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Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease

Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source re...

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Autores principales: Meng, Xianglian, Liu, Junlong, Fan, Xiang, Bian, Chenyuan, Wei, Qingpeng, Wang, Ziwei, Liu, Wenjie, Jiao, Zhuqing
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149574/
https://www.ncbi.nlm.nih.gov/pubmed/35651528
http://dx.doi.org/10.3389/fnagi.2022.911220
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author Meng, Xianglian
Liu, Junlong
Fan, Xiang
Bian, Chenyuan
Wei, Qingpeng
Wang, Ziwei
Liu, Wenjie
Jiao, Zhuqing
author_facet Meng, Xianglian
Liu, Junlong
Fan, Xiang
Bian, Chenyuan
Wei, Qingpeng
Wang, Ziwei
Liu, Wenjie
Jiao, Zhuqing
author_sort Meng, Xianglian
collection PubMed
description Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD.
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spelling pubmed-91495742022-05-31 Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease Meng, Xianglian Liu, Junlong Fan, Xiang Bian, Chenyuan Wei, Qingpeng Wang, Ziwei Liu, Wenjie Jiao, Zhuqing Front Aging Neurosci Neuroscience Alzheimer’s disease (AD) is a neurodegenerative brain disease, and it is challenging to mine features that distinguish AD and healthy control (HC) from multiple datasets. Brain network modeling technology in AD using single-modal images often lacks supplementary information regarding multi-source resolution and has poor spatiotemporal sensitivity. In this study, we proposed a novel multi-modal LassoNet framework with a neural network for AD-related feature detection and classification. Specifically, data including two modalities of resting-state functional magnetic resonance imaging (rs-fMRI) and diffusion tensor imaging (DTI) were adopted for predicting pathological brain areas related to AD. The results of 10 repeated experiments and validation experiments in three groups prove that our proposed framework outperforms well in classification performance, generalization, and reproducibility. Also, we found discriminative brain regions, such as Hippocampus, Frontal_Inf_Orb_L, Parietal_Sup_L, Putamen_L, Fusiform_R, etc. These discoveries provide a novel method for AD research, and the experimental study demonstrates that the framework will further improve our understanding of the mechanisms underlying the development of AD. Frontiers Media S.A. 2022-05-16 /pmc/articles/PMC9149574/ /pubmed/35651528 http://dx.doi.org/10.3389/fnagi.2022.911220 Text en Copyright © 2022 Meng, Liu, Fan, Bian, Wei, Wang, Liu and Jiao. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Meng, Xianglian
Liu, Junlong
Fan, Xiang
Bian, Chenyuan
Wei, Qingpeng
Wang, Ziwei
Liu, Wenjie
Jiao, Zhuqing
Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease
title Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease
title_full Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease
title_fullStr Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease
title_full_unstemmed Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease
title_short Multi-Modal Neuroimaging Neural Network-Based Feature Detection for Diagnosis of Alzheimer’s Disease
title_sort multi-modal neuroimaging neural network-based feature detection for diagnosis of alzheimer’s disease
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9149574/
https://www.ncbi.nlm.nih.gov/pubmed/35651528
http://dx.doi.org/10.3389/fnagi.2022.911220
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