Peripheral nerve injuries (PNIs) severely impair motor and sensory function, diminishing patient independence and quality of life. Despite decades of research, rehabilitation has prioritized motor recovery and residual function over the sensory restoration essential for embodiment, intuitive control, and natural movement. This lack of feedback drives poor prosthetic integration, high cognitive demand, and high abandonment rates. This review organizes the field along the translational pathway from injury to functional recovery: the biology of what is lost, strategies that restore the native substrate, the interfaces required when it cannot be rebuilt, and how these strategies are integrated and embodied. Approaches are compared by biological target, sensory function restored, invasiveness, clinical maturity, and limitation, emphasizing how interface properties such as modulus mismatch, charge injection capacity, and foreign body response govern long-term stability and naturalness. Proprioception, the hardest and least measurable sensation to restore, is examined in depth, as are biomimetic, neuromorphic, biohybrid, and machine-learning approaches toward adaptive, closed-loop communication, weighed against their limits to adoption. The review closes with design principles and the challenges they face, from power and scalability to regulatory and ethical hurdles. The future lies in patient-centered interfaces that let individuals not only move, but feel again.
Sydney Swedick, S. El Hadwe, Ke-Si Liang et al.· Advances in Materials· 0 citations
Spinal cord injury affects over 2.5 million people worldwide, yet current neuroprosthetic strategies remain fragmented, addressing motor, sensory, or autonomic function in isolation. Here we show that a single ultrathin circumferential electrode array, conforming to the spinal cord without penetrating neural tissue, can simultaneously decode motor intent, classify sensory inputs, and discriminate visceral sensory inputs. In freely moving rats during short-term implantation (up to three days), deep learning decoders achieved robust motor intent decoding (R² = 0.97) by exploiting low-frequency spinal oscillations aligned with central pattern generator rhythms. The same interface classified eight sensory modalities with 94.4% accuracy. In acutely anaesthetized pigs, cross-species validation confirmed translational scalability and reliably distinguished visceral sensory inputs. Uniquely, the two-row electrode configuration resolved directional propagation within spinal tracts while electrode-dense one-row devices enabled high-precision intraspinal source localization. By consolidating motor, sensory, and visceral afferent decoding within a single conformal interface, this approach positions the spinal cord as a target for multifunctional neuroprosthetic interfacing, offering a path toward integrated restoration of physiological function after neurological injury. Spinal cord injury disrupts motor, sensory, and autonomic functions. Here, the authors demonstrate that a single ultrathin circumferential intradural electrode array can decode motor intent, classify sensory inputs, and discriminate visceral afferent signals across rodent and porcine models.
S. El Hadwe, Rubén Ruiz-Mateos Serrano, George Psaltakis et al.· Nature Communications· 0 citations