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Behavior Score-Embedded Brain Encoder Network for Improved Classification of Alzheimer Disease Using Resting State fMRI
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 restingstate fMRI data for automatic classification tasks. BSEN is based on a 3D convolutional autoencoder structure with contrastive loss jointly optimized using behavior scores from MiniMental 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.
Figures
A schematic of the BSEN architecture in classifying HC, MCI and AD. The input of the network is BOLD signal of subjects per time.
A schematic of the BSEN architecture in classifying HC, MCI and AD. The input of the network is BOLD signal of subjects per time.
Authors
Hao-Chun Yang Chi-Chun Lee
Publication Date
2020/07/20
Conference
EMBC
2020 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)
DOI
10.1109/embc44109.2020.9175312
Publisher
IEEE