Evidence map›Paper›PMID 35219571›Full record

ArticleNeuromodulation : journal of the International Neuromodulation Society2023

Combining Multimodal Biomarkers to Guide Deep Brain Stimulation Programming in Parkinson Disease.

Ashesh Shah, Thuy-Anh Khoa Nguyen, Katrin Peterman, Saed Khawaldeh, Ines Debove, Syed Ahmar Shah, Flavie Torrecillos, Huiling Tan, Alek Pogosyan, Martin Lenard Lachenmayer and 6 more

Registry-linked trialAbstract read
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In one paragraph

Article in Neuromodulation : journal of the International Neuromodulation Society, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07369310 (Randomized Trial on DBS Programming Based on Biomarkers vs. Standard Monopolar Review), which is not on this map. Cited by 28 papers.

0numbers the graph read from it
0cells of the map it votes in
28citing 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.

NCT07369310 nanot yet recruitingnot on this mapstarted 2026, after this paper: background citation

Randomized Trial on DBS Programming Based on Biomarkers vs. Standard Monopolar Review

TypeinterventionalSponsorFundació Institut de Recerca de l'Hospital de la Santa Creu i Sant PauRan2026 to 2028Enrolled20ConditionsParkinson's Disease, Parkinson, Parkinsons Disease (PD), Motor FluctuationsArmsConventional DBS Programming (Monopolar Review), Objective-Guided DBS Programming (Imaging and Local Field Potential-Based)
3 · Its place in the literature

Who cites it

28 citing papers in PubMed.

  1. Article
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  4. Review
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  10. Review
  11. Article
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  14. Paradoxical Modulation of STN β-Band Activity with Medication Compared to Deep Brain Stimulation.Movement disorders : official journal of the Movement Disorder Society · 2024
    Article
  15. Article
  16. Clinical neurophysiological interrogation of motor slowing: A critical step towards tuning adaptive deep brain stimulation.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2023
    Article
  17. Spectral Topography of the Subthalamic Nucleus to Inform Next-Generation Deep Brain Stimulation.Movement disorders : official journal of the Movement Disorder Society · 2023
    Article
  18. Article
  19. Adaptive Stimulations in a Biophysical Network Model of Parkinson's Disease.International journal of molecular sciences · 2023
    Article
  20. Deep Brain Stimulation: When to Test Directional?Movement disorders clinical practice · 2023
    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

16 authors.

Ashesh ShahDepartment of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.
Thuy-Anh Khoa NguyenDepartment of Neurosurgery, Bern University Hospital, University of Bern, Bern, Switzerland; ARTORG Center for Biomedical Engineering Research, University of Bern, Bern, Switzerland.
Katrin PetermanDepartment of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.
Saed KhawaldehMRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, University of Oxford, Oxford, UK.
Ines DeboveDepartment of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.
Syed Ahmar ShahUsher Institute, Edinburgh Medical School, The University of Edinburgh, Edinburgh, UK.
Flavie TorrecillosMRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Huiling TanMRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Alek PogosyanMRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Martin Lenard LachenmayerDepartment of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.
Joan MichelisDepartment of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.
Peter BrownMRC Brain Network Dynamics Unit, University of Oxford, Oxford, UK; Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
Claudio PolloDepartment of Neurosurgery, Bern University Hospital, University of Bern, Bern, Switzerland.
Paul KrackDepartment of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland.
Andreas NowackiDepartment of Neurosurgery, Bern University Hospital, University of Bern, Bern, Switzerland.
Gerd TinkhauserDepartment of Neurology, Bern University Hospital, University of Bern, Bern, Switzerland. Electronic address: gerd.tinkhauser@insel.ch.

Funding

Medical Research Council G0901503Medical Research Council MC_UU_00003/2Medical Research Council MC_UU_12024/1Medical Research Council MR/P012272/1Medical Research Council MR/V00655X/1
6 · The paper itself

Abstract

backgroundDeep brain stimulation (DBS) programming of multicontact DBS leads relies on a very time-consuming manual screening procedure, and strategies to speed up this process are needed. Beta activity in subthalamic nucleus (STN) local field potentials (LFP) has been suggested as a promising marker to index optimal stimulation contacts in patients with Parkinson disease.

objectiveIn this study, we investigate the advantage of algorithmic selection and combination of multiple resting and movement state features from STN LFPs and imaging markers to predict three relevant clinical DBS parameters (clinical efficacy, therapeutic window, side-effect threshold). MATERIALS AND

methodsSTN LFPs were recorded at rest and during voluntary movements from multicontact DBS leads in 27 hemispheres. Resting- and movement-state features from multiple frequency bands (alpha, low beta, high beta, gamma, fast gamma, high frequency oscillations [HFO]) were used to predict the clinical outcome parameters. Subanalyses included an anatomical stimulation sweet spot as an additional feature.

resultsBoth resting- and movement-state features contributed to the prediction, with resting (fast) gamma activity, resting/movement-modulated beta activity, and movement-modulated HFO being most predictive. With the proposed algorithm, the best stimulation contact for the three clinical outcome parameters can be identified with a probability of almost 90% after considering half of the DBS lead contacts, and it outperforms the use of beta activity as single marker. The combination of electrophysiological and imaging markers can further improve the prediction.

conclusionLFP-guided DBS programming based on algorithmic selection and combination of multiple electrophysiological and imaging markers can be an efficient approach to improve the clinical routine and outcome of DBS patients.

Indexed as

Deep Brain StimulationParkinson DiseaseSubthalamic NucleusBiomarkersHumansMovementTreatment OutcomeBiomarkersDBS programmingdeep brain stimulationlocal field potentialsParkinson diseasesubthalamic nucleus

Identifiers

PMID35219571
PMCPMC7614142

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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.