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The goal of this clinical trial is to learn whether local field potential (LFP) signals recorded from the globus pallidus interna (GPi) using the Medtronic Percept™ deep brain stimulation (DBS) device can help optimize DBS programming for people with dystonia. The study will also explore whether LFP patterns can serve as a biomarker of disease activity and predict treatment response.
The main questions it aims to answer are:
Do LFP peaks in the alpha-theta range reliably correlate with dystonia severity and clinical characteristics? Can LFP-based programming achieve similar or better clinical outcomes compared to traditional programming methods? How do LFP profiles change with stimulation and other treatments such as botulinum toxin or oral medications?
Researchers will compare two programming approaches:
Traditional programming based on clinical assessment and imaging. LFP-guided programming based on the site and characteristics of LFP peaks
Participants will:
Undergo DBS surgery for dystonia as part of standard clinical care. Attend regular follow-up visits for DBS programming and outcome assessments. Complete clinical rating scales for dystonia severity, quality of life, cognition, and mood.
Take part in neurophysiological assessments, including surface EMG, EEG, and reaction time tasks.
Have LFP recordings collected using the Medtronic Percept™ device during clinic visits and, where possible, at home using device sensing features.
This study will help determine whether LFP analysis can shorten the time to optimal DBS settings and improve outcomes for people with dystonia.
Full description
Dystonia is a disabling movement disorder characterized by sustained or intermittent muscle contractions that cause abnormal postures and movements. Deep brain stimulation (DBS) of the globus pallidus interna (GPi) is an established treatment for patients who do not respond adequately to first-line therapies such as botulinum toxin. However, clinical response to DBS in dystonia is highly variable, and optimization of stimulation settings often requires months of trial and error. This delay can prolong disability and increase healthcare burden.
Local field potentials (LFPs) are neural signals recorded from implanted DBS electrodes. In Parkinson's disease, LFP analysis has been used to guide programming and develop adaptive stimulation strategies. In dystonia, early studies suggest that low-frequency LFP peaks (typically in the alpha-theta range) may correlate with disease severity and optimal stimulation sites, but these findings have been limited to short-term recordings using externalized leads. The Medtronic Percept™ DBS system now allows chronic sensing of LFPs during routine clinical care, creating an opportunity to validate these observations and assess their clinical utility.
This single-site, prospective study will evaluate whether LFP profiles recorded from the GPi can guide DBS programming in patients with primary dystonia. The study includes an internal pilot phase followed by two main cohorts. Cohort 2 will undergo traditional programming based on clinical assessment and imaging, with LFP recordings collected at each visit. Cohort 3 will use an LFP-guided approach, selecting contacts and stimulation parameters based on the site and characteristics of LFP peaks. Participants will be followed for up to 12 months, with blinded video assessments and standardized rating scales to compare outcomes between programming strategies. Neurophysiological measures such as surface electromyography (EMG), electroencephalography (EEG), and reaction time tasks will also be collected to explore mechanistic links between stimulation and motor control.
The study aims to determine whether LFP analysis can shorten the time to optimal DBS settings and improve clinical outcomes compared to traditional methods. If successful, this approach could inform future development of adaptive DBS systems for dystonia, reducing variability in care and improving patient quality of life.
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25 participants in 3 patient groups
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Central trial contact
David Ledingham, MA, MBBS
Data sourced from clinicaltrials.gov
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