Evidence map›Paper›PMID 41162751›Full record

ArticleNPJ digital medicine2025

Electrophysiological signatures predict the therapeutic window of deep brain stimulation electrode contacts.

Fayed Rassoulou, Abhinav Sharma, Alexandra Steina, Markus Butz, Christian J Hartmann, Bahne H Bahners, Jan Vesper, Alfons Schnitzler, Jan Hirschmann

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

9 authors.

Fayed RassoulouInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Abhinav SharmaMRC Brain Networks Dynamics Unit, University of Oxford, Oxford, UK.
Alexandra SteinaInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Markus ButzInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Christian J HartmannInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Bahne H BahnersInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Jan VesperDepartment of Functional Neurosurgery and Stereotaxy, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Alfons SchnitzlerInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
Jan HirschmannInstitute of Clinical Neuroscience and Medical Psychology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany. Jan.Hirschmann@med.uni-duesseldorf.de.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Deep brain stimulation (DBS) is an effective treatment for Parkinson's disease. Identifying the optimal parameters is a complex task. Here, we investigated whether electrophysiology, combined with machine learning, can support contact selection. We applied tree learning to resting-state magnetoencephalographic and local field potential recordings from the subthalamic nucleus (STN). STN power and STN-cortex coherence in various frequency bands served to predict the therapeutic window. The model successfully predicted therapeutic windows in the original (r = 0.45, p < 0.001, N = 45) and in an independent cohort (r = 0.30, p < 0.001, N = 8). It relied mostly on fast (>35 Hz) subthalamic activity and on STN-cortex coherence in several bands. Furthermore, it was able to order contacts such that the optimal contact can be found faster. Our study demonstrates the feasibility of predicting therapeutic windows from electrophysiological features and could contribute to automated contact selection in the future.

Identifiers

PMID41162751
PMCPMC12572235

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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.