Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data
Abstract Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) play a significant role in the early diagnosis of Alzheimer’s disease (AD) and can be helpful in early treatment to reduce the risk of conversion to AD. We proposed a classification method o...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Springer
2024-12-01
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| Series: | Complex & Intelligent Systems |
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| Online Access: | https://doi.org/10.1007/s40747-024-01680-0 |
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| _version_ | 1832571165293412352 |
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| author | Yang Xi Qian Wang Chenxue Wu Lu Zhang Ying Chen Zhu Lan |
| author_facet | Yang Xi Qian Wang Chenxue Wu Lu Zhang Ying Chen Zhu Lan |
| author_sort | Yang Xi |
| collection | DOAJ |
| description | Abstract Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) play a significant role in the early diagnosis of Alzheimer’s disease (AD) and can be helpful in early treatment to reduce the risk of conversion to AD. We proposed a classification method of sMCIs and pMCIs based on multi-modality data fusion of single-nucleotide polymorphisms (SNP), ratio of gray matter volume (RGV) obtained by morphometric measures, and sMRI images to predict the progression of AD. We validated the effectiveness of the proposed method by applying it to the task of identifying the disease status on the Alzheimer’s Disease Neuroimaging Initiative dataset. The results showed that the classification performances of our method was better than other state-of-the-art methods, and the accuracy rate for the classification of pMCI and sMCI reached 94.37%. The accuracy of our method was better than that of existing classification methods based on multi-modality images, and the accuracy rate for the classification of pMCI and sMCI reached 94.37%. Our study demonstrated that compared with unimodal and bimodal data, the method based on trimodal data fusion can better distinguish sMCI and pMCI, obtaining higher prediction accuracy for AD conversion. In addition, as a morphological feature, ratio of gray matter volume played a key role in distinguish of sMCI and pMCI, which can be used for the early diagnosis of AD. |
| format | Article |
| id | doaj-art-54a90e82381a44229825bfee0e45fc7f |
| institution | Kabale University |
| issn | 2199-4536 2198-6053 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Springer |
| record_format | Article |
| series | Complex & Intelligent Systems |
| spelling | doaj-art-54a90e82381a44229825bfee0e45fc7f2025-02-02T12:48:50ZengSpringerComplex & Intelligent Systems2199-45362198-60532024-12-0111112010.1007/s40747-024-01680-0Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic dataYang Xi0Qian Wang1Chenxue Wu2Lu Zhang3Ying Chen4Zhu Lan5School of Computer Science, Northeast Electric Power UniversitySchool of Computer Science and Technology, Changchun University of Science and TechnologySchool of Computer Science, Northeast Electric Power UniversitySchool of Computer Science, Northeast Electric Power UniversitySchool of Computer Science, Northeast Electric Power UniversitySchool of Computer Science, Northeast Electric Power UniversityAbstract Identifying progressive mild cognitive impairment (pMCI) and stable mild cognitive impairment (sMCI) play a significant role in the early diagnosis of Alzheimer’s disease (AD) and can be helpful in early treatment to reduce the risk of conversion to AD. We proposed a classification method of sMCIs and pMCIs based on multi-modality data fusion of single-nucleotide polymorphisms (SNP), ratio of gray matter volume (RGV) obtained by morphometric measures, and sMRI images to predict the progression of AD. We validated the effectiveness of the proposed method by applying it to the task of identifying the disease status on the Alzheimer’s Disease Neuroimaging Initiative dataset. The results showed that the classification performances of our method was better than other state-of-the-art methods, and the accuracy rate for the classification of pMCI and sMCI reached 94.37%. The accuracy of our method was better than that of existing classification methods based on multi-modality images, and the accuracy rate for the classification of pMCI and sMCI reached 94.37%. Our study demonstrated that compared with unimodal and bimodal data, the method based on trimodal data fusion can better distinguish sMCI and pMCI, obtaining higher prediction accuracy for AD conversion. In addition, as a morphological feature, ratio of gray matter volume played a key role in distinguish of sMCI and pMCI, which can be used for the early diagnosis of AD.https://doi.org/10.1007/s40747-024-01680-0ADsMCIpMCIMulti-modality dataPrediction |
| spellingShingle | Yang Xi Qian Wang Chenxue Wu Lu Zhang Ying Chen Zhu Lan Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data Complex & Intelligent Systems AD sMCI pMCI Multi-modality data Prediction |
| title | Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data |
| title_full | Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data |
| title_fullStr | Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data |
| title_full_unstemmed | Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data |
| title_short | Predicting conversion of Alzheimer’s disease based on multi-modal fusion of neuroimaging and genetic data |
| title_sort | predicting conversion of alzheimer s disease based on multi modal fusion of neuroimaging and genetic data |
| topic | AD sMCI pMCI Multi-modality data Prediction |
| url | https://doi.org/10.1007/s40747-024-01680-0 |
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