Alzheimer's disease (AD) is a progressive neurodegenerative brain disorder characterized by memory loss and cognitive decline. Early detection and accurate prognosis of AD is an important research topic, and numerous machine learning methods have been proposed to solve this problem. However, traditional machine learning models are facing challenges in effectively integrating longitudinal neuroimaging data and biologically meaningful structure and knowledge to build accurate and interpretable prognostic predictors. To bridge this gap, we propose an interpretable graph neural network (GNN) model for AD prognostic prediction based on longitudinal neuroimaging data while embracing the valuable knowledge of structural brain connectivity. In our empirical study, we demonstrate that 1) the proposed model outperforms several competing models (i.e., DNN, SVM) in terms of prognostic prediction accuracy, and 2) our model can capture neuroanatomical contribution to the prognostic predictor and yield biologically meaningful interpretation to facilitate better mechanistic understanding of the Alzheimer's disease. Source code is available at https://github.com/JaesikKim/temporal-GNN.
ACKNOWLEDGMENT This work was supported in part by the National Institutes of Health [U01 AG068057, RF1 AG063481, R01 LM013463, R01 AG071470, RF1 AG063481] and National Science Foundation IIS 1837964. This work was also supported in part by the National Research Foundation of Korea [NRF-2020R1A6A3A03038525]. Imaging and clinical data used in the preparation of this article were obtained from the Alzheimer\\u2019s Disease Neuroimaging Initiative (ADNI) database (adni.loni.usc.edu). Reference brain connectivity network was computed using data provided by the Human Connectome Project, WU-Minn Consortium (Principal Investigators: David Van Essen and Kamil Ugurbil; 1U54MH091657).
