Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis
Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capt...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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PeerJ Inc.
2024-04-01
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| Series: | PeerJ |
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| Online Access: | https://peerj.com/articles/17078.pdf |
| _version_ | 1797214228162019328 |
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| author | Feng Zhao Ke Lv Shixin Ye Xiaobo Chen Hongyu Chen Sizhe Fan Ning Mao Yande Ren |
| author_facet | Feng Zhao Ke Lv Shixin Ye Xiaobo Chen Hongyu Chen Sizhe Fan Ning Mao Yande Ren |
| author_sort | Feng Zhao |
| collection | DOAJ |
| description | Dynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties. |
| first_indexed | 2024-04-24T11:10:50Z |
| format | Article |
| id | doaj.art-7815e40d9db24c9a820cbbace0abcd4d |
| institution | Directory Open Access Journal |
| issn | 2167-8359 |
| language | English |
| last_indexed | 2024-04-24T11:10:50Z |
| publishDate | 2024-04-01 |
| publisher | PeerJ Inc. |
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| series | PeerJ |
| spelling | doaj.art-7815e40d9db24c9a820cbbace0abcd4d2024-04-11T15:05:06ZengPeerJ Inc.PeerJ2167-83592024-04-0112e1707810.7717/peerj.17078Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysisFeng Zhao0Ke Lv1Shixin Ye2Xiaobo Chen3Hongyu Chen4Sizhe Fan5Ning Mao6Yande Ren7School of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool of Computer Science and Technology, Shandong Technology and Business University, Yantai, ChinaSchool Hospital, Shandong Technology and Business University, Yantai, ChinaCanada Qingdao Secondary School (CQSS), Qingdao, ChinaDepartment of Radiology, Yantai Yuhuangding Hospital, Yantai, ChinaDepartment of Radiology, The Affiliated Hospital of Qingdao University, Qingdao, ChinaDynamic functional connectivity, derived from resting-state functional magnetic resonance imaging (rs-fMRI), has emerged as a crucial instrument for investigating and supporting the diagnosis of neurological disorders. However, prevalent features of dynamic functional connectivity predominantly capture either temporal or spatial properties, such as mean and global efficiency, neglecting the significant information embedded in the fusion of spatial and temporal attributes. In addition, dynamic functional connectivity suffers from the problem of temporal mismatch, i.e., the functional connectivity of different subjects at the same time point cannot be matched. To address these problems, this article introduces a novel feature extraction framework grounded in two-directional two-dimensional principal component analysis. This framework is designed to extract features that integrate both spatial and temporal properties of dynamic functional connectivity. Additionally, we propose to use Fourier transform to extract temporal-invariance properties contained in dynamic functional connectivity. Experimental findings underscore the superior performance of features extracted by this framework in classification experiments compared to features capturing individual properties.https://peerj.com/articles/17078.pdfDynamic functional connectivitySpatial and temporal properties |
| spellingShingle | Feng Zhao Ke Lv Shixin Ye Xiaobo Chen Hongyu Chen Sizhe Fan Ning Mao Yande Ren Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis PeerJ Dynamic functional connectivity Spatial and temporal properties |
| title | Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis |
| title_full | Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis |
| title_fullStr | Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis |
| title_full_unstemmed | Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis |
| title_short | Integration of temporal & spatial properties of dynamic functional connectivity based on two-directional two-dimensional principal component analysis for disease analysis |
| title_sort | integration of temporal spatial properties of dynamic functional connectivity based on two directional two dimensional principal component analysis for disease analysis |
| topic | Dynamic functional connectivity Spatial and temporal properties |
| url | https://peerj.com/articles/17078.pdf |
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