Evidence map›Paper›PMID 23852650›Full record

ArticleAnnals of neurology2013

Adaptive deep brain stimulation in advanced Parkinson disease.

Simon Little, Alex Pogosyan, Spencer Neal, Baltazar Zavala, Ludvic Zrinzo, Marwan Hariz, Thomas Foltynie, Patricia Limousin, Keyoumars Ashkan, James FitzGerald and 3 more

4 registry-linked trialsAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of neurology, 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 4 registered trials, which are not on this map. Cited by 605 papers, 7 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
605citing papers in PubMed, 7 pooled it
–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.

NCT02585154 nacompletednot on this mapstarted 2016, after this paper: background citation

Studying Movement Control in Parkinson's Disease Using Closed Loop Deep Brain Stimulation

TypeinterventionalSponsorUniversity of OxfordRan2016 to 2019Enrolled30ConditionsParkinson DiseaseArmsDeep Brain Stimulation applied through custom built external stimulator (Little et al., 2013, 2015), closed-loop Deep Brain Stimulation applied through custom built external stimulator (Little et al., 2013, 2015), Deep Brain Stimulation off, local field potentials and electroencephalography recordings
NCT03079960 naunknown statusnot on this mapstarted 2017, after this paper: background citation

Data-Driven Characterization of Neuronal Markers During Deep Brain Stimulation for Patients With Parkinson's Disease

TypeinterventionalSponsorProf. Dr. Volker Arnd CoenenRan2017 to 2021Enrolled120ConditionsParkinson DiseaseArmsElectrophysiological recording and measurement devices
NCT04675398 naactive not recruitingnot on this mapstarted 2021, after this paper: background citation

Adaptive Cortical and Subcortical Brain Stimulation to Improve Motor Behaviors and Gait in Parkinson's Disease

TypeinterventionalSponsorDoris Wang, MD, PhDRan2021 to 2030Enrolled10ConditionsParkinson DiseaseArmsSummit RC+S
NCT07424508 naenrolling by invitationnot on this mapstarted 2026, after this paper: background citation

Movement State-Dependent Adaptive Deep Brain Stimulation for Parkinson's Disease

TypeinterventionalSponsorDoris Wang, MD, PhDRan2026 to 2030Enrolled6ConditionsParkinson's Disease (PD)ArmsSummit RC+S
3 · Its place in the literature

Who cites it

605 citing papers in PubMed, 7 syntheses or guidelines pooled it.

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545 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

13 authors.

Simon LittleNuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford.
Alex Pogosyan
Spencer Neal
Baltazar Zavala
Ludvic Zrinzo
Marwan Hariz
Thomas Foltynie
Patricia Limousin
Keyoumars Ashkan
James FitzGerald
Alexander L Green
Tipu Z Aziz
Peter Brown

Funding

Medical Research Council G0901503
6 · The paper itself

Abstract

objectiveBrain-computer interfaces (BCIs) could potentially be used to interact with pathological brain signals to intervene and ameliorate their effects in disease states. Here, we provide proof-of-principle of this approach by using a BCI to interpret pathological brain activity in patients with advanced Parkinson disease (PD) and to use this feedback to control when therapeutic deep brain stimulation (DBS) is delivered. Our goal was to demonstrate that by personalizing and optimizing stimulation in real time, we could improve on both the efficacy and efficiency of conventional continuous DBS.

methodsWe tested BCI-controlled adaptive DBS (aDBS) of the subthalamic nucleus in 8 PD patients. Feedback was provided by processing of the local field potentials recorded directly from the stimulation electrodes. The results were compared to no stimulation, conventional continuous stimulation (cDBS), and random intermittent stimulation. Both unblinded and blinded clinical assessments of motor effect were performed using the Unified Parkinson's Disease Rating Scale.

resultsMotor scores improved by 66% (unblinded) and 50% (blinded) during aDBS, which were 29% (p = 0.03) and 27% (p = 0.005) better than cDBS, respectively. These improvements were achieved with a 56% reduction in stimulation time compared to cDBS, and a corresponding reduction in energy requirements (p < 0.001). aDBS was also more effective than no stimulation and random intermittent stimulation.

interpretationBCI-controlled DBS is tractable and can be more efficient and efficacious than conventional continuous neuromodulation for PD.

Indexed as

Brain-Computer InterfacesDeep Brain StimulationAgedAntiparkinson AgentsHumansMiddle AgedParkinson DiseaseSubthalamic NucleusTreatment OutcomeAntiparkinson Agents

Identifiers

PMID23852650
PMCPMC3886292

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

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.