Parkinson Disease
Conditions
Brief summary
This study employs an exploratory, prospective, single center, naturalistic clinical trial design with a randomized crossover intervention.
Detailed description
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.
Interventions
OTHERNighttime Deep Brain Stimulation with Varying Amplitude Settings
Participants will undergo three different nighttime-only Deep Brain Stimulation (DBS) settings in a randomized crossover design: 0% amplitude (stimulation off), 50% amplitude of their optimal clinical DBS settings, and full clinical DBS settings (100%). Each setting is applied for two weeks during sleep, over a total six-week home monitoring period. The intervention is designed to assess how varying levels of subthalamic nucleus DBS influence sleep quality and neural oscillatory activity. Only nighttime DBS settings are modified; daytime settings remain unchanged.
Sponsors
The Cleveland Clinic
American Parkinson's Disease Association, Inc
Beacon Biosignals
Study design
Allocation
NA
Intervention model
SINGLE_GROUP
Primary purpose
SUPPORTIVE_CARE
Masking
NONE
Eligibility
Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No
Inclusion criteria
* Male or female \> 18 years of age * Diagnosed with idiopathic Parkinson Disease * Have bilateral implantation of Medtronic's PerceptTM PC (Medtronic Neurological Division, Minneapolis, MN, USA) Deep Brain Stimulation (DBS) system targeting the subthalamic nucleus (STN) and have had their stimulation settings optimized for a minimum of three months * Report a response score of one or greater on question 1.7 of the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) part I; * Demonstrate ability to ambulate independently; * Be capable of providing informed consent; * If taking sleep-supporting medications, dosing must be stable for at least 30 days
Exclusion criteria
* Presence of neurological diseases other than Parkinson Disease, such as stroke or multiple sclerosis * Active sleep disorders, including narcolepsy, moderate to severe (Apnea-Hypopnea Index\>=15) untreated sleep apnea, uncontrolled restless legs syndrome * Cognitive impairment meeting the criteria for dementia as per the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition (DSM-IV), based on formal neuropsychological evaluation * Ongoing alcohol or substance abuse; Working night shifts or irregular work hours The main concern for vulnerable subjects will be for the possibility of reduced decision-making capacity. For this we intend to exclude individuals who have a diagnosis of dementia.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| To demonstrate differences in sleep efficiency (SE) among different stimulation settings. | At the end of each 2-week stimulation period (over 6 weeks total) | Sleep efficiency will be calculated as: (total sleep time ÷ total time in bed) × 100, using nightly data collected via the Dreem Headband. The mean SE for each 2-week stimulation setting (0%, 50%, 100% amplitude) will be compared within subjects using crossover analysis. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Average Nightly Beta Band Power | At the end of each 2-week stimulation period (over 6 weeks total) | Average beta band power will be derived from subthalamic nucleus local field potentials recorded via the Medtronic Percept™ system. |
| Correlation Between Sleep Efficiency and Beta Band Power | Over each night during the 6-week study period | The correlation between sleep efficiency (measured by Dreem Headband) and beta band power (recorded from DBS electrodes) will be evaluated. Analyses will include full-night averages and specific sleep stages (N1/N2, N3). |
| Correlation of Wake After Sleep Onset (WASO) with Beta Band Power | Across 6-week study period and at the end of each 2-week stimulation phase | Wake after sleep onset (WASO) will be analyzed in relation to Deep Brain Stimulation (DBS) band power. Relationships will be examined across each stimulation condition and averaged over the study. |
| Coherence Across Deep Brain Stimulation Amplitude Settings | At the end of each 2-week stimulation period (over 6 weeks total) | Coherence between left and right subthalamic nucleus (STN) will be calculated. Comparisons will be made across the three stimulation settings |
| Correlation of sleep fragmentation index with Beta Band Power | Across 6-week study period and at the end of each 2-week stimulation phase | Sleep fragmentation index will be analyzed in relation to Deep Brain Stimulation (DBS) band power. Relationships will be examined across each stimulation condition and averaged over the study. |
| Correlation of Pittsburgh Sleep Quality Index (PSQI) with Beta Band Power | Across 6-week study period and at the end of each 2-week stimulation phase | Pittsburgh Sleep Quality Index (PSQI) will be analyzed in relation to Deep Brain Stimulation (DBS) band power. Relationships will be examined across each stimulation condition and averaged over the study. |
| Correlation of and Epworth Sleepiness Scale (ESS) with Beta Band Power | Across 6-week study period and at the end of each 2-week stimulation phase | Epworth Sleepiness Scale (ESS) will be analyzed in relation to Deep Brain Stimulation (DBS) band power. Relationships will be examined across each stimulation condition and averaged over the study. |
Countries
United States
Contacts
CONTACTSaar Anis, MD
Outcome results
None listed