Clinical Trial Details
— Status: Active, not recruiting
Administrative data
| NCT number |
NCT04620551 |
| Other study ID # |
0120-20-FB |
| Secondary ID |
|
| Status |
Active, not recruiting |
| Phase |
N/A
|
| First received |
|
| Last updated |
|
| Start date |
October 21, 2020 |
| Est. completion date |
June 29, 2024 |
Study information
| Verified date |
January 2024 |
| Source |
University of Nebraska |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Interventional
|
Clinical Trial Summary
Parkinson's disease (PD) is a neurodegenerative disorder that leads to both motor and
non-motor symptoms. Therapies have been developed that effectively target the motor symptoms.
Non-motor symptoms are far more disabling for patients, precede the onset of motor symptoms
by a decade, are more insidious in onset, have been less apparent to clinicians, and are less
effectively treated. Sleep dysfunction is oftentimes the most burdensome of the non-motor
symptoms. There are limited options for treating sleep dysfunction in PD, and the mainstay of
therapy is the use of sedative-hypnotic drugs without addressing the underlying mechanisms.
Patients with PD who demonstrate significant motor fluctuations and dyskinesia are considered
for subthalamic nucleus (STN) deep brain stimulation (DBS) surgery. Several studies have
reported that STN-DBS also provides benefit for sleep dysregulation. Additionally, local
field potentials recorded from STN DBS electrodes implanted for the treatment of PD, have led
to the identification of unique patterns in STN oscillatory activity that correlate with
distinct sleep cycles, offering insight into sleep dysregulation. This proposal will leverage
novel investigational DBS battery technology (RC+S Summit System; Medtronic) that allows the
exploration of sleep biomarkers and prototyping of closed-loop stimulation algorithms, to
test the hypothesis that STN contributes to the regulation and disruption of human sleep
behavior and can be manipulated for therapeutic advantage. Specifically, in PD patients
undergoing STN-DBS, the investigators will determine whether STN oscillations correlate with
sleep stage transitions, then construct and evaluate sensing and adaptive stimulation
paradigms that allow ongoing sleep-stage identification, and induce through adaptive
stimulation an increase in duration of sleep stages associated with restorative sleep.
Description:
Although STN-DBS is routinely used to treat PD motor symptoms, several studies have reported
that STN-DBS also provides benefit for sleep dysregulation through normalization of sleep
architecture. In our previous work, using local field potentials (LFP) recorded from STN DBS
electrodes implanted for the treatment of PD, unique spectral patterns in STN oscillatory
activity were identified that correlated with distinct sleep cycles, offering insight into
sleep dysregulation. These findings were used to construct an Artificial Neural Network (ANN)
that can accurately predict sleep stage. Building on this work with the use of new DBS
battery technology that allows exploration of potential biomarkers and prototyping of
closed-loop algorithms, the investigators will test the hypothesis that STN-a highly
interconnected node within the basal ganglia- contributes to the regulation and disruption of
human sleep behavior and can be manipulated for therapeutic advantage.
This is the first part, Aim 1, of a two-part study. Investigators will enroll 20 subjects for
Aim 1 of this study and 20 subjects for Aim 2, with 10 subjects enrolled at each clinical
site for each aim (University of Nebraska Medical Center and Stanford University Medical
Campus). In Aim 1, subjects will undergo standard-of-care STN DBS lead implantation surgery
for the treatment of PD. They will return 3 weeks later to the in-patient Sleep Lab for 3
nights of STN LFP recordings with concurrent PSG, EMG, EOG, actigraphy, and video-EEG. The
first two nights of recording will be used to establish a physiological sleep baseline for
each patient. The third night of recording will involve sub-clinical thresholds of
stimulation in all subjects, in an effort to favorably alter sleep-stage duration, so that
NREM and REM-3 are prolonged. As a secondary outcome, subjects will be asked to complete a
sleep questionnaire for all three nights, sleep during which stimulation occurred will be
compared to the preceding two nights. Data collected during all three nights of recordings
will be used to predict sleep stage identity from the LFPs recorded within STN, with the
ground truth for each sleep stage provided by sleep-expert evaluated PSG. These data will
also be used to identify the optimal sub-clinical threshold current amplitude and sleep-stage
timing for adaptive stimulation to improve sleep. The stimulation algorithm developed in Aim
1 will be implemented in the second part of the study, Aim 2, to provide adaptive stimulation
to subjects during nighttime sleep, over the course of 3 weeks of in-home sleep.
