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Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data
Identifying patients with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer’s disease (AD) is crucial for early treatment of AD. However, it is difficult to predict the cognitive states of patients. This study developed an extreme learning machine (ELM)-based grading m...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140986/ https://www.ncbi.nlm.nih.gov/pubmed/32296326 http://dx.doi.org/10.3389/fnagi.2020.00077 |
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author | Lin, Weiming Gao, Qinquan Yuan, Jiangnan Chen, Zhiying Feng, Chenwei Chen, Weisheng Du, Min Tong, Tong |
author_facet | Lin, Weiming Gao, Qinquan Yuan, Jiangnan Chen, Zhiying Feng, Chenwei Chen, Weisheng Du, Min Tong, Tong |
author_sort | Lin, Weiming |
collection | PubMed |
description | Identifying patients with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer’s disease (AD) is crucial for early treatment of AD. However, it is difficult to predict the cognitive states of patients. This study developed an extreme learning machine (ELM)-based grading method to efficiently fuse multimodal data and predict MCI-to-AD conversion. First, features were extracted from magnetic resonance (MR) images, and useful features were selected using a feature selection method. Second, multiple modalities of MCI subjects, including MRI, positron emission tomography, cerebrospinal fluid biomarkers, and gene data, were individually graded using the ELM method. Finally, these grading scores calculated from different modalities were fed into a classifier to discriminate subjects with progressive MCI from those with stable MCI. The proposed approach has been validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, and an accuracy of 84.7% was achieved for an AD prediction within 3 years. Experiments on predicting AD conversion from MCI within different periods showed similar results with the 3-year prediction. The experimental results demonstrate that the proposed approach benefits from the efficient fusion of four modalities, resulting in an accurate prediction of MCI-to-AD conversion. |
format | Online Article Text |
id | pubmed-7140986 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-71409862020-04-15 Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data Lin, Weiming Gao, Qinquan Yuan, Jiangnan Chen, Zhiying Feng, Chenwei Chen, Weisheng Du, Min Tong, Tong Front Aging Neurosci Neuroscience Identifying patients with mild cognitive impairment (MCI) who are at high risk of progressing to Alzheimer’s disease (AD) is crucial for early treatment of AD. However, it is difficult to predict the cognitive states of patients. This study developed an extreme learning machine (ELM)-based grading method to efficiently fuse multimodal data and predict MCI-to-AD conversion. First, features were extracted from magnetic resonance (MR) images, and useful features were selected using a feature selection method. Second, multiple modalities of MCI subjects, including MRI, positron emission tomography, cerebrospinal fluid biomarkers, and gene data, were individually graded using the ELM method. Finally, these grading scores calculated from different modalities were fed into a classifier to discriminate subjects with progressive MCI from those with stable MCI. The proposed approach has been validated on the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort, and an accuracy of 84.7% was achieved for an AD prediction within 3 years. Experiments on predicting AD conversion from MCI within different periods showed similar results with the 3-year prediction. The experimental results demonstrate that the proposed approach benefits from the efficient fusion of four modalities, resulting in an accurate prediction of MCI-to-AD conversion. Frontiers Media S.A. 2020-04-01 /pmc/articles/PMC7140986/ /pubmed/32296326 http://dx.doi.org/10.3389/fnagi.2020.00077 Text en Copyright © 2020 Lin, Gao, Yuan, Chen, Feng, Chen, Du and Tong. http://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 Lin, Weiming Gao, Qinquan Yuan, Jiangnan Chen, Zhiying Feng, Chenwei Chen, Weisheng Du, Min Tong, Tong Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data |
title | Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data |
title_full | Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data |
title_fullStr | Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data |
title_full_unstemmed | Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data |
title_short | Predicting Alzheimer’s Disease Conversion From Mild Cognitive Impairment Using an Extreme Learning Machine-Based Grading Method With Multimodal Data |
title_sort | predicting alzheimer’s disease conversion from mild cognitive impairment using an extreme learning machine-based grading method with multimodal data |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7140986/ https://www.ncbi.nlm.nih.gov/pubmed/32296326 http://dx.doi.org/10.3389/fnagi.2020.00077 |
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