An algorithmic procedure for measuring deep brain stimulation-induced capsular activation using motor evoked potentials Article Swipe
Eric R. Cole
,
Enrico Opri
,
Seyyed Bahram Borgheai
,
Han Yu-ji
,
Faiçal Isbaine
,
Nicholas M. Boulis
,
Jon T. Willie
,
Nicholas Au Yong
,
Robert E. Gross
,
Svjetlana Miocinovic
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.26.25334489
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1101/2025.08.26.25334489
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.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.1101/2025.08.26.25334489
- https://www.medrxiv.org/content/medrxiv/early/2025/08/28/2025.08.26.25334489.full.pdf
- OA Status
- green
- References
- 47
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4413791450
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4413791450Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1101/2025.08.26.25334489Digital Object Identifier
- Title
-
An algorithmic procedure for measuring deep brain stimulation-induced capsular activation using motor evoked potentialsWork title
- Type
-
preprintOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-08-28Full publication date if available
- Authors
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Eric R. Cole, Enrico Opri, Seyyed Bahram Borgheai, Han Yu-ji, Faiçal Isbaine, Nicholas M. Boulis, Jon T. Willie, Nicholas Au Yong, Robert E. Gross, Svjetlana MiocinovicList of authors in order
- Landing page
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https://doi.org/10.1101/2025.08.26.25334489Publisher landing page
- PDF URL
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https://www.medrxiv.org/content/medrxiv/early/2025/08/28/2025.08.26.25334489.full.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://www.medrxiv.org/content/medrxiv/early/2025/08/28/2025.08.26.25334489.full.pdfDirect OA link when available
- Concepts
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Deep brain stimulation, Stimulation, Neuroscience, Computer science, Psychology, Physical medicine and rehabilitation, Medicine, Internal medicine, Parkinson's disease, DiseaseTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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47Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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