Understanding how brain stimulation affects sleep

Parkinson's disease (PD) is a prevalent neurodegenerative disorder characterized by both motor and non-motor symptoms. Subthalamic nucleus (STN) deep brain stimulation (DBS) effectively alleviates motor symptoms; however, non-motor symptoms such as sleep disorders significantly impair patients' quality of life. While preliminary evidence suggests DBS may improve sleep, the underlying neural mechanisms and long-term effects on sleep regulation remain poorly elucidated. This study employs a wearable polysomnography (PSG) device to investigate how STN-DBS modulates sleep architecture, local field potentials (LFPs), and clinical outcomes in PD patients. We hypothesize that STN-DBS contributes to both the regulation and disruption of normal sleep behavior. By synchronizing PSG with STN-LFP recordings under DBS-ON and DBS-OFF conditions, we aim to decode sleep stages using STN-LFPs, characterize PD-specific sleep abnormalities (e.g., sleep architecture, atonia), and analyze DBS-induced LFP changes and their correlations with clinical efficacy. According to the above data, the sleep stage characteristics were decoded, and the algorithm was used to determine the optimal clinical threshold current amplitude to improve sleep. Then, the patients will be given adaptive stimulation, and the sleep quality was monitored to verify its efficacy.This study will provide a neurophysiological foundation for developing closed-loop stimulation strategies targeting sleep dysfunction in PD.

This single-center longitudinal observational study will enroll 20 idiopathic Parkinson's disease (PD) patients with bilateral subthalamic nucleus (STN) deep brain stimulation (DBS) systems (Medtronic Percept™ PC) to evaluate the neurophysiological mechanisms of DBS in sleep regulation. Participants will undergo preoperative clinical assessments (MDS-UPDRS III for motor symptoms, NMSS for non-motor symptoms, PDSS for sleep-specific dysfunction) and two nights of wearable PSG recordings. Postoperatively, DBS parameters will be optimized at 1 month for motor symptom control. Follow-up evaluations at 3, 6, and 12 months post-operation include in-hospital PSG and local field potential (LFP) recordings: Night 1 captures data under DBS-OFF conditions, followed by Night 2 with DBS-ON under optimized programming, alongside repeated clinical assessments. Sleep architecture (NREM/REM stages, arousal indices,atonia) and STN-LFPs will be analyzed and correlated with clinical outcomes. Machine learning models will identify LFP biomarkers predictive of sleep improvement to inform closed-loop stimulation strategies. Based on the machine learning results, we will investigate the adaptive algorithm and validate its effectiveness in the second phase. Adaptive stimulation will be administered for one month, followed by two consecutive nights of polysomnography (PSG) monitoring and Parkinson's Disease Sleep Scale (PDSS) assessments at the study interval endpoint. Subsequently, patients will undergo routine open-loop stimulation for one month, with two additional consecutive nights of PSG monitoring and PDSS evaluations conducted upon completion of this phase. Sleep improvement outcomes will be systematically compared between the two stimulation modalities.