A Direct Sensorimotor Connection with the Spared Neural Code of Movement to Regain Motor Function

Introduction

Many individuals suffer partial or complete muscle paralysis with no available cures. Even though neural interfaces have the potential to restore motor function with assistive systems, their use is still very limited.

Current Limitations

Even in the case of state-of-the-art invasive neural implants, the control of the movements of the paralyzed limbs is highly unsatisfactory. These neural interfaces suffer from:

• High surgical risks
• Poor control of the activity of spinal motor neurons
• Inaccurate mapping of the attempted movements

Spinal motor neurons are the last cells of the nervous system that convert motor commands into movement, and their activity can be accessed with minimally invasive methods.

Neural Lesions and Spinal Motor Neurons

In most neural lesions, such as spinal cord injury and stroke, there are functionally active spinal motor neurons projecting to paralyzed muscles that are modulated by brain input.

Proposed Solution

In this project, I propose a bidirectional interface that is driven by the real-time identification of efferent spinal motor neuron activity. We will develop novel sensing, decoding, and feedback methods with precise cellular resolution.

Methodology

This neural interface will:

1. Map, engage, and augment the spared output of the spinal cord through new deep learning methods.
2. Utilize hundreds of fine-tuned electromyographic sensors recording action potentials of individual motor units for the muscles controlling the hand.

The output of this interface will enable highly accurate temporal associations between efferent motor neuron activity and sensorimotor feedback by delivering multiple visual and somatosensory inputs.

Goals

This bidirectional neural interface will entrain and monitor the spared neural pathways at the direct cellular level with the goal of transforming and augmenting the activity of the spared motor neurons into highly functional motor dimensions.

Conclusion

Using these new technologies, we aim to answer open questions in movement neuroscience and spinal cord injury.