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BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease

Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer’s Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch traini...

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Autores principales: Cao, Yu, Kuai, Hongzhi, Liang, Peipeng, Pan, Jeng-Shyang, Yan, Jianzhuo, Zhong, Ning
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326383/
https://www.ncbi.nlm.nih.gov/pubmed/37424996
http://dx.doi.org/10.3389/fnins.2023.1202382
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author Cao, Yu
Kuai, Hongzhi
Liang, Peipeng
Pan, Jeng-Shyang
Yan, Jianzhuo
Zhong, Ning
author_facet Cao, Yu
Kuai, Hongzhi
Liang, Peipeng
Pan, Jeng-Shyang
Yan, Jianzhuo
Zhong, Ning
author_sort Cao, Yu
collection PubMed
description Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer’s Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance.
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spelling pubmed-103263832023-07-08 BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease Cao, Yu Kuai, Hongzhi Liang, Peipeng Pan, Jeng-Shyang Yan, Jianzhuo Zhong, Ning Front Neurosci Neuroscience Recent advancements in AI, big data analytics, and magnetic resonance imaging (MRI) have revolutionized the study of brain diseases such as Alzheimer’s Disease (AD). However, most AI models used for neuroimaging classification tasks have limitations in their learning strategies, that is batch training without the incremental learning capability. To address such limitations, the systematic Brain Informatics methodology is reconsidered to realize evidence combination and fusion computing with multi-modal neuroimaging data through continuous learning. Specifically, we introduce the BNLoop-GAN (Loop-based Generative Adversarial Network for Brain Network) model, utilizing multiple techniques such as conditional generation, patch-based discrimination, and Wasserstein gradient penalty to learn the implicit distribution of brain networks. Moreover, a multiple-loop-learning algorithm is developed to combine evidence with better sample contribution ranking during training processes. The effectiveness of our approach is demonstrated through a case study on the classification of individuals with AD and healthy control groups using various experimental design strategies and multi-modal brain networks. The BNLoop-GAN model with multi-modal brain networks and multiple-loop-learning can improve classification performance. Frontiers Media S.A. 2023-06-23 /pmc/articles/PMC10326383/ /pubmed/37424996 http://dx.doi.org/10.3389/fnins.2023.1202382 Text en Copyright © 2023 Cao, Kuai, Liang, Pan, Yan and Zhong. 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
Cao, Yu
Kuai, Hongzhi
Liang, Peipeng
Pan, Jeng-Shyang
Yan, Jianzhuo
Zhong, Ning
BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease
title BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease
title_full BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease
title_fullStr BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease
title_full_unstemmed BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease
title_short BNLoop-GAN: a multi-loop generative adversarial model on brain network learning to classify Alzheimer’s disease
title_sort bnloop-gan: a multi-loop generative adversarial model on brain network learning to classify alzheimer’s disease
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10326383/
https://www.ncbi.nlm.nih.gov/pubmed/37424996
http://dx.doi.org/10.3389/fnins.2023.1202382
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