國家衛生研究院 NHRI:Item 3990099045/12937

NHRI > Institute of Biomedical Engineering and Nanomedicine > Li-Wei Kuo > Conference Papers/Meeting Abstract > Item 3990099045/12937

Please use this identifier to cite or link to this item: http://ir.nhri.org.tw/handle/3990099045/12937

Title:	Behavior score-embedded brain encoder network for improved classification of Alzheimer disease using resting state fMRI
Authors:	Hsieh, WT;Lefort-Besnard, J;Yang, HC;Kuo, LW;Lee, CC
Contributors:	Institute of Biomedical Engineering and Nanomedicine
Abstract:	The ability to accurately detect onset of dementia is important in the treatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD) and Mild Cognitive Impairment (MCI) patients are based on an integrated assessment of psychological tests and brain imaging such as positron emission tomography (PET) and anatomical magnetic resonance imaging (MRI). In this work using two different datasets, we propose a behavior score-embedded encoder network (BSEN) that integrates regularly adminstrated psychological tests information into the encoding procedure of representing subject's resting-state fMRI data for automatic classification tasks. BSEN is based on a 3D convolutional autoencoder structure with contrastive loss jointly optimized using behavior scores from Mini-Mental State Examination (MMSE) and Clinical Dementia Rating (CDR). Our proposed classification framework of using BSEN achieved an overall recognition accuracy of 59.44% (3-class classification: AD, MCI and Healthy Control), and we further extracted the most discriminative regions between healthy control (HC) and AD patients.
Date:	2020-08-27
Relation:	2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 2020 Aug 27:5486-5489.
Link to:	http://dx.doi.org/10.1109/EMBC44109.2020.9175312
Cited Times(WOS):	https://www.webofscience.com/wos/woscc/full-record/WOS:000621592205193
Cited Times(Scopus):	https://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85091027983
Appears in Collections:	[Li-Wei Kuo] Conference Papers/Meeting Abstract

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