A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network
BackgroundThe convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its uncl...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2023-08-01
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| Series: | Frontiers in Neuroscience |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnins.2023.1177424/full |
| _version_ | 1827874630490128384 |
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| author | Ruofan Wang Qiguang He Chunxiao Han Haodong Wang Lianshuan Shi Yanqiu Che |
| author_facet | Ruofan Wang Qiguang He Chunxiao Han Haodong Wang Lianshuan Shi Yanqiu Che |
| author_sort | Ruofan Wang |
| collection | DOAJ |
| description | BackgroundThe convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.ObjectiveThe aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.MethodsFirst, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.ResultsFinally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD. |
| first_indexed | 2024-03-12T16:54:35Z |
| format | Article |
| id | doaj.art-57dec887fc4649fb8698f473f1544d7d |
| institution | Directory Open Access Journal |
| issn | 1662-453X |
| language | English |
| last_indexed | 2024-03-12T16:54:35Z |
| publishDate | 2023-08-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neuroscience |
| spelling | doaj.art-57dec887fc4649fb8698f473f1544d7d2023-08-08T07:47:14ZengFrontiers Media S.A.Frontiers in Neuroscience1662-453X2023-08-011710.3389/fnins.2023.11774241177424A deep learning framework for identifying Alzheimer's disease using fMRI-based brain networkRuofan Wang0Qiguang He1Chunxiao Han2Haodong Wang3Lianshuan Shi4Yanqiu Che5School of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaTianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaSchool of Information Technology Engineering, Tianjin University of Technology and Education, Tianjin, ChinaTianjin Key Laboratory of Information Sensing and Intelligent Control, School of Automation and Electrical Engineering, Tianjin University of Technology and Education, Tianjin, ChinaBackgroundThe convolutional neural network (CNN) is a mainstream deep learning (DL) algorithm, and it has gained great fame in solving problems from clinical examination and diagnosis, such as Alzheimer's disease (AD). AD is a degenerative disease difficult to clinical diagnosis due to its unclear underlying pathological mechanism. Previous studies have primarily focused on investigating structural abnormalities in the brain's functional networks related to the AD or proposing different deep learning approaches for AD classification.ObjectiveThe aim of this study is to leverage the advantages of combining brain topological features extracted from functional network exploration and deep features extracted by the CNN. We establish a novel fMRI-based classification framework that utilizes Resting-state functional magnetic resonance imaging (rs-fMRI) with the phase synchronization index (PSI) and 2D-CNN to detect abnormal brain functional connectivity in AD.MethodsFirst, PSI was applied to construct the brain network by region of interest (ROI) signals obtained from data preprocessing stage, and eight topological features were extracted. Subsequently, the 2D-CNN was applied to the PSI matrix to explore the local and global patterns of the network connectivity by extracting eight deep features from the 2D-CNN convolutional layer.ResultsFinally, classification analysis was carried out on the combined PSI and 2D-CNN methods to recognize AD by using support vector machine (SVM) with 5-fold cross-validation strategy. It was found that the classification accuracy of combined method achieved 98.869%.ConclusionThese findings show that our framework can adaptively combine the best brain network features to explore network synchronization, functional connections, and characterize brain functional abnormalities, which could effectively detect AD anomalies by the extracted features that may provide new insights into exploring the underlying pathogenesis of AD.https://www.frontiersin.org/articles/10.3389/fnins.2023.1177424/fullfMRIAlzheimer's disease2D-CNNphase synchronization indexROI |
| spellingShingle | Ruofan Wang Qiguang He Chunxiao Han Haodong Wang Lianshuan Shi Yanqiu Che A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network Frontiers in Neuroscience fMRI Alzheimer's disease 2D-CNN phase synchronization index ROI |
| title | A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network |
| title_full | A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network |
| title_fullStr | A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network |
| title_full_unstemmed | A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network |
| title_short | A deep learning framework for identifying Alzheimer's disease using fMRI-based brain network |
| title_sort | deep learning framework for identifying alzheimer s disease using fmri based brain network |
| topic | fMRI Alzheimer's disease 2D-CNN phase synchronization index ROI |
| url | https://www.frontiersin.org/articles/10.3389/fnins.2023.1177424/full |
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