Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging data.

Journal: Proceedings. IEEE International Conference on Bioinformatics and Biomedicine
Published Date: Dec 1, 2021

Abstract

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.

Authors

  • Mansu Kim
    Department of Artificial Intelligence, Catholic University of Korea, Bucheon, South Korea.
  • Jaesik Kim
    Department of Computer Engineering, Ajou University, Suwon, South Korea.
  • Jeffrey Qu
    School of Engineering and Applied Sciences, University of Pennsylvania, Philadelphia, USA.
  • Heng Huang
    Department of Electrical and Computer Engineering, University of Pittsburgh, Pittsburgh, USA.
  • Qi Long
    Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.
  • Kyung-Ah Sohn
    Department of Artificial Intelligence, Ajou University, Suwon, South Korea.
  • Dokyoon Kim
    Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, USA.
  • Li Shen
    Department of Clinical Pharmacy, Children's Hospital of Fudan University, National Children's Medical Center, Shanghai, China.

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