Summary
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. 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 high surgical risks, poor control of the activity of spinal motor neurons, and 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. 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. 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. This neural interface will map, engage, and augment the spared output of the spinal cord through new deep learning methods and 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. 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. Using these new technologies, we aim to answer open questions in movement neuroscience and spinal cord injury.
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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/101118089 |
| Start date: | 01-01-2024 |
| End date: | 31-12-2028 |
| Total budget - Public funding: | 1 495 271,00 Euro - 1 495 271,00 Euro |
Cordis data
Original description
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. 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 high surgical risks, poor control of the activity of spinal motor neurons, and 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. 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. 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. This neural interface will map, engage, and augment the spared output of the spinal cord through new deep learning methods and 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. 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. Using these new technologies, we aim to answer open questions in movement neuroscience and spinal cord injury.Status
SIGNEDCall topic
ERC-2023-STGUpdate Date
12-03-2024
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