ADAPTIVE DEEP BRAIN STIMULATION FOR PARKINSON’S DISEASE: NEURAL BIOMARKERS, GAIT-SYNCHRONIZED CONTROL, AND PATIENT-SPECIFIC NETWORK NEUROMODULATION
AbstractBackground. Conventional deep brain stimulation provides continuous therapy for Parkinson’s disease, but fixed stimulation cannot accommodate medication cycles, sleep–wake transitions, gait freezing, dyskinesia, or biomarker drift. Adaptive deep brain stimulation offers closed- loop neuromodulation by adjusting stimulation according to sensed neural or behavioral signals.Materials and methods. A structured narrative review used randomized and nonrandomized trials, prospective cohorts, documents, neurophysiological studies, and investigations published through July 2026. Evidence was organized by biomarker validity, control architecture, programming feasibility, effectiveness, safety, energy efficiency, generalizability, and human-factor integration. A multicenter crossover study is proposed for adults with levodopa-responsive Parkinson’s disease and motor fluctuations despite optimized conventional stimulation.Results. Subthalamic beta amplitude and beta-burst duration remain the most mature control variables, whereas stimulation-entrained gamma activity, cortical signals, wearable-derived gait events, and multimodal decoders may better capture dyskinesia, freezing, and naturalistic behavior. Chronic studies suggest that personalized adaptive stimulation can improve residual motor symptoms and quality of life, while gait-synchronized and activity-dependent paradigms may address axial disability. Major limitations include sensing artifacts, unstable biomarkers, heterogeneous programming, small samples, insufficient blinding, and limited evidence regarding cognition, speech, falls, and device burden. The proposed primary endpoint combines blinded motor-state improvement with reduced troublesome dyskinesia and off time. Secondary endpoints include falls, gait freezing, speech, cognition, quality of life, stimulation energy, programming time, adverse events, calibration, and subgroup performance.Conclusion. Adaptive deep brain stimulation is transitioning from experimental physiology to regulated clinical therapy. Its durable value will depend on biomarker personalization, transparent algorithms, standardized outcomes, and independent multicenter validation.Keywords: Parkinson’s disease, adaptive deep brain stimulation, closed-loop neuromodulation, beta oscillations, local field potentials, gait freezing, neural biomarkers, personalized neurostimulation.