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Morphologic brain network predicts levodopa responsiveness in Parkinson disease

BACKGROUND: The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson’s Disease Rating Scale (UPDRS)-III score > 33% in the LC...

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Autores principales: Xie, Yongsheng, Gao, Chunyan, Wu, Bin, Peng, Liling, Wu, Jianjun, Lang, Liqin
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849367/
https://www.ncbi.nlm.nih.gov/pubmed/36688150
http://dx.doi.org/10.3389/fnagi.2022.990913
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author Xie, Yongsheng
Gao, Chunyan
Wu, Bin
Peng, Liling
Wu, Jianjun
Lang, Liqin
author_facet Xie, Yongsheng
Gao, Chunyan
Wu, Bin
Peng, Liling
Wu, Jianjun
Lang, Liqin
author_sort Xie, Yongsheng
collection PubMed
description BACKGROUND: The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson’s Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS. METHODS: A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient’s neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen–Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups. RESULTS: The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased. CONCLUSION: Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS.
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spelling pubmed-98493672023-01-20 Morphologic brain network predicts levodopa responsiveness in Parkinson disease Xie, Yongsheng Gao, Chunyan Wu, Bin Peng, Liling Wu, Jianjun Lang, Liqin Front Aging Neurosci Aging Neuroscience BACKGROUND: The levodopa challenge test (LCT) has been routinely used in Parkinson disease (PD) evaluation and predicts the outcome of deep brain stimulation (DBS). Guidelines recommend that patients with an improvement in Unified Parkinson’s Disease Rating Scale (UPDRS)-III score > 33% in the LCT receive DBS treatment. However, LCT results are affected by many factors, and only provide information on the immediate effectiveness of dopamine. The aim of the present study was to investigate the relationship between LCT outcome and brain imaging features of PD patients to determine whether the latter can be used to identify candidates for DBS. METHODS: A total of 38 PD patients were enrolled in the study. Based on improvement in UPDRS-III score in the LCT, patients were divided into low improvement (PD-LCT-L) and high improvement (PD-LCT-H) groups. Each patient’s neural network was reconstructed based on T1-weighted magnetic resonance imaging data using the Jensen–Shannon divergence similarity estimation method. The network was established with the multiple kernel support vector machine technique. We analyzed differences in individual morphologic brain networks and their global and local metrics to determine whether there were differences in the connectomes of PD-LCT-L and PD-LCT-H groups. RESULTS: The 2 groups were similar in terms of demographic and clinical characteristics. Mean ± SD levodopa responsiveness was 26.52% ± 3.47% in the PD-LCT-L group (N = 13) and 58.66% ± 4.09% in the PD-LCT-H group (N = 25). There were no significant differences between groups in global and local metrics. There were 43 consensus connections that were affected in both groups; in PD-LCT-L patients, most of these connections were decreased whereas those related to the dorsolateral superior frontal gyrus and left cuneus were significantly increased. CONCLUSION: Morphologic brain network assessment is a valuable method for predicting levodopa responsiveness in PD patients, which can facilitate the selection of candidates for DBS. Frontiers Media S.A. 2023-01-05 /pmc/articles/PMC9849367/ /pubmed/36688150 http://dx.doi.org/10.3389/fnagi.2022.990913 Text en Copyright © 2023 Xie, Gao, Wu, Peng, Wu and Lang. 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
Xie, Yongsheng
Gao, Chunyan
Wu, Bin
Peng, Liling
Wu, Jianjun
Lang, Liqin
Morphologic brain network predicts levodopa responsiveness in Parkinson disease
title Morphologic brain network predicts levodopa responsiveness in Parkinson disease
title_full Morphologic brain network predicts levodopa responsiveness in Parkinson disease
title_fullStr Morphologic brain network predicts levodopa responsiveness in Parkinson disease
title_full_unstemmed Morphologic brain network predicts levodopa responsiveness in Parkinson disease
title_short Morphologic brain network predicts levodopa responsiveness in Parkinson disease
title_sort morphologic brain network predicts levodopa responsiveness in parkinson disease
topic Aging Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9849367/
https://www.ncbi.nlm.nih.gov/pubmed/36688150
http://dx.doi.org/10.3389/fnagi.2022.990913
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