Automated Daytime Adaptive Deep Brain Stimulation Parameter Optimization in Patients Implanted With Percept Neurostimulator

Recruiting now · Not applicable

Conditions studied: PD - Parkinson's Disease

In brief

Parkinson's disease (PD) affects \~1% of people over 60 years old, is highly disabling and represents a large economic burden. While dopaminergic medications effectively treat motor symptoms early in the disease, most patients develop complications, including motor fluctuations and dyskinesias, which can be partially managed by deep brain stimulation (DBS). This surgical therapy consists of delivering continuous electrical stimulation through electrodes permanently implanted in basal ganglia nuclei, with a pulse generator and battery unit implanted in the chest. However, conventional DBS therapy is delivered with constant stimulation parameters, referred to as constant deep brain stimulation (cDBS), that are unresponsive to patient activities or to variations in the severity of symptoms during daily life. This leaves many patients under- or over-stimulated during parts of the day. To address the shortcomings of cDBS, adaptive DBS (aDBS) uses real-time detection of neural signals to automatically adjust stimulation amplitude or other parameters in response to patients' dynamic clinical needs. aDBS was approved by the U.S. Food and Drugs Administration (FDA) for clinical treatment of PD in the Percept PC and RC (Medtronic) device in February 2025. Fully leveraging this therapy in the real world is limited by technical challenges, in particular the fact that: while the investigators developed a consistent pipeline for implementing aDBS, there were several critical control parameters that strongly influenced algorithm performance and required prolonged trial-and-error based testing, to achieve successful control. In this new study, the investigators seek to significantly extend this work and address the major barriers to widespread, easy adoption of aDBS by groups without specialized knowledge of neurophysiology or feedback control. Here the investigators aim to test an automated, data-driven pipeline for the recommendation of the adaptive control parameters.

Key facts

Study ID: NCT07682311
Run by: University of California, San Francisco
People needed: 40
Starts: 2026-06-01
Expected to finish: 2032-05-01
Last updated by the study team: 2026-07-02

Who can join

Age: 25 and older, up to 75. Sex: any. Healthy volunteers: not accepted.

You may qualify if…

Age 25-75.
Diagnosis of idiopathic PD.
Patient has undergone surgical intervention with deep brain stimulation (Percept device) for their disorder at least 2 months prior to recruitment.
Absence of significant cognitive impairment (score of 24 or greater on the Montreal Cognitive Assessment (MoCA)).
Signed informed consent.
Ability to comply with study follow-up visits for brain recording, testing of adaptive stimulation, and clinical assessment.
Patient has been on cDBS for at least two months and still experiences significant residual motor fluctuation while on cDBS. Consequently, patient is undergoing aDBS treatment currently or was recommended aDBS as part of clinical care to manage their residual motor fluctuation.
Has available cDBS Timeline LFP and stimulation data lasting at least 1 day.
To be enrolled in the clinical trial phase of the study, patient will need to consent to reverting back to their baseline cDBS settings for all required cDBS testings.

You may not qualify if…

Patient meets criteria for a psychogenic movement disorder.
Significant untreated depression (BDI-II score >20). History of suicidal attempt or active suicidal ideation (Yes to #2-5 on C-SSRS).
Any personality or mood symptoms that study personnel believe will interfere with study requirements.

Where it is running

University of California San Francisco — San Francisco, California, United States (enrolling)

Full record on ClinicalTrials.gov

Trial information comes from ClinicalTrials.gov and is refreshed daily. TrialsForMe does not provide medical care and does not run the studies it lists.