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Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features

Due to the clinical continuum of Alzheimer’s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC),...

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Autores principales: Qin, Yao, Cui, Jing, Ge, Xiaoyan, Tian, Yuling, Han, Hongjuan, Fan, Zhao, Liu, Long, Luo, Yanhong, Yu, Hongmei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399682/
https://www.ncbi.nlm.nih.gov/pubmed/36034132
http://dx.doi.org/10.3389/fnagi.2022.935055
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author Qin, Yao
Cui, Jing
Ge, Xiaoyan
Tian, Yuling
Han, Hongjuan
Fan, Zhao
Liu, Long
Luo, Yanhong
Yu, Hongmei
author_facet Qin, Yao
Cui, Jing
Ge, Xiaoyan
Tian, Yuling
Han, Hongjuan
Fan, Zhao
Liu, Long
Luo, Yanhong
Yu, Hongmei
author_sort Qin, Yao
collection PubMed
description Due to the clinical continuum of Alzheimer’s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD, and (2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, namely, cortical thickness (CTh), fractal dimension (FD), gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination (MMSE) scores at four-time points after the initial visit, we identified an optimal subset of 40 imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using the AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus (PHG), parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus (STS). Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice.
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spelling pubmed-93996822022-08-25 Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features Qin, Yao Cui, Jing Ge, Xiaoyan Tian, Yuling Han, Hongjuan Fan, Zhao Liu, Long Luo, Yanhong Yu, Hongmei Front Aging Neurosci Neuroscience Due to the clinical continuum of Alzheimer’s disease (AD), the accuracy of early diagnostic remains unsatisfactory and warrants further research. The objectives of this study were: (1) to develop an effective hierarchical multi-class framework for clinical populations, namely, normal cognition (NC), early mild cognitive impairment (EMCI), late mild cognitive impairment (LMCI), and AD, and (2) to explore the geometric properties of cognition-related anatomical structures in the cerebral cortex. A total of 1,670 participants were enrolled in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database, comprising 985 participants (314 NC, 208 EMCI, 258 LMCI, and 205 AD) in the model development set and 685 participants (417 NC, 110 EMCI, 83 LMCI, and 75 AD) after 2017 in the temporal validation set. Four cortical geometric properties for 148 anatomical structures were extracted, namely, cortical thickness (CTh), fractal dimension (FD), gyrification index (GI), and sulcus depth (SD). By integrating these imaging features with Mini-Mental State Examination (MMSE) scores at four-time points after the initial visit, we identified an optimal subset of 40 imaging features using the temporally constrained group sparse learning method. The combination of selected imaging features and clinical variables improved the multi-class performance using the AdaBoost algorithm, with overall accuracy rates of 0.877 in the temporal validation set. Clinical Dementia Rating (CDR) was the primary clinical variable associated with AD-related populations. The most discriminative imaging features included the bilateral CTh of the dorsal part of the posterior cingulate gyrus, parahippocampal gyrus (PHG), parahippocampal part of the medial occipito-temporal gyrus, and angular gyrus, the GI of the left inferior segment of the insula circular sulcus, and the CTh and SD of the left superior temporal sulcus (STS). Our hierarchical multi-class framework underscores the utility of combining cognitive variables with imaging features and the reliability of surface-based morphometry, facilitating more accurate early diagnosis of AD in clinical practice. Frontiers Media S.A. 2022-08-10 /pmc/articles/PMC9399682/ /pubmed/36034132 http://dx.doi.org/10.3389/fnagi.2022.935055 Text en Copyright © 2022 Qin, Cui, Ge, Tian, Han, Fan, Liu, Luo and Yu. 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
Qin, Yao
Cui, Jing
Ge, Xiaoyan
Tian, Yuling
Han, Hongjuan
Fan, Zhao
Liu, Long
Luo, Yanhong
Yu, Hongmei
Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features
title Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features
title_full Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features
title_fullStr Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features
title_full_unstemmed Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features
title_short Hierarchical multi-class Alzheimer’s disease diagnostic framework using imaging and clinical features
title_sort hierarchical multi-class alzheimer’s disease diagnostic framework using imaging and clinical features
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9399682/
https://www.ncbi.nlm.nih.gov/pubmed/36034132
http://dx.doi.org/10.3389/fnagi.2022.935055
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