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Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network
Most current research on Alzheimer’s disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030143/ https://www.ncbi.nlm.nih.gov/pubmed/35457373 http://dx.doi.org/10.3390/ijerph19084508 |
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author | Sun, Haijing Wang, Anna He, Shanshan |
author_facet | Sun, Haijing Wang, Anna He, Shanshan |
author_sort | Sun, Haijing |
collection | PubMed |
description | Most current research on Alzheimer’s disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer’s disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively. |
format | Online Article Text |
id | pubmed-9030143 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-90301432022-04-23 Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network Sun, Haijing Wang, Anna He, Shanshan Int J Environ Res Public Health Article Most current research on Alzheimer’s disease (AD) is based on transverse measurements. Given the nature of neurodegeneration in AD progression, observing longitudinal changes in the structural features of brain networks over time may improve the accuracy of the predicted transformation and provide a good measure of the progression of AD. Currently, there is no cure for patients with existing AD dementia, but patients with mild cognitive impairment (MCI) in the prodromal stage of AD dementia may be diagnosed. The study of the early diagnosis of MCI and the prediction of MCI to AD transformation is of great significance for the monitoring of the MCI to AD transformation process. Despite the high rate of MCI conversion to AD, the neuropathological cause of MCI is heterogeneous. However, many people with MCI remain stable. Treatment options are different for patients with stable MCI and those with underlying dementia. Therefore, it is of great significance for clinical practice to predict whether patients with MCI will develop AD dementia. This paper proposes an improved algorithm that is based on a convolution neural network (CNN) with residuals combined with multi-layer long short-term memory (LSTM) to diagnose AD and predict MCI. Firstly, multi-time resting-state fMRI images were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database for preprocessing, and then an AAL brain partition template was used to construct a 90 × 90 functional connectivity (FC) network matrix of a whole-brain region of interest (ROI). Secondly, the diversity of training samples was increased by generating an adversarial network (GAN). Finally, a CNN with residuals and a multi-layer LSTM model were constructed to automatically classify and predict the functional adjacency matrix. This method can not only distinguish Alzheimer’s disease from normal health conditions at multiple time points, but can also predict progressive MCI (pMCI) and stable MCI (sMCI) at multiple time points. The classification accuracies in AD vs. NC and sMCI vs.pMCI reached 93.5% and 75.5%, respectively. MDPI 2022-04-08 /pmc/articles/PMC9030143/ /pubmed/35457373 http://dx.doi.org/10.3390/ijerph19084508 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Sun, Haijing Wang, Anna He, Shanshan Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network |
title | Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network |
title_full | Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network |
title_fullStr | Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network |
title_full_unstemmed | Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network |
title_short | Temporal and Spatial Analysis of Alzheimer’s Disease Based on an Improved Convolutional Neural Network and a Resting-State FMRI Brain Functional Network |
title_sort | temporal and spatial analysis of alzheimer’s disease based on an improved convolutional neural network and a resting-state fmri brain functional network |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9030143/ https://www.ncbi.nlm.nih.gov/pubmed/35457373 http://dx.doi.org/10.3390/ijerph19084508 |
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