Improving Alzheimer Diagnoses With An Interpretable Deep Learning Framework: Including Neuropsychiatric Symptoms.

ABSTRACT

Alzheimer's disease (AD) is a prevalent neurodegenerative disorder characterized by the progressive cognitive decline. Among the various clinical symptoms, neuropsychiatric symptoms (NPS) commonly occur during the course of AD. Previous researches have demonstrated a strong association between NPS and severity of AD, while the research methods are not sufficiently intuitive. Here, we report a hybrid deep learning framework for AD diagnosis using multimodal inputs such as structural MRI, behavioral scores, age, and gender information. The framework uses a 3D convolutional neural network to automatically extract features from MRI. The imaging features are passed to the Principal Component Analysis for dimensionality reduction, which fuse with non-imaging information to improve the diagnosis of AD. According to the experimental results, our model achieves an accuracy of 0.91 and an area under the curve of 0.97 in the task of classifying AD and cognitively normal individuals. SHapley Additive exPlanations are used to visually exhibit the contribution of specific NPS in the proposed model. Among all behavioral symptoms, apathy plays a particularly important role in the diagnosis of AD, which can be considered a valuable factor in further studies, as well as clinical trials.