Evidence map›Paper›PMID 40909847›Full record

ArticlemedRxiv : the preprint server for health sciences2025

An algorithmic procedure for measuring deep brain stimulation-induced capsular activation using motor evoked potentials.

Eric R Cole, Enrico Opri, Seyyed Bahram Borgheai, Yuji Han, Faical Isbaine, Nicholas Boulis, Jon T Willie, Nicholas AuYong, Robert E Gross, Svjetlana Miocinovic

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in medRxiv : the preprint server for health sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

10 authors.

Eric R ColeWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.ORCID 0000-0001-6695-0373
Enrico OpriDepartment of Biomedical Engineering, University of Michigan, Ann Arbor, MI, 30322, USA.ORCID 0000-0002-7774-4142
Seyyed Bahram BorgheaiDepartment of Neurology, Emory University School of Medicine, Atlanta, GA, 30322, USA.
Yuji HanWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.
Faical IsbaineDepartment of Neurosurgery, Emory University School of Medicine, Atlanta, GA, 30322, USA.
Nicholas BoulisDepartment of Neurosurgery, Emory University School of Medicine, Atlanta, GA, 30322, USA.
Jon T WillieDepartment of Neurosurgery, Washington University, St. Louis, Missouri.
Nicholas AuYongWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.
Robert E GrossDepartment of Neurosurgery, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, NJ, 08901, USA.
Svjetlana MiocinovicWallace H. Coulter Department of Biomedical Engineering, Georgia Institute of Technology and Emory University, Atlanta, GA, 30332, USA.

Funding

Optimizing Patient-Specific Deep Brain Stimulation Models Using ElectrophysiologyR01NS125143 · NINDS · EMORY UNIVERSITY · PI Svjetlana Miocinovic · 2022 to 2026
$2.4M
NINDS NIH HHS R01 NS125143
6 · The paper itself

Abstract

Objective: Effective deep brain stimulation (DBS) treatment for Parkinson's disease requires careful adjustment of stimulation parameters and targeting to avoid motor side effects caused by activation of the internal capsule. Currently, patients must self-report side effects during device programming and implantation surgery - a challenging and subjective process that could lead to suboptimal therapy or exacerbate the time needed to optimize treatment. Motor evoked potentials (mEP), the use of electromyography to record DBS-induced muscle activation, offer a promising biomarker for objective motor side effect detection. Approach: Here, we present an automated algorithmic procedure for mEP detection and quantification. Main Results: First, we design and evaluate a series of signal processing techniques to accurately detect mEP while mitigating the influence of stimulation artifacts and noise, then demonstrate a strategy for integrating multi-channel EMG responses into a single side effect biomarker (the mEP score). Next, we use data from a large patient cohort of intraoperative recordings (N = 54 STN leads) to quantify several physiological features of mEP, including their response frequency, latency, amplitude, and waveform similarity properties. Last, we show that the mEP score responds to DBS amplitude and contact configuration parameters in a manner that is consistent with expected STN-capsular anatomy. Significance: The results of this study inform an end-to-end approach for side effect biomarker measurement that could aid the precision and efficiency of DBS programming and surgical targeting.

Indexed as

corticospinal tractDBS side effectsinternal capsuleprogrammingsignal processing

Identifiers

PMID40909847
PMCPMC12407655

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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.