Ph.D. Research

Experimental Validation of Primary Afferent Based Somatosensory Neuroprosthesis

Losing a limb has a profound impact on the life of an individual. Unfortunately even the most advanced prosthetic limb pales in comparison to the dexterity and strength-to-weight ratio of a natural limb. Furthermore, artificial limbs cannot provide the exquisitely sensitive feedback provided by the body’s natural sensors. For these reasons, the adoption rates for prosthetics remain low.
Recent advances in neurotechnology have provided researchers with the ability to stimulate through many independent electrodes. This technology can form the basis of future somatosensory neural interfaces (SSNI), replacing feedback once provided by skin and muscle with the state of a prosthetic limb encoded in patterned electrical stimulation. Suitable neural implant sites and electrode technologies for restoring sensation are the subject of active investigation.
One potential substrate for a SSNI are the dorsal root ganglia (DRG), enlargements of the spinal nerves that house primary afferent cell bodies. The DRG are compact structures located bilaterally outside the spinal cord but still isolated from movements and large muscles within the vertebral column. Unlike the peripheral nerves, the DRG are segregated from efferent flow so afferents can be recruited by stimulation without the risk of producing uncomfortable contractions of residual muscles.
My dissertation evaluates the recruitment properties of electrodes implanted in the DRG. First, recordings from primary somatosensory cortex were used as the basis for evaluating primary afferent stimulation. Then, the effective lifetime of microelectrodes implanted in the DRG was characterized using recordings from the sciatic nerve. Subsequent instrumentation of many distal nerves enabled direct measurement of the selectivity and peripheral targets of recruited neurons. Finally the selectivity and dynamic range of epineural electrodes were characterized to demonstrate a path forward to clinical testing.

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