Evidence map›Paper›PMID 40002449›Full record

ReviewBrain sciences2025

The Place of Local Field Potentials in Deep Brain Stimulation Programming for Parkinson's Disease: A Review.

Chun Him Shelton Leung, Hugh D Simpson, Dominic Thyagarajan

Abstract readReview
In one paragraph

Review in Brain sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Review
  3. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Chun Him Shelton LeungDepartment of Neurology, The Alfred Hospital, Melbourne, VIC 3004, Australia.
Hugh D SimpsonDepartment of Neurology, The Alfred Hospital, Melbourne, VIC 3004, Australia.
Dominic ThyagarajanDepartment of Neurology, The Alfred Hospital, Melbourne, VIC 3004, Australia.ORCID 0000-0002-7766-4003

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background/Objections: The pharmacological management of Parkinson's Disease (PD) is often supplemented by deep brain stimulation (DBS) to tackle problems of advanced disease such as motor fluctuation, dyskinesias or medication-resistant tremor. DBS uses high-frequency stimulation with spatially distributed electrodes to produce electrical fields that influence neuronal networks. The programming of such stimulation is complex and time-consuming. Recent technological advancements have enabled DBS systems to record local field potentials (LFPs). In conjunction with biomarker discovery, such as beta oscillations, this shows promise in streamlining the DBS programming process. This review aims to synthesize the current literature investigating LFP characteristics in PD in order to understand the place of LFPs in assisting with DBS programming.

methodsA comprehensive literature search was conducted using databases including OVID MEDLINE, Scopus, and Cochrane Library, resulting in 738 identified articles; 122 studies remained after screening and 87 studies were selected for detailed analysis.

resultsAnalyzing LFPs clearly has the potential to assist or streamline DBS programming in clinical practice, but there are knowledge gaps and challenges to overcome, especially in the utilization of intraoperative LFPs.

conclusionsMore research is required to compare different algorithms that utilize LFPs in DBS programming to identify a simple, practical and time-saving algorithm incorporating reliable LFP biomarkers that will enhance the DBS programming experience for both patients and clinicians.

Indexed as

beta oscillationsdeep brain stimulationleads contact selection and programminglocal field potentialsParkinson’s disease

Identifiers

PMID40002449
PMCPMC11853521

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Registered trials

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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.