A Graph Convolutional Network Based on Univariate Neurodegeneration Biomarker for Alzheimer's Disease Diagnosis

Objective: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD. Methods: We proposed a univari...

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Vydáno v:IEEE journal of translational engineering in health and medicine Ročník 11; s. 405 - 416
Hlavní autoři: Qu, Zongshuai, Yao, Tao, Liu, Xinghui, Wang, Gang
Médium: Journal Article
Jazyk:angličtina
Vydáno: United States IEEE 01.01.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Témata:
Abnormalities
Accuracy
Alzheimer Disease - diagnosis
Alzheimer's disease
Artificial neural networks
Atrophy
attention module
Biomarkers
Cerebral cortex
Classification
Convolutional neural networks
graph convolutional network
Humans
Magnetic resonance imaging
Magnetic Resonance Imaging - methods
Medical imaging
Modules
Morphology
Neurodegeneration
Neurodegenerative Diseases
Neuroimaging
Neuroimaging - methods
Signs and symptoms
Similarity
Surface morphology
univariate neurodegeneration biomarker
graph convolutional network
univariate neurodegeneration biomarker
magnetic resonance imaging
Alzheimer’s disease
attention module
ISSN:2168-2372, 2168-2372
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Popis
Shrnutí:Objective: Alzheimer's disease (AD) is a progressive and irreversible neurodegenerative disease that is not easily detectable in the early stage. This study proposed an efficient method of applying a graph convolutional network (GCN) on the early prediction of AD. Methods: We proposed a univariate neurodegeneration biomarker (UNB) based GCN semi-supervised classification framework. We generated UNB by comparing the similarity of individual morphological atrophy pattern and the atrophy pattern of <inline-formula> <tex-math notation="LaTeX">\text{A}\beta + </tex-math></inline-formula> AD group according to the brain morphological abnormalities induced by AD. For the GCN semi-supervised classification model, we took the UNBs of individuals as the features of nodes and constructed the weight of edges according to the similarity of phenotypic information between individuals, which explored the essential features of individuals through spectral graph convolution. The attention module was constructed and embedded into the GCN framework, which may refine the input morphological features to highlight the main impact of AD on the cerebral cortex and weaken the instability caused by individual diversities, thereby identifying the significant ROIs affected by AD and improving the classification accuracy. Results: We tested the UNB-GCN framework on the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The estimated minimum sample sizes were 156, 349 and 423 for the longitudinal <inline-formula> <tex-math notation="LaTeX">\text{A}\beta + </tex-math></inline-formula> AD, <inline-formula> <tex-math notation="LaTeX">\text{A}\beta + </tex-math></inline-formula> mild cognitive impairment (MCI) and <inline-formula> <tex-math notation="LaTeX">\text{A}\beta + </tex-math></inline-formula> cognitively unimpaired (CU) groups, respectively. And the proposed UNB-GCN framework combined with the attention module can effectively improve the classification performance with 93.90% classification accuracy for AD vs. CU and 82.05% for AD vs. MCI on the validation set. Conclusion: The proposed UNB measures were superior to the conventional volume measures in describing the AD-induced cerebral cortex morphological changes. And the UNB-GCN framework combined with attention module may effectively improve the classification performance between MCI subjects and AD patients. Clinical and Translational Impact Statement: This study aims to predict the early AD patients, so as to help clinicians develop effective interventions to delay the deterioration of AD symptoms.
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ISSN:2168-2372
2168-2372
DOI:10.1109/JTEHM.2023.3285723