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Prediction of STN-DBS for Parkinson’s disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI
INTRODUCTION: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by dyskinesia and is closely related to oxidative stress. Uric acid (UA) is a natural antioxidant found in the body. Previous studies have shown that UA has played an important role in the development and developmen...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941535/ https://www.ncbi.nlm.nih.gov/pubmed/36824266 http://dx.doi.org/10.3389/fnagi.2023.1105107 |
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author | Chang, Bowen Xiong, Chi Ni, Chen Chen, Peng Jiang, Manli Mei, Jiaming Niu, Chaoshi |
author_facet | Chang, Bowen Xiong, Chi Ni, Chen Chen, Peng Jiang, Manli Mei, Jiaming Niu, Chaoshi |
author_sort | Chang, Bowen |
collection | PubMed |
description | INTRODUCTION: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by dyskinesia and is closely related to oxidative stress. Uric acid (UA) is a natural antioxidant found in the body. Previous studies have shown that UA has played an important role in the development and development of PD and is an important biomarker. Subthalamic nucleus deep brain stimulation (STN-DBS) is a common treatment for PD. METHODS: Based on resting state function MRI (rs-fMRI), the relationship between UA-related brain function connectivity (FC) and STN-DBS outcomes in PD patients was studied. We use UA and DC values from different brain regions to build the FC characteristics and then use the SVR model to predict the outcome of the operation. RESULTS: The results show that PD patients with UA-related FCs are closely related to STN-DBS efficacy and can be used to predict prognosis. A machine learning model based on UA-related FC was successfully developed for PD patients. DISCUSSION: The two biomarkers, UA and rs-fMRI, were combined to predict the prognosis of STN-DBS in treating PD. Neurosurgeons are provided with effective tools to screen the best candidate and predict the prognosis of the patient. |
format | Online Article Text |
id | pubmed-9941535 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-99415352023-02-22 Prediction of STN-DBS for Parkinson’s disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI Chang, Bowen Xiong, Chi Ni, Chen Chen, Peng Jiang, Manli Mei, Jiaming Niu, Chaoshi Front Aging Neurosci Aging Neuroscience INTRODUCTION: Parkinson’s disease (PD) is a neurodegenerative disorder characterized by dyskinesia and is closely related to oxidative stress. Uric acid (UA) is a natural antioxidant found in the body. Previous studies have shown that UA has played an important role in the development and development of PD and is an important biomarker. Subthalamic nucleus deep brain stimulation (STN-DBS) is a common treatment for PD. METHODS: Based on resting state function MRI (rs-fMRI), the relationship between UA-related brain function connectivity (FC) and STN-DBS outcomes in PD patients was studied. We use UA and DC values from different brain regions to build the FC characteristics and then use the SVR model to predict the outcome of the operation. RESULTS: The results show that PD patients with UA-related FCs are closely related to STN-DBS efficacy and can be used to predict prognosis. A machine learning model based on UA-related FC was successfully developed for PD patients. DISCUSSION: The two biomarkers, UA and rs-fMRI, were combined to predict the prognosis of STN-DBS in treating PD. Neurosurgeons are provided with effective tools to screen the best candidate and predict the prognosis of the patient. Frontiers Media S.A. 2023-02-07 /pmc/articles/PMC9941535/ /pubmed/36824266 http://dx.doi.org/10.3389/fnagi.2023.1105107 Text en Copyright © 2023 Chang, Xiong, Ni, Chen, Jiang, Mei and Niu. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. |
spellingShingle | Aging Neuroscience Chang, Bowen Xiong, Chi Ni, Chen Chen, Peng Jiang, Manli Mei, Jiaming Niu, Chaoshi Prediction of STN-DBS for Parkinson’s disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI |
title | Prediction of STN-DBS for Parkinson’s disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI |
title_full | Prediction of STN-DBS for Parkinson’s disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI |
title_fullStr | Prediction of STN-DBS for Parkinson’s disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI |
title_full_unstemmed | Prediction of STN-DBS for Parkinson’s disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI |
title_short | Prediction of STN-DBS for Parkinson’s disease by uric acid-related brain function connectivity: A machine learning study based on resting state function MRI |
title_sort | prediction of stn-dbs for parkinson’s disease by uric acid-related brain function connectivity: a machine learning study based on resting state function mri |
topic | Aging Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9941535/ https://www.ncbi.nlm.nih.gov/pubmed/36824266 http://dx.doi.org/10.3389/fnagi.2023.1105107 |
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