Precision Neurorehabilitation: Integrating Biomarkers, Neuroimaging and AI for Personalized Therapy

Abstract

Introduction: Precision neurorehabilitation represents a fundamental paradigm shift away from standardized, protocol-driven treatment toward dynamically adaptive therapies tailored to the unique biological and functional profiles of individual patients. The recognized high degree of heterogeneity in structural injury, recovery mechanisms, and functional outcomes following central nervous system (CNS) insults such as stroke, traumatic brain injury (TBI), and multiple sclerosis (MS) necessitates this personalized approach. Objectives & Scope: This narrative review synthesizes the current advancements and critical challenges across three interconnected domains: neurobiological biomarkers (electrophysiological, molecular, and digital), advanced neuroimaging (Diffusion Tensor Imaging, functional Magnetic Resonance Imaging, and connectomics), and computational intelligence (Artificial Intelligence [AI] and causal modeling). Synthesis Approach: The methodology involved a systematic synthesis of high-quality literature and key conceptual frameworks, consistent with the transparency guidelines of PRISMA 2020 , to build a comprehensive framework focused on multi-modal data fusion. This approach specifically analyzed the application of sophisticated causal models (Digital Twins) to identify the Optimal Dynamic Treatment Regimens (ODTR). Key Findings & Gaps: The synthesis confirms the predictive value of key neurobiological markers (e.g., EEG signatures, Neurofilament Light Chain [NFL]) and highlights the crucial role of AI in transforming prognostic data into prescriptive therapeutic policy. However, significant translational gaps persist, including the fragmentation of clinical data, the urgent need for standardized intervention documentation, and critical ethical and equity concerns. Algorithmic bias in deep learning models limits generalizability, and the governance of adaptive, real-time closed-loop systems demands reflexive ethical oversight that extends well beyond current procedural compliance. Conclusion: Future success hinges on adopting unified research frameworks and prioritizing the equitable, mechanism-driven development of neuroadaptive rehabilitation systems.