuals with spinal cord injury become paralyzed because they have lost the ability to activate their muscles. These patients' muscles can still be made to contract if they are activated by means of electrical stimuli applied directly to the muscle or nerves. Likewise, the areas of the brain that normally control movement are still active, but their connection to the muscles has been lost as a result of the injury. Researchers at Case Western Reserve University (CWRU) have demonstrated that implanted functional electrical stimulation (FES) neuroprostheses can be used to restore grasp functions to individuals with tetraplegia. Although remarkable, these systems are limited to pre-programmed grasp patterns, and require considerable conscious attention. A more natural control system, with more degrees of freedom could provide greatly improved function. At Northwestern, we have developed methods to predict the activity of arm and hand muscles during grasping movements based on micro-electrode recordings from the brain of a monkey. From a single, chronically implanted array of electrodes, predictions can be made of the activity of shoulder, arm and hand muscles. This type of electrode has yielded maintained recordings for periods in excess of 3 years, and it has recently been approved for experimental use in human patients. We believe that intra-cortical recordings like these provide the potential for simultaneous control of multiple degrees of freedom through natural thought processes. By combining the strengths of the Northwestern and CWRU groups, we propose to develop a brain-computer interface adequate for controlling a neuroprosthesis. The development of a primate model of this neuroprosthetic system would be a major step toward its implementation in human patients. This application includes the following specific aims: 1) We propose to use a 100-electrode array implanted in the primary motor cortex of a mnkey to provide the input to a set of decoders designed to produce real-time predictions of the activity of particular hand muscles. 2) We propose to use the control algorithms developed in aim 1 and an implanted FES prosthesis to restore grasp following temporary muscle paralysis induced by a pharmacological nerve block. 3) We propose to develop these control algorithms without the use of initial EMG measurements, as would be necessary in order to implement the system for a patient. ? ?

National Institute of Health (NIH)
National Institute of Neurological Disorders and Stroke (NINDS)
Research Project (R01)
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Special Emphasis Panel (ZRG1-IFCN-F (02))
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Chen, Daofen
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Northwestern University at Chicago
Schools of Medicine
United States
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Pandarinath, Chethan; Ames, K Cora; Russo, Abigail A et al. (2018) Latent Factors and Dynamics in Motor Cortex and Their Application to Brain-Machine Interfaces. J Neurosci 38:9390-9401
Gallego, Juan A; Perich, Matthew G; Naufel, Stephanie N et al. (2018) Cortical population activity within a preserved neural manifold underlies multiple motor behaviors. Nat Commun 9:4233
Benjamin, Ari S; Fernandes, Hugo L; Tomlinson, Tucker et al. (2018) Modern Machine Learning as a Benchmark for Fitting Neural Responses. Front Comput Neurosci 12:56
Perich, Matthew G; Gallego, Juan A; Miller, Lee E (2018) A Neural Population Mechanism for Rapid Learning. Neuron 100:964-976.e7
Brill, N A; Naufel, S N; Polasek, K et al. (2018) Evaluation of high-density, multi-contact nerve cuffs for activation of grasp muscles in monkeys. J Neural Eng 15:036003
Perich, Matthew G; Miller, Lee E (2017) Altered tuning in primary motor cortex does not account for behavioral adaptation during force field learning. Exp Brain Res 235:2689-2704
Gallego, Juan A; Perich, Matthew G; Miller, Lee E et al. (2017) Neural Manifolds for the Control of Movement. Neuron 94:978-984
Brill, Natalie A; Tyler, Dustin J (2017) Quantification of human upper extremity nerves and fascicular anatomy. Muscle Nerve 56:463-471
Ethier, Christian; Acuna, Daniel; Solla, Sara A et al. (2016) Adaptive neuron-to-EMG decoder training for FES neuroprostheses. J Neural Eng 13:046009
Sachs, Nicholas A; Ruiz-Torres, Ricardo; Perreault, Eric J et al. (2016) Brain-state classification and a dual-state decoder dramatically improve the control of cursor movement through a brain-machine interface. J Neural Eng 13:016009

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