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: Yang Xi, Qian Wang, Chenxue Wu, Lu Zhang, Ying Chen, Zhu Lan
Format: Article
Language:English
Published: Springer 2024-12-01
Series:Complex & Intelligent Systems
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Online Access:https://doi.org/10.1007/s40747-024-01680-0
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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.
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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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