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The ability to accurately detect onset of dementia is important in thetreatment of the disease. Clinically, the diagnosis of Alzheimer Disease (AD)and Mild Cognitive Impairment (MCI) patients are based on an integratedassessment of psychological tests and brain imaging such as positron emissiontomography (PET) and anatomical magnetic resonance imaging (MRI). In this workusing two different datasets, we propose a behavior score-embedded encodernetwork (BSEN) that integrates regularly adminstrated psychological testsinformation into the encoding procedure of representing subject's restingstatefMRI data for automatic classification tasks. BSEN is based on a 3Dconvolutional autoencoder structure with contrastive loss jointly optimizedusing behavior scores from MiniMental State Examination (MMSE) and ClinicalDementia Rating (CDR). Our proposed classification framework of using BSENachieved an overall recognition accuracy of 59.44% (3-class classification: AD,MCI and Healthy Control), and we further extracted the most discriminativeregions between healthy control (HC) and AD patients.

Behavior Score-Embedded Brain Encoder Network for Improved Classification of Alzheimer Disease Using Resting State fMRI