Background Spinal cord injury (SCI) has profound and often lifelong consequences. In the absence of proven cellular or pharmacologic therapies to reverse neural damage, neurorehabilitation strategies that harness neuroplasticity through practice and training remain the most effective means of improving function. Task‑oriented practice drives use‑dependent neuroplasticity and functional gains, and can be augmented by neuromodulatory interventions. To maximize real‑world impact, neuromodulatory strategies must prioritize cost‑effective, clinically accessible tools. Obtaining the best outcomes from rehabilitation also depends on the ability to anticipate future functional potential and tailor interventions accordingly. When expertly and consistently applied, some current clinical measures provide valuable predictive insight, but heterogeneity in clinical presentation and variability in responsiveness continue to challenge individualized treatment planning. Objectives This article will examine how neuromodulatory strategies, predictive measures, and biomarkers can be integrated with intensive, neuroscience-informed rehabiltation to improve functional outcomes, enhance clinical trial design, and advance individualized care for people with SCI. Results Clinically accessible neuromodulation can influence spinal and cortical circuits to enhance motor output for improved hand and walking function and for managing spasticity. Emerging electrophysiological, imaging, and molecular biomarkers, along with established clinical prediction models offer means to estimate recovery potential, guide intervention selection, and identify likely responders. Novel physiologic measures, such as limb accelerations recorded during sleep, provide noninvasive indicators of neuromotor function that may improve prediction accuracy. Conclusion Incorporating intensive, neuroscience-informed and systematically‑documented rehabilitation into clinical trial protocols is essential for developing the most effective SCI therapies. In addition, stratifying participants using biomarker or prediction model profiles can lead to more robust research evidence. Leveraging predictive tools to reduce variability and enhance statistical power can improve reproducibility and optimize detection of treatment effects. Together, these strategies support more targeted, efficient, and impactful SCI rehabilitation and research, with the potential to deliver advances that have long eluded the field.
Field‐Fote et al. (Wed,) studied this question.