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ALGORITHM DEVELOPMENT: SIGNIFICANT TIBIAL NERVE FASCICULAR CONTRIBUTIONS TO LEG CONTROL
Presenter: James Allison, BA
Co-Authors: Washabaugh EP; Moon JD; Kung TA; Langhals NB; Cederna PS; Urbanchek MG
University Of Michigan

Purpose: Electrode containing Regenerative Peripheral Nerve Interfaces (RPNIs) along with wireless technology are in development to control prosthetic devices. RPNIs are implanted surgically grafted muscle neurotized with nerve fascicles in the residual limb. Current prosthetic algorithms are derived from surface electromyography; they code for only a few degrees of freedom. RPNIs based on individual fascicles will transmit multiple, independent, isolated motor and sensory signals allowing more degrees of freedom and sensory feedback from prosthetic devices. Our purpose is to characterize individual mixed nerve fascicles and end organ contributions to closed loop neural control.

Methods: Rat (n=9) tibial nerves were dissected into three fascicles (Fig 1). Each fascicle was stimulated using a bipolar probe. Evoked compound muscle action potentials (CMAPs) were measured in the medial and lateral gastrocnemius, soleus and posterior tibialis muscles (recording muscle). Multiple regression analysis was performed to determine the strength of CMAP prediction using nerve fascicular anatomy and recording muscles as predictors. Cross-sections of tibial nerve were histologically labeled for sensory nerve fibers and digitally measured.

Results: Analysis revealed that both stimulated fascicle and recording muscle were statistically significant contributors to CMAP amplitude at =0.05 (Table 1). Furthermore, the fascicle stimulated was an independent contributor to CMAP area while recording muscle was a significant contributor to conduction velocity. Preliminary nerve histology indicates sensory nerve fibers compose 20% of the total nerve area and they are clustered within the nerve (Fig 2).

Conclusions: Our data demonstrate that individual nerve fascicles and recording muscles each make unique contributions to neural control. Selection of the appropriate nerve fascicle and muscle for an RPNI can have significant implications for motor and sensory signal fidelity and motor control. These data will contribute to algorithms for predicting closed loop neural control to maximize the degrees of freedom in prosthetic limbs.


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