Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment
Combined arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) can reveal more comprehensive properties of the spatiotemporal and quantitative properties of brain networks. Imaging markers of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) will be...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Bioengineering |
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| Online Access: | https://www.mdpi.com/2306-5354/10/8/958 |
| _version_ | 1797585441600307200 |
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| author | Xidong Fu Chaofan Song Rupu Zhang Haifeng Shi Zhuqing Jiao |
| author_facet | Xidong Fu Chaofan Song Rupu Zhang Haifeng Shi Zhuqing Jiao |
| author_sort | Xidong Fu |
| collection | DOAJ |
| description | Combined arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) can reveal more comprehensive properties of the spatiotemporal and quantitative properties of brain networks. Imaging markers of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) will be sought from these properties. The current multimodal classification methods often neglect to collect high-order relationships of brain regions and remove noise from the feature matrix. A multimodal classification framework is proposed to address this issue using hypergraph latent relation (HLR). A brain functional network with hypergraph structural information is constructed by fMRI data. The feature matrix is obtained through graph theory (GT). The cerebral blood flow (CBF) from ASL is selected as the second modal feature matrix. Then, the adaptive similarity matrix is constructed by learning the latent relation between feature matrices. Latent relation adaptive similarity learning (LRAS) is introduced to multi-task feature learning to construct a multimodal feature selection method based on latent relation (LRMFS). The experimental results show that the best classification accuracy (ACC) reaches 88.67%, at least 2.84% better than the state-of-the-art methods. The proposed framework preserves more valuable information between brain regions and reduces noise among feature matrixes. It provides an essential reference value for ESRDaMCI recognition. |
| first_indexed | 2024-03-11T00:06:18Z |
| format | Article |
| id | doaj.art-36527326cf8640e4a0ea89ec2ad41716 |
| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-11T00:06:18Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-36527326cf8640e4a0ea89ec2ad417162023-11-19T00:18:23ZengMDPI AGBioengineering2306-53542023-08-0110895810.3390/bioengineering10080958Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive ImpairmentXidong Fu0Chaofan Song1Rupu Zhang2Haifeng Shi3Zhuqing Jiao4School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, ChinaSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, ChinaSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, ChinaDepartment of Radiology, The Affiliated Changzhou No.2 People’s Hospital of Nanjing Medical University, Changzhou 213003, ChinaSchool of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, ChinaCombined arterial spin labeling (ASL) and functional magnetic resonance imaging (fMRI) can reveal more comprehensive properties of the spatiotemporal and quantitative properties of brain networks. Imaging markers of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI) will be sought from these properties. The current multimodal classification methods often neglect to collect high-order relationships of brain regions and remove noise from the feature matrix. A multimodal classification framework is proposed to address this issue using hypergraph latent relation (HLR). A brain functional network with hypergraph structural information is constructed by fMRI data. The feature matrix is obtained through graph theory (GT). The cerebral blood flow (CBF) from ASL is selected as the second modal feature matrix. Then, the adaptive similarity matrix is constructed by learning the latent relation between feature matrices. Latent relation adaptive similarity learning (LRAS) is introduced to multi-task feature learning to construct a multimodal feature selection method based on latent relation (LRMFS). The experimental results show that the best classification accuracy (ACC) reaches 88.67%, at least 2.84% better than the state-of-the-art methods. The proposed framework preserves more valuable information between brain regions and reduces noise among feature matrixes. It provides an essential reference value for ESRDaMCI recognition.https://www.mdpi.com/2306-5354/10/8/958end-stage renal diseasemild cognitive impairmenthypergraphlatent relation adaptive similarity learningmultimodal classification framework |
| spellingShingle | Xidong Fu Chaofan Song Rupu Zhang Haifeng Shi Zhuqing Jiao Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment Bioengineering end-stage renal disease mild cognitive impairment hypergraph latent relation adaptive similarity learning multimodal classification framework |
| title | Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment |
| title_full | Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment |
| title_fullStr | Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment |
| title_full_unstemmed | Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment |
| title_short | Multimodal Classification Framework Based on Hypergraph Latent Relation for End-Stage Renal Disease Associated with Mild Cognitive Impairment |
| title_sort | multimodal classification framework based on hypergraph latent relation for end stage renal disease associated with mild cognitive impairment |
| topic | end-stage renal disease mild cognitive impairment hypergraph latent relation adaptive similarity learning multimodal classification framework |
| url | https://www.mdpi.com/2306-5354/10/8/958 |
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