ArticleNPJ Parkinson's disease2025
Online prediction of optimal deep brain stimulation contacts from local field potentials in Parkinson's disease.
Article in NPJ Parkinson's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Matters arising: bridging predictive algorithms and clinical practice in DBS contact selection.NPJ Parkinson's disease · 2026Article
- Reply to: Bridging predictive algorithms and clinical practice in DBS contact selection.NPJ Parkinson's disease · 2026Article
- Identifying maximal beta power from directional subthalamic local field potentials in Parkinson's disease.NPJ Parkinson's disease · 2026Article
- Pallidal alpha activity is an electrophysiological biomarker of symptom severity in obsessive-compulsive disorder.Research square · 2026Article
- Beta-Band and Aperiodic Activity Across New and Chronic Subthalamic Nucleus Deep Brain Stimulation in Parkinson's Disease.Movement disorders : official journal of the Movement Disorder Society · 2026Article
- Chronic adaptive deep brain stimulation in Parkinson's disease: ADAPT-START findings and programming principles.NPJ Parkinson's disease · 2026Article
- Clinical utility of evoked potentials for programming subthalamic deep brain stimulation in Parkinsons disease.NPJ Parkinson's disease · 2026Review
Corrections and comments
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Authors and funding
16 authors.
Funding
Abstract
Selecting optimal contacts for chronic deep-brain stimulation (DBS) requires a monopolar review, involving time-consuming manual testing by trained personnel, often causing patient discomfort. Neural biomarkers, such as local field potentials (LFP), could streamline this process. This study aimed to validate LFP recordings from chronically implanted neurostimulators for guiding clinical contact-level selection. We retrospectively analysed bipolar LFP recordings from Parkinson's disease patients across three centres (Netherlands: 68, Switzerland: 21, Germany: 32). Using beta-band power measures (13-35 Hz), we ranked channels based on clinical contact-level choices and developed two prediction algorithms: (i) a "decision tree" method for in-clinic use and (ii) a "pattern based" method for offline validation. The "decision tree" method achieved accuracies of 86.5% (NL), 86.7% (CH), and 75.0% (DE) for predicting the top two contact-levels. Both methods outperformed an existing algorithm. These findings suggest LFP-based approaches can enhance DBS programming efficiency, potentially reducing patient burden and clinical workload.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.