Decoding Sleep Architecture from Ambulatory Basal Ganglia Signals in Parkinson's Disease

BACKGROUND: Sleep architecture and circadian rhythms are frequently disrupted in Parkinson's disease (PD). BrainSense-enabled neurostimulators combined with wearable technology enable chronic assessment of nocturnal brain activity, with potential for future diagnostics and personalized treatments. OBJECTIVES: To neurophysiologically characterize and decode sleep architecture and circadian rhythmicity from ambulatory subthalamic nucleus (STN) recordings in PD and to evaluate the influence of clinical factors. METHODS: Eighteen PD patients implanted with the Medtronic Percept system underwent 4-8 weeks of ambulatory STN local field potential recordings, alongside wearable-based sleep monitoring. Spectral dynamics of three biomarkers (low-frequency-, beta-, and finely-tuned-gamma [FTG]-activity) were characterized across circadian cycles and sleep stages (Awake, Core, Deep, REM [rapid eye movement]). Machine-learning classifiers were developed for state decoding. RESULTS: A total of 3140 hr of representative sleep data were analyzed. All biomarkers exhibited circadian modulation, most evident in beta and FTG activity. REM sleep and nocturnal wakefulness were associated with increased beta/FTG, whereas Deep sleep showed increased low-frequency and reduced beta/FTG. Classifiers showed that beta and FTG decoded circadian states, while low-frequency identified Deep sleep. Bilateral biomarker combination improved prediction. Clinically, greater motor impairment correlated with reduced REM beta power. Levodopa dosage and electrocardiogram artefacts influenced beta-based predictions, whereas age and sleep quality affected FTG-related predictions. CONCLUSIONS: This proof-of-concept study demonstrates that ambulatory basal ganglia recordings from implantable neurostimulators can capture key aspects of sleep stage architecture in PD. Distinct spectral biomarkers show differential sleep insights and practical utility, supporting the development of diagnostic tools and sleep-informed adaptive deep brain stimulation. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.