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Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI
As Alzheimer’s disease (AD) is featured with degeneration and irreversibility, the diagnosis of AD at early stage is important. In recent years, some researchers have tried to apply neural network (NN) to classify AD patients from healthy controls (HC) based on functional MRI (fMRI) data. But most s...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6137384/ https://www.ncbi.nlm.nih.gov/pubmed/30245623 http://dx.doi.org/10.3389/fninf.2018.00060 |
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author | Bi, Xia-an Jiang, Qin Sun, Qi Shu, Qing Liu, Yingchao |
author_facet | Bi, Xia-an Jiang, Qin Sun, Qi Shu, Qing Liu, Yingchao |
author_sort | Bi, Xia-an |
collection | PubMed |
description | As Alzheimer’s disease (AD) is featured with degeneration and irreversibility, the diagnosis of AD at early stage is important. In recent years, some researchers have tried to apply neural network (NN) to classify AD patients from healthy controls (HC) based on functional MRI (fMRI) data. But most study focus on a single NN and the classification accuracy was not high. Therefore, this paper used the random neural network cluster which was composed of multiple NNs to improve classification performance. Sixty one subjects (25 AD and 36 HC) were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This method not only could be used in the classification, but also could be used for feature selection. Firstly, we chose Elman NN from five types of NNs as the optimal base classifier of random neural network cluster based on the results of feature selection, and the accuracies of the random Elman neural network cluster could reach to 92.31% which was the highest and stable. Then we used the random Elman neural network cluster to select significant features and these features could be used to find out the abnormal regions. Finally, we found out 23 abnormal regions such as the precentral gyrus, the frontal gyrus and supplementary motor area. These results fully show that the random neural network cluster is worthwhile and meaningful for the diagnosis of AD. |
format | Online Article Text |
id | pubmed-6137384 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-61373842018-09-21 Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI Bi, Xia-an Jiang, Qin Sun, Qi Shu, Qing Liu, Yingchao Front Neuroinform Neuroscience As Alzheimer’s disease (AD) is featured with degeneration and irreversibility, the diagnosis of AD at early stage is important. In recent years, some researchers have tried to apply neural network (NN) to classify AD patients from healthy controls (HC) based on functional MRI (fMRI) data. But most study focus on a single NN and the classification accuracy was not high. Therefore, this paper used the random neural network cluster which was composed of multiple NNs to improve classification performance. Sixty one subjects (25 AD and 36 HC) were acquired from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset. This method not only could be used in the classification, but also could be used for feature selection. Firstly, we chose Elman NN from five types of NNs as the optimal base classifier of random neural network cluster based on the results of feature selection, and the accuracies of the random Elman neural network cluster could reach to 92.31% which was the highest and stable. Then we used the random Elman neural network cluster to select significant features and these features could be used to find out the abnormal regions. Finally, we found out 23 abnormal regions such as the precentral gyrus, the frontal gyrus and supplementary motor area. These results fully show that the random neural network cluster is worthwhile and meaningful for the diagnosis of AD. Frontiers Media S.A. 2018-09-07 /pmc/articles/PMC6137384/ /pubmed/30245623 http://dx.doi.org/10.3389/fninf.2018.00060 Text en Copyright © 2018 Bi, Jiang, Sun, Shu and Liu. 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 Bi, Xia-an Jiang, Qin Sun, Qi Shu, Qing Liu, Yingchao Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI |
title | Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI |
title_full | Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI |
title_fullStr | Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI |
title_full_unstemmed | Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI |
title_short | Analysis of Alzheimer’s Disease Based on the Random Neural Network Cluster in fMRI |
title_sort | analysis of alzheimer’s disease based on the random neural network cluster in fmri |
topic | Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6137384/ https://www.ncbi.nlm.nih.gov/pubmed/30245623 http://dx.doi.org/10.3389/fninf.2018.00060 |
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