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Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis

Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to...

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Autores principales: Shi, Dafa, Zhang, Haoran, Wang, Guangsong, Wang, Siyuan, Yao, Xiang, Li, Yanfei, Guo, Qiu, Zheng, Shuang, Ren, Ke
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928361/
https://www.ncbi.nlm.nih.gov/pubmed/35309885
http://dx.doi.org/10.3389/fnagi.2022.806828
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author Shi, Dafa
Zhang, Haoran
Wang, Guangsong
Wang, Siyuan
Yao, Xiang
Li, Yanfei
Guo, Qiu
Zheng, Shuang
Ren, Ke
author_facet Shi, Dafa
Zhang, Haoran
Wang, Guangsong
Wang, Siyuan
Yao, Xiang
Li, Yanfei
Guo, Qiu
Zheng, Shuang
Ren, Ke
author_sort Shi, Dafa
collection PubMed
description Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex.
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spelling pubmed-89283612022-03-18 Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis Shi, Dafa Zhang, Haoran Wang, Guangsong Wang, Siyuan Yao, Xiang Li, Yanfei Guo, Qiu Zheng, Shuang Ren, Ke Front Aging Neurosci Neuroscience Parkinson’s disease (PD) is one of the most common progressive degenerative diseases, and its diagnosis is challenging on clinical grounds. Clinically, effective and quantifiable biomarkers to detect PD are urgently needed. In our study, we analyzed data from two centers, the primary set was used to train the model, and the independent external validation set was used to validate our model. We applied amplitude of low-frequency fluctuation (ALFF)-based radiomics method to extract radiomics features (including first- and high-order features). Subsequently, t-test and least absolute shrinkage and selection operator (LASSO) were harnessed for feature selection and data dimensionality reduction, and grid search method and nested 10-fold cross-validation were applied to determine the optimal hyper-parameter λ of LASSO and evaluate the performance of the model, in which a support vector machine was used to construct the classification model to classify patients with PD and healthy controls (HCs). We found that our model achieved good performance [accuracy = 81.45% and area under the curve (AUC) = 0.850] in the primary set and good generalization in the external validation set (accuracy = 67.44% and AUC = 0.667). Most of the discriminative features were high-order radiomics features, and the identified brain regions were mainly located in the sensorimotor network and lateral parietal cortex. Our study indicated that our proposed method can effectively classify patients with PD and HCs, ALFF-based radiomics features that might be potential biomarkers of PD, and provided further support for the pathological mechanism of PD, that is, PD may be related to abnormal brain activity in the sensorimotor network and lateral parietal cortex. Frontiers Media S.A. 2022-03-03 /pmc/articles/PMC8928361/ /pubmed/35309885 http://dx.doi.org/10.3389/fnagi.2022.806828 Text en Copyright © 2022 Shi, Zhang, Wang, Wang, Yao, Li, Guo, Zheng and Ren. 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 Neuroscience
Shi, Dafa
Zhang, Haoran
Wang, Guangsong
Wang, Siyuan
Yao, Xiang
Li, Yanfei
Guo, Qiu
Zheng, Shuang
Ren, Ke
Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis
title Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis
title_full Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis
title_fullStr Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis
title_full_unstemmed Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis
title_short Machine Learning for Detecting Parkinson’s Disease by Resting-State Functional Magnetic Resonance Imaging: A Multicenter Radiomics Analysis
title_sort machine learning for detecting parkinson’s disease by resting-state functional magnetic resonance imaging: a multicenter radiomics analysis
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8928361/
https://www.ncbi.nlm.nih.gov/pubmed/35309885
http://dx.doi.org/10.3389/fnagi.2022.806828
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