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Interpretable temporal graph neural network for prognostic prediction of Alzheimer's disease using longitudinal neuroimaging dataoa mark
  • Kim, Mansu ;
  • Kim, Jaesik ;
  • Qu, Jeffrey ;
  • Huang, Heng ;
  • Long, Qi ;
  • Sohn, Kyung Ah ;
  • Kim, Dokyoon ;
  • Shen, Li
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Publication Year
2021-01-01
Journal
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021
Publisher
Institute of Electrical and Electronics Engineers Inc.
Citation
Proceedings - 2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021, pp.1381-1384
Keyword
Alzheimer's diseaseBrain imagingGraph neural networkLongitudinal data analysisPrognostic prediction
Mesh Keyword
Alzheimers diseaseBrain disordersBrain imagingCognitive declineGraph neural networksLongitudinal data analyseMemory lossNeurodegenerativePrognostic predictionTemporal graphs
All Science Classification Codes (ASJC)
Artificial IntelligenceComputer Science ApplicationsBiomedical EngineeringHealth InformaticsInformation Systems and Management
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.
Language
eng
URI
https://aurora.ajou.ac.kr/handle/2018.oak/36669
https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85125168686&origin=inward
DOI
https://doi.org/10.1109/bibm52615.2021.9669504
Journal URL
http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9669261
Type
Conference
Funding
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).
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