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A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification
Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefor...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9339621/ https://www.ncbi.nlm.nih.gov/pubmed/35923552 http://dx.doi.org/10.3389/fnagi.2022.925468 |
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author | Feng, Quan Huang, Yongjie Long, Yun Gao, Le Gao, Xin |
author_facet | Feng, Quan Huang, Yongjie Long, Yun Gao, Le Gao, Xin |
author_sort | Feng, Quan |
collection | PubMed |
description | Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%. |
format | Online Article Text |
id | pubmed-9339621 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
spelling | pubmed-93396212022-08-02 A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification Feng, Quan Huang, Yongjie Long, Yun Gao, Le Gao, Xin Front Aging Neurosci Aging Neuroscience Mild cognitive impairment (MCI) is a nervous system disease, and its clinical status can be used as an early warning of Alzheimer's disease (AD). Subtle and slow changes in brain structure between patients with MCI and normal controls (NCs) deprive them of effective diagnostic methods. Therefore, the identification of MCI is a challenging task. The current functional brain network (FBN) analysis to predict human brain tissue structure is a new method emerging in recent years, which provides sensitive and effective medical biomarkers for the diagnosis of neurological diseases. Therefore, to address this challenge, we propose a novel Deep Spatiotemporal Attention Network (DSTAN) framework for MCI recognition based on brain functional networks. Specifically, we first extract spatiotemporal features between brain functional signals and FBNs by designing a spatiotemporal convolution strategy (ST-CONV). Then, on this basis, we introduce a learned attention mechanism to further capture brain nodes strongly correlated with MCI. Finally, we fuse spatiotemporal features for MCI recognition. The entire network is trained in an end-to-end fashion. Extensive experiments show that our proposed method significantly outperforms current baselines and state-of-the-art methods, with a classification accuracy of 84.21%. Frontiers Media S.A. 2022-07-18 /pmc/articles/PMC9339621/ /pubmed/35923552 http://dx.doi.org/10.3389/fnagi.2022.925468 Text en Copyright © 2022 Feng, Huang, Long, Gao and Gao. 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 Feng, Quan Huang, Yongjie Long, Yun Gao, Le Gao, Xin A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification |
title | A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification |
title_full | A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification |
title_fullStr | A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification |
title_full_unstemmed | A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification |
title_short | A Deep Spatiotemporal Attention Network for Mild Cognitive Impairment Identification |
title_sort | deep spatiotemporal attention network for mild cognitive impairment identification |
topic | Aging Neuroscience |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9339621/ https://www.ncbi.nlm.nih.gov/pubmed/35923552 http://dx.doi.org/10.3389/fnagi.2022.925468 |
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