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Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking
The expanding application of deep brain stimulation (DBS) therapy both drives and is informed by our growing understanding of disease pathophysiology and innovations in neurosurgical care. Neurophysiological targeting, a mainstay for identifying optimal, motor responsive targets, has remained largel...
Autores principales: | , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613670/ https://www.ncbi.nlm.nih.gov/pubmed/36302865 http://dx.doi.org/10.1038/s41598-022-21860-7 |
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author | Tekriwal, Anand Baker, Sunderland Christensen, Elijah Petersen-Jones, Humphrey Tien, Rex N. Ojemann, Steven G. Kern, Drew S. Kramer, Daniel R. Felsen, Gidon Thompson, John A. |
author_facet | Tekriwal, Anand Baker, Sunderland Christensen, Elijah Petersen-Jones, Humphrey Tien, Rex N. Ojemann, Steven G. Kern, Drew S. Kramer, Daniel R. Felsen, Gidon Thompson, John A. |
author_sort | Tekriwal, Anand |
collection | PubMed |
description | The expanding application of deep brain stimulation (DBS) therapy both drives and is informed by our growing understanding of disease pathophysiology and innovations in neurosurgical care. Neurophysiological targeting, a mainstay for identifying optimal, motor responsive targets, has remained largely unchanged for decades. Utilizing deep learning-based computer vision and related computational methods, we developed an effective and simple intraoperative approach to objectively correlate neural signals with movements, automating and standardizing the otherwise manual and subjective process of identifying ideal DBS electrode placements. Kinematics are extracted from video recordings of intraoperative motor testing using a trained deep neural network and compared to multi-unit activity recorded from the subthalamic nucleus. Neuro-motor correlations were quantified using dynamic time warping with the strength of a given comparison measured by comparing against a null distribution composed of related neuro-motor correlations. This objective measure was then compared to clinical determinations as recorded in surgical case notes. In seven DBS cases for treatment of Parkinson’s disease, 100 distinct motor testing epochs were extracted for which clear clinical determinations were made. Neuro-motor correlations derived by our automated system compared favorably with expert clinical decision making in post-hoc comparisons, although follow-up studies are necessary to determine if improved correlation detection leads to improved outcomes. By improving the classification of neuro-motor relationships, the automated system we have developed will enable clinicians to maximize the therapeutic impact of DBS while also providing avenues for improving continued care of treated patients. |
format | Online Article Text |
id | pubmed-9613670 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-96136702022-10-29 Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking Tekriwal, Anand Baker, Sunderland Christensen, Elijah Petersen-Jones, Humphrey Tien, Rex N. Ojemann, Steven G. Kern, Drew S. Kramer, Daniel R. Felsen, Gidon Thompson, John A. Sci Rep Article The expanding application of deep brain stimulation (DBS) therapy both drives and is informed by our growing understanding of disease pathophysiology and innovations in neurosurgical care. Neurophysiological targeting, a mainstay for identifying optimal, motor responsive targets, has remained largely unchanged for decades. Utilizing deep learning-based computer vision and related computational methods, we developed an effective and simple intraoperative approach to objectively correlate neural signals with movements, automating and standardizing the otherwise manual and subjective process of identifying ideal DBS electrode placements. Kinematics are extracted from video recordings of intraoperative motor testing using a trained deep neural network and compared to multi-unit activity recorded from the subthalamic nucleus. Neuro-motor correlations were quantified using dynamic time warping with the strength of a given comparison measured by comparing against a null distribution composed of related neuro-motor correlations. This objective measure was then compared to clinical determinations as recorded in surgical case notes. In seven DBS cases for treatment of Parkinson’s disease, 100 distinct motor testing epochs were extracted for which clear clinical determinations were made. Neuro-motor correlations derived by our automated system compared favorably with expert clinical decision making in post-hoc comparisons, although follow-up studies are necessary to determine if improved correlation detection leads to improved outcomes. By improving the classification of neuro-motor relationships, the automated system we have developed will enable clinicians to maximize the therapeutic impact of DBS while also providing avenues for improving continued care of treated patients. Nature Publishing Group UK 2022-10-27 /pmc/articles/PMC9613670/ /pubmed/36302865 http://dx.doi.org/10.1038/s41598-022-21860-7 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Article Tekriwal, Anand Baker, Sunderland Christensen, Elijah Petersen-Jones, Humphrey Tien, Rex N. Ojemann, Steven G. Kern, Drew S. Kramer, Daniel R. Felsen, Gidon Thompson, John A. Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking |
title | Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking |
title_full | Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking |
title_fullStr | Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking |
title_full_unstemmed | Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking |
title_short | Quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking |
title_sort | quantifying neuro-motor correlations during awake deep brain stimulation surgery using markerless tracking |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9613670/ https://www.ncbi.nlm.nih.gov/pubmed/36302865 http://dx.doi.org/10.1038/s41598-022-21860-7 |
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