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Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data

To improve the diagnosis and classification of Alzheimer’s disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1...

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Autores principales: Wang, Huiquan, Feng, Tianzi, Zhao, Zhe, Bai, Xue, Han, Guang, Wang, Jinhai, Dai, Zongrui, Wang, Rong, Zhao, Weibiao, Ren, Fuxin, Gao, Fei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315355/
https://www.ncbi.nlm.nih.gov/pubmed/35903535
http://dx.doi.org/10.3389/fnagi.2022.927217
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author Wang, Huiquan
Feng, Tianzi
Zhao, Zhe
Bai, Xue
Han, Guang
Wang, Jinhai
Dai, Zongrui
Wang, Rong
Zhao, Weibiao
Ren, Fuxin
Gao, Fei
author_facet Wang, Huiquan
Feng, Tianzi
Zhao, Zhe
Bai, Xue
Han, Guang
Wang, Jinhai
Dai, Zongrui
Wang, Rong
Zhao, Weibiao
Ren, Fuxin
Gao, Fei
author_sort Wang, Huiquan
collection PubMed
description To improve the diagnosis and classification of Alzheimer’s disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The t-test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD (r = 0.627), and the F statistics were largest (F = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD.
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spelling pubmed-93153552022-07-27 Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data Wang, Huiquan Feng, Tianzi Zhao, Zhe Bai, Xue Han, Guang Wang, Jinhai Dai, Zongrui Wang, Rong Zhao, Weibiao Ren, Fuxin Gao, Fei Front Aging Neurosci Neuroscience To improve the diagnosis and classification of Alzheimer’s disease (AD), a modeling method is proposed based on the combining magnetic resonance images (MRI) brain structural data with metabolite levels of the frontal and parietal regions. First, multi-atlas brain segmentation technology based on T1-weighted images and edited magnetic resonance spectroscopy (MRS) were used to extract data of 279 brain regions and levels of 12 metabolites from regions of interest (ROIs) in the frontal and parietal regions. The t-test combined with false discovery rate (FDR) correction was used to reduce the dimensionality in the data, and MRI structural data of 54 brain regions and levels of 4 metabolites that obviously correlated with AD were screened out. Lastly, the stacked auto-encoder neural network (SAE) was used to classify AD and healthy controls (HCs), which judged the effect of classification method by fivefold cross validation. The results indicated that the mean accuracy of the five experimental model increased from 96 to 100%, the AUC value increased from 0.97 to 1, specificity increased from 90 to 100%, and F1 value increased from 0.97 to 1. Comparing the effect of each metabolite on model performance revealed that the gamma-aminobutyric acid (GABA) + levels in the parietal region resulted in the most significant improvement in model performance, with the accuracy rate increasing from 96 to 98%, the AUC value increased from 0.97 to 0.99 and the specificity increasing from 90 to 95%. Moreover, the GABA + levels in the parietal region was significantly correlated with Mini Mental State Examination (MMSE) scores of patients with AD (r = 0.627), and the F statistics were largest (F = 25.538), which supports the hypothesis that dysfunctional GABAergic system play an important role in the pathogenesis of AD. Overall, our findings support that a comprehensive method that combines MRI structural and metabolic data of brain regions can improve model classification efficiency of AD. Frontiers Media S.A. 2022-07-12 /pmc/articles/PMC9315355/ /pubmed/35903535 http://dx.doi.org/10.3389/fnagi.2022.927217 Text en Copyright © 2022 Wang, Feng, Zhao, Bai, Han, Wang, Dai, Wang, Zhao, Ren and Gao. 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
Wang, Huiquan
Feng, Tianzi
Zhao, Zhe
Bai, Xue
Han, Guang
Wang, Jinhai
Dai, Zongrui
Wang, Rong
Zhao, Weibiao
Ren, Fuxin
Gao, Fei
Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data
title Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data
title_full Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data
title_fullStr Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data
title_full_unstemmed Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data
title_short Classification of Alzheimer’s Disease Based on Deep Learning of Brain Structural and Metabolic Data
title_sort classification of alzheimer’s disease based on deep learning of brain structural and metabolic data
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9315355/
https://www.ncbi.nlm.nih.gov/pubmed/35903535
http://dx.doi.org/10.3389/fnagi.2022.927217
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