Deep Brain Stimulation Neural Recordings of Varied Stimulation During Sleep in Parkinson's Disease

Deep brain stimulation (DBS) stands as an established and robust treatment for various motor symptoms in patients with Parkinson's disease (PWP). While it has shown promise in ameliorating non-motor symptoms, the mechanisms underlying these improvements remain poorly understood. A significant forthcoming shift in the DBS landscape is the transition towards closed-loop or "adaptive DBS" (aDBS). This approach relies on expanding knowledge of basal ganglia electrophysiology and its correlation with motor symptoms. Augmented beta frequency oscillations (13-35 Hz) in local field potentials (LFP) from the basal ganglia is correlated with severity of the motor systems bradykinesia/rigidity and serve as an electrophysiological biomarker for clinical state. Essentially, aDBS aims to modulate stimulation in response to neural state, offering more precise symptom control.

Sleep disturbances are a prevalent symptom in PWP, affecting a vast majority of patients, and serve as a significant non-motor contributor to quality of life. While DBS has demonstrated benefits in enhancing sleep efficiency and architecture, the mechanisms by which this might occur, as well as the optimal stimulation parameters for treating sleep dysfunctions are unknown. Sleep is associated with a dramatic change in subcortical neural activity compared to the wake state, with decreased beta activity, which could serve as a neurophysiological biomarker for the sleep state. Since beta frequencies are a common target for adaptive DBS studies in PD, addressing sleep-induced reductions in beta activity will be crucial for future algorithm development. Incorrectly interpreting sleep as the "medication-on" state may result in an adaptive algorithm providing the patient with non-optimal stimulation amplitudes that may adversely affect sleep.

There is an urgent need to identify the dose-response curve regarding how stimulation affects sleep quality and neurophysiology. Our primary objective is to address this knowledge gap by obtaining a comprehensive understanding of the subcortical neural signatures of sleep, and their correlation with sleep outcomes under different stimulation currents. This will ultimately enable us to establish the control policy for adaptive control of stimulation amplitude (current). Our central hypothesis is that different stimulation currents will elicit distinct effects on sleep subcortical neural signatures and sleep quality.

• Average Nightly Beta Band Power [Time frame: At the end of each 2-week stimulation period (over 6 weeks total)]
• Correlation Between Sleep Efficiency and Beta Band Power [Time frame: Over each night during the 6-week study period]
• Correlation of Wake After Sleep Onset (WASO) with Beta Band Power [Time frame: Across 6-week study period and at the end of each 2-week stimulation phase]
• Coherence Across Deep Brain Stimulation Amplitude Settings [Time frame: At the end of each 2-week stimulation period (over 6 weeks total)]
• Correlation of sleep fragmentation index with Beta Band Power [Time frame: Across 6-week study period and at the end of each 2-week stimulation phase]
• Correlation of Pittsburgh Sleep Quality Index (PSQI) with Beta Band Power [Time frame: Across 6-week study period and at the end of each 2-week stimulation phase]
• Correlation of and Epworth Sleepiness Scale (ESS) with Beta Band Power [Time frame: Across 6-week study period and at the end of each 2-week stimulation phase]