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Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification

Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer&...

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Autores principales: Lin, Kai, Jie, Biao, Dong, Peng, Ding, Xintao, Bian, Weixin, Liu, Mingxia
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298744/
https://www.ncbi.nlm.nih.gov/pubmed/35873806
http://dx.doi.org/10.3389/fnins.2022.933660
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author Lin, Kai
Jie, Biao
Dong, Peng
Ding, Xintao
Bian, Weixin
Liu, Mingxia
author_facet Lin, Kai
Jie, Biao
Dong, Peng
Ding, Xintao
Bian, Weixin
Liu, Mingxia
author_sort Lin, Kai
collection PubMed
description Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks.
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spelling pubmed-92987442022-07-21 Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification Lin, Kai Jie, Biao Dong, Peng Ding, Xintao Bian, Weixin Liu, Mingxia Front Neurosci Neuroscience Dynamic functional connectivity (dFC) networks derived from resting-state functional magnetic resonance imaging (rs-fMRI) help us understand fundamental dynamic characteristics of human brains, thereby providing an efficient solution for automated identification of brain diseases, such as Alzheimer's disease (AD) and its prodromal stage. Existing studies have applied deep learning methods to dFC network analysis and achieved good performance compared with traditional machine learning methods. However, they seldom take advantage of sequential information conveyed in dFC networks that could be informative to improve the diagnosis performance. In this paper, we propose a convolutional recurrent neural network (CRNN) for automated brain disease classification with rs-fMRI data. Specifically, we first construct dFC networks from rs-fMRI data using a sliding window strategy. Then, we employ three convolutional layers and long short-term memory (LSTM) layer to extract high-level features of dFC networks and also preserve the sequential information of extracted features, followed by three fully connected layers for brain disease classification. Experimental results on 174 subjects with 563 rs-fMRI scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI) demonstrate the effectiveness of our proposed method in binary and multi-category classification tasks. Frontiers Media S.A. 2022-07-06 /pmc/articles/PMC9298744/ /pubmed/35873806 http://dx.doi.org/10.3389/fnins.2022.933660 Text en Copyright © 2022 Lin, Jie, Dong, Ding, Bian and Liu. 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
Lin, Kai
Jie, Biao
Dong, Peng
Ding, Xintao
Bian, Weixin
Liu, Mingxia
Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification
title Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification
title_full Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification
title_fullStr Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification
title_full_unstemmed Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification
title_short Convolutional Recurrent Neural Network for Dynamic Functional MRI Analysis and Brain Disease Identification
title_sort convolutional recurrent neural network for dynamic functional mri analysis and brain disease identification
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9298744/
https://www.ncbi.nlm.nih.gov/pubmed/35873806
http://dx.doi.org/10.3389/fnins.2022.933660
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