Advancing Spine Connectomics and Neural Integration through Machine Learning and Neuroengineering

Spine connectomics and neural integration have redefined the spinal cord as a dynamic computational hub, pivotal for sensorimotor coordination and adaptive motor control. This review synthesizes advancements from 2015 to 2025, highlighting how high-density electrophysiology, optogenetics, and brain-machine interfaces (BMIs), including Neuralink’s high-resolution neural arrays, elucidate spinal circuit architecture and function. We explore predictive coding and Bayesian integration mechanisms within spinal networks, revealing their role in processing multimodal sensory inputs and modulating motor outputs. Cutting-edge technologies, such as Neuropixels-Cord probes and 7 Tesla axis-resolved fMRI, enable laminar-specific mapping, while computational models uncover small-world network properties and hierarchical organization. Clinical applications include closed-loop epidural stimulation for spinal cord injury (SCI) rehabilitation, memristive sensors for prosthetic feedback, and personalized neuromodulation for chronic pain and scoliosis. Neuralink’s bidirectional BMIs demonstrate potential in restoring motor and sensory functions post-SCI, though challenges like electrode stability persist. Ethical considerations, including equitable access and neurodata privacy, are critical as these technologies advance. Future directions involve AI-driven discovery, multi-omics integration, and non-invasive neuromodulation to achieve comprehensive spinal connectomes, promising precision therapies for neurological disorders and enhanced musculoskeletal health.