  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.
