A Neuro-Musculoskeletal Integrated Framework for Locomotor Recovery Post-Injury

Thesis Abstract

The study addresses the persistent gap between neural control and musculoskeletal mechanics in locomotor recovery following lower-limb injuries, where conventional rehabilitation often treats neural and mechanical factors in isolation. By proposing a Neuro-Musculoskeletal Integrated Framework, the research aims to advance understanding of how neural plasticity, sensorimotor integration, and biomechanical constraint interact to shape functional locomotion, and to identify modifiable targets for optimized rehabilitation. The objectives are to (1) delineate the interdependencies between cortical–subcortical drive, spinal reflex modulation, and lower-limb biomechanics during gait recovery; (2) develop a consolidated theoretical model that integrates motor control theories (dynamic systems theory and optimal feedback control) with musculoskeletal dynamics; (3) evaluate the predictive value of the framework for locomotor outcomes across injury severities; and (4) generate evidence-based recommendations for rehabilitation protocols that concurrently address neural and musculoskeletal restoration. The methodology adopts a mixed-methods sequential explanatory design. In the quantitative phase, a cohort of 120 adults aged 18–65 with unilateral ankle or knee injuries undergoing physical rehabilitation will be followed for 12 weeks post-injury. A stratified sampling strategy ensures balanced representation of injury types and severities. Data collection instruments include gait analysis using 3D motion capture and instrumented treadmill, surface electromyography (EMG) of major lower-limb muscles, transcranial magnetic stimulation (TMS) to assess corticospinal excitability, and comprehensive clinical measures (Timed Up and Go, gait-speed, two-minute walk test). Instrument validity and reliability will be established through pilot testing (Cronbach’s alpha >0.80 for composite motor function scales, intraclass correlation coefficients >0.75 for gait metrics). Data will be analyzed using multivariate regression to identify predictors of locomotor recovery, structural equation modeling to test the integrated framework, and time-series analyses to capture dynamic changes in neural–musculoskeletal coupling. The qualitative phase will involve semi-structured interviews with 30 participants and 12 rehabilitation clinicians to explore perceived barriers and facilitators to integration of neural and mechanical rehabilitation. Thematic analysis will be conducted following Braun and Clarke’s approach, with coding triangulated against quantitative findings to enhance the framework’s explanatory power. The theoretical underpinning combines dynamic systems theory, which emphasizes multi-component interactions and nonlinear recovery trajectories, with the Theory of Motor Control (optimal feedback control), to articulate how the nervous system optimizes control under changing musculoskeletal states. Expected findings include (a) empirical evidence of synchronized adaptation between corticospinal excitability and joint kinematics during progression through rehabilitation; (b) identification of distinct neuro-mechanical coupling profiles associated with fast versus slow recovery trajectories; (c) validation of the integrated framework’s predictive model for functional gait outcomes, with acceptable fit indices (CFI >0.90, RMSEA <0.08) across injury subgroups; and (d) practical rehabilitation indicators—such as targeted neuromuscular training regimens and timing of gait-specific loading—that enhance both neural recovery and biomechanical efficiency. The study contributes to knowledge by offering a theoretically grounded, operational framework that unites neural and musculoskeletal domains into a single model of locomotor recovery, provides validated measurement pathways for neural–musculoskeletal interactions, and informs evidence-based rehabilitation protocols that harmonize neural training with mechanical loading. The anticipated conclusion posits that synchronized neuromodulatory and biomechanical interventions yield superior locomotor outcomes compared with siloed approaches, with implications for personalized rehabilitation planning. Recommendations include integrating neurostimulation modalities and task-specific gait training within a progressive, feedback-driven program, standardizing assessment of neural–mechanical coupling in clinical practice, and pursuing longitudinal studies to examine long-term maintenance of recovered function.