Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment
Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the...
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AIMS Press
2023-07-01
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| Series: | Mathematical Biosciences and Engineering |
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| Online Access: | https://www.aimspress.com/article/doi/10.3934/mbe.2023664?viewType=HTML |
| _version_ | 1797749191605223424 |
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| author | Chaofan Song Tongqiang Liu Huan Wang Haifeng Shi Zhuqing Jiao |
| author_facet | Chaofan Song Tongqiang Liu Huan Wang Haifeng Shi Zhuqing Jiao |
| author_sort | Chaofan Song |
| collection | DOAJ |
| description | Effectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis. |
| first_indexed | 2024-03-12T16:16:41Z |
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| institution | Directory Open Access Journal |
| issn | 1551-0018 |
| language | English |
| last_indexed | 2024-03-12T16:16:41Z |
| publishDate | 2023-07-01 |
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| series | Mathematical Biosciences and Engineering |
| spelling | doaj.art-a83be81757654093a6ae91955f7fe5942023-08-09T01:13:14ZengAIMS PressMathematical Biosciences and Engineering1551-00182023-07-01208148271484510.3934/mbe.2023664Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairmentChaofan Song 0Tongqiang Liu1Huan Wang 2Haifeng Shi3Zhuqing Jiao41. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China2. Department of Nephrology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China1. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, China3. Department of Radiology, The Affiliated Changzhou No.2 People's Hospital of Nanjing Medical University, Changzhou 213003, China1. School of Computer Science and Artificial Intelligence, Changzhou University, Changzhou 213164, ChinaEffectively selecting discriminative brain regions in multi-modal neuroimages is one of the effective means to reveal the neuropathological mechanism of end-stage renal disease associated with mild cognitive impairment (ESRDaMCI). Existing multi-modal feature selection methods usually depend on the Euclidean distance to measure the similarity between data, which tends to ignore the implied data manifold. A self-expression topological manifold based multi-modal feature selection method (SETMFS) is proposed to address this issue employing self-expression topological manifold. First, a dynamic brain functional network is established using functional magnetic resonance imaging (fMRI), after which the betweenness centrality is extracted. The feature matrix of fMRI is constructed based on this centrality measure. Second, the feature matrix of arterial spin labeling (ASL) is constructed by extracting the cerebral blood flow (CBF). Then, the topological relationship matrices are constructed by calculating the topological relationship between each data point in the two feature matrices to measure the intrinsic similarity between the features, respectively. Subsequently, the graph regularization is utilized to embed the self-expression model into topological manifold learning to identify the linear self-expression of the features. Finally, the selected well-represented feature vectors are fed into a multicore support vector machine (MKSVM) for classification. The experimental results show that the classification performance of SETMFS is significantly superior to several state-of-the-art feature selection methods, especially its classification accuracy reaches 86.10%, which is at least 4.34% higher than other comparable methods. This method fully considers the topological correlation between the multi-modal features and provides a reference for ESRDaMCI auxiliary diagnosis.https://www.aimspress.com/article/doi/10.3934/mbe.2023664?viewType=HTMLmild cognitive impairmenttopological manifoldmulti-modal feature selectionfunctional magnetic resonance imagingarterial spin labeling |
| spellingShingle | Chaofan Song Tongqiang Liu Huan Wang Haifeng Shi Zhuqing Jiao Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment Mathematical Biosciences and Engineering mild cognitive impairment topological manifold multi-modal feature selection functional magnetic resonance imaging arterial spin labeling |
| title | Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment |
| title_full | Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment |
| title_fullStr | Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment |
| title_full_unstemmed | Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment |
| title_short | Multi-modal feature selection with self-expression topological manifold for end-stage renal disease associated with mild cognitive impairment |
| title_sort | multi modal feature selection with self expression topological manifold for end stage renal disease associated with mild cognitive impairment |
| topic | mild cognitive impairment topological manifold multi-modal feature selection functional magnetic resonance imaging arterial spin labeling |
| url | https://www.aimspress.com/article/doi/10.3934/mbe.2023664?viewType=HTML |
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