Development of a Regenerative Peripheral Nerve Interface for Control of a Neuroprosthetic Limb

Abstract

Background. The purpose of this experiment was to develop a peripheral nerve interface using cultured myoblasts within a scaffold to provide a biologically stable interface while providing signal amplification for neuroprosthetic control and preventing neuroma formation. Methods. A Regenerative Peripheral Nerve Interface (RPNI) composed of a scaffold and cultured myoblasts was implanted on the end of a divided peroneal nerve in rats (n = 25). The scaffold material consisted of either silicone mesh, acellular muscle, or acellular muscle with chemically polymerized poly(3,4-ethylenedioxythiophene) conductive polymer. Average implantation time was 93 days. Electrophysiological tests were performed at endpoint to determine RPNI viability and ability to transduce neural signals. Tissue samples were examined using both light microscopy and immunohistochemistry. Results. All implanted RPNIs, regardless of scaffold type, remained viable and displayed robust vascularity. Electromyographic activity and stimulated compound muscle action potentials were successfully recorded from all RPNIs. Physiologic efferent motor action potentials were detected from RPNIs in response to sensory foot stimulation. Histology and transmission electron microscopy revealed mature muscle fibers, axonal regeneration without neuroma formation, neovascularization, and synaptogenesis. Desmin staining confirmed the preservation and maturation of myoblasts within the RPNIs. Conclusions. RPNI demonstrates significant myoblast maturation, innervation, and vascularization without neuroma formation.

The future of upper extremity rehabilitation robotics: research and practice

“…The regenerative peripheral nerve interface (RPNI) peripheral nerve interface takes advantage of nerve and muscle regeneration to provide many distinct, unique control signals for high‐fidelity control of prosthetic devices . An RPNI is composed of a transected peripheral nerve, or peripheral nerve fascicle, which is implanted into a free muscle graft (Figure B).…”

When Bio Meets Technology: Biohybrid Neural Interfaces

“…Urbanchek et al [64] Regenerative Peripheral Nerve Interface (with cultured myoblasts) Irwin et al [66] Regenerative Peripheral Nerve Interfaces (with muscle tissue)…”

“…RPNI present a potential strategy to interface divided peripheral nerves with prosthetic limbs. Urbanchek et al [64] encased myoblasts within a biologically stable scaffold (RPNI) that had an ability to provide signal amplification through mature myotubes and prevent neuroma formation (Figure 7). Three scaffold materials were compared: silicone mesh (SM), acellular muscle (AM), and acellular muscle combined with chemically polymerized poly(3,4-ethylendioxythiopene) (PEDOT) conducting polymer (AM+PEDOT).…”

“…Soleus muscle myoblasts were isolated from rats and cultured for 13-17 d. A total of 3 million myoblasts were implanted onto each scaffold (Acellular muscle group (AM) and Acellular muscle and PEDOT (AM+PEDOT)) when myotubes showed signs of contraction in vitro. [64] PEDOT was used to chemically polymerize the inner lining of the implant with the aim of improving the conductivity. A 2 cm section of the distal peroneal nerve was removed.…”

Experimental Testing of Bionic Peripheral Nerve and Muscle Interfaces: Animal Model Considerations

“…For both control and sensory feedback, standard surface electrodes suffer significant limitations, which may potentially be solved by implantable interfaces (Ortiz-Catalan et al, 2012;Farina and Aszmann, 2014). With fairly new surgical procedures, such as targeted muscle reinnervation (TMR) and regenerative peripheral nerve interfaces (RPNI), more muscle signals can be created and, as recent findings show, more cognitive input can be extracted from muscles after targeted reinnervation (Urbanchek et al, 2016;Frost et al, 2018;Vu et al, 2018;Bergmeister et al, 2019). Accordingly, new interfaces are essential to deal with this surplus of potential motor control signals (Bergmeister et al, 2017).…”