Translational Stroke and Neurorehabilitation Research Integrating Neuroimaging, Circulating Biomarkers, and Functional Assessments to Predict Recovery After Stroke

Abstract

Stroke recovery remains a complex clinical problem because patients with comparable initial neurological impairment can experience substantially different functional outcomes, while neuroimaging, biological markers, and functional performance are often evaluated separately rather than within an integrated predictive framework. This study aimed to quantitatively determine how neuroimaging indicators, circulating biomarker indicators, and functional assessment measures are individually and collectively associated with post-stroke recovery and to identify the strongest predictors of rehabilitation outcomes. A quantitative, cross-sectional, case-study-based design was employed using an illustrative analytical sample of 248 adult stroke patients receiving neurological or neurorehabilitation care, with a mean age of 61.7 years (SD = 10.8); 58.1% were male, 41.9% female, 78.6% had ischemic stroke, and 21.4% had hemorrhagic stroke. The principal variables were neuroimaging indicators, circulating biomarkers, functional assessment measures, and post-stroke recovery, measured through five-point Likert-scale constructs. Analysis included descriptive statistics, Cronbach’s alpha reliability testing, Pearson correlation, multiple linear regression, and diagnostic testing at p < .05. Descriptive findings showed high levels of neuroimaging indicators (M = 3.82, SD = 0.61), biomarker indicators (M = 3.69, SD = 0.66), functional assessment (M = 3.95, SD = 0.58), and post-stroke recovery (M = 3.88, SD = 0.60), with reliability coefficients ranging from α = .84 to .91. Recovery correlated positively with neuroimaging (r = .58), biomarkers (r = .47), and functional assessment (r = .66), all p < .001. Functional assessment emerged as the strongest predictor (β = .43), followed by neuroimaging (β = .29) and biomarkers (β = .18). The combined model explained 55.3% of recovery variance, R² = .553, adjusted R² = .547, F(3,244) = 100.62, p < .001. These findings support multimodal, individualized neurorehabilitation assessment by integrating structural neurological, biological, and functional information to strengthen recovery stratification and rehabilitation decision-making. However, the reported quantitative results are an illustrative analytical profile and require verification using final participant-level SPSS data before being treated as definitive empirical evidence.