Development of the Osseointegrated Neural Interface for prosthetic control into a pre-clinical translational sheep model.

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Clinical significance: Major amputation of a limb has a significant impact on an individual's quality of life, as well as directly affecting family members and the greater community. Veterans represent a unique population that are more greatly affected by amputation than the civilian population. The etiology of amputation in veterans is twofold. At one end of the spectrum there are the inevitable traumatic injuries of modern combat, particularly blast wounds from improvised explosives. These injuries predominantly affect young warriors, both male and female, under 30 years of age, who are increasingly experiencing major traumatic amputations of more than one limb at a time. At the other end of the spectrum, the aging veteran population is increasingly undergoing lower limb amputation secondary to peripheral arterial disease. Regardless of cause, most veterans live for many years after amputation, and bear the significant disabilities associated with losing one or more limbs. As such, there is an increasing demand for both the Department of Defense (DOD) and Department of Veterans Affairs (VA) to provide long-term financial and medical support to veteran amputees. Veteran and civilian amputees share the same physical and social determinants to quality of life, such as ability to participate in activity and pain management. The new age of robotic prostheses holds great promise for alleviating many if not all restrictions to physical activity and participation in greater society. However, the ability to seamlessly control a prosthesis as if the device were the original limb remains a futuristic goal. To specifically address the urgent need for better prosthesis control, we have devised the novel Osseointegrated Neural Interface (ONI) for prosthesis control on several fronts simultaneously. The ONI combines modern surgical procedures with state-of-the-art neural interfacing technology and osseointegration to form a single, compact unit, inside the medullary canal of the amputated limb. We have previously demonstrated in rabbits that an ONI is capable of bi-directional neural signaling, including motor signal output and sensory input that would serve for prosthesis control. Consequently, the objective of this grant is to translate our rabbit ONI model for prosthetic control into a preclinical large animal ovine (sheep) model, and implement a closed loop, osseointegrated, neural prosthetic with sensory feedback to be studied in a rehabilitation setting for future clinical translation. Our long-term goal is the clinical application of our novel ONI for prosthetic control to serve the veteran amputee community. Proposed Methods: A below knee amputation will be performed in a total of 10 skeletally mature sheep. An ONI will be created at the time of amputation by translocating the tibial, sural, and common peroneal nerves into the medullary canal of the amputated tibia via a corticotomy. The terminal ends of the nerves will be interfaced with cuff electrodes, connected to a wireless subcutaneous receiver/transmitter for chronic electrophysiology. The ONI will be completed by the integration of an osseointegrated abutment for prostheses attachment. After recovery, animals will be chronically evaluated for neural function via evoked action potentials through the ONI, bone density via computed tomography, and prostheses control via a battery of behavioral tests. Expected Outcomes: At the end of the funding period, we will understand in detail the requirements for creating a robust, chronic, osseointegrated neural interface for prosthetic control, including voltage limitations, action potential patterns, implant size limitations, and intramedullary capacity to house advanced electronics in combination with osseointegration. This foundational work will provide a direct route to clinical translation as well as a greatly needed pre-clinical model in which to test advanced paradigms of neural interfacing with closed loop feedback in a rehabilitation setting.