Electroencephalography-based parietofrontal connectivity modulated by electroacupuncture for predicting upper limb motor recovery in subacute stroke.

Abstract

Predicting motor recovery in stroke patients is essential for effective rehabilitation planning and goal setting. However, intervention-specific biomarkers for such predictions are limited. This study investigates the potential of electroacupuncture (EA) - induced brain network connectivity as a prognostic biomarker for upper limb motor recovery in stroke. A randomized crossover and prospective observational study was conducted involving 40 stroke patients within 30 days of onset. Patients underwent both EA and sham electroacupuncture (SEA) interventions. Simultaneously, resting electroencephalography signals were recorded to assess brain response. Patients' motor function was monitored for 3 months and categorized into Poor and proportional (Prop) recovery groups. The correlations between the targeted brain network of parietofrontal (PF) functional connectivity (FC) during the different courses of the 2 EA interventions and partial least squares regression models were constructed to predict upper limb motor recovery. Before the EA intervention, only ipsilesional PF network FC in the beta band correlated with motor recovery (r = -0.37, P = .041). Post-EA intervention, significant correlations with motor recovery were found in the beta band of the contralesional PF network FC (r = -0.43, P = .018) and the delta and theta bands of the ipsilesional PF network FC (delta: r = -0.59, P = .0004; theta: r = -0.45, P = .0157). No significant correlations were observed for the SEA intervention (all P > .05). Specifically, the delta band ipsilesional PF network FC after EA stimulation significantly differed between Poor and Prop groups (t = 3.474, P = .002, Cohen's d = 1.287, Poor > Prop). Moreover, the partial least squares regression model fitted after EA stimulation exhibited high explanatory power (R2 = 0.613), predictive value (Q2 = 0.547), and the lowest root mean square error (RMSE = 0.192) for predicting upper limb proportional recovery compared to SEA. EA-induced PF network FC holds potential as a robust prognostic biomarker for upper limb motor recovery, providing valuable insights for clinical decision-making.