神经影像学
计算机科学
人工智能
机器学习
图形
人工神经网络
认知
桥(图论)
阿尔茨海默病神经影像学倡议
支持向量机
模式识别(心理学)
神经科学
认知障碍
心理学
医学
内科学
理论计算机科学
作者
Mansu Kim,Jae‐Sik Kim,Jeffrey Qu,Heng Huang,Qi Long,Kyung-Ah Sohn,Dokyoon Kim,Li Shen
标识
DOI:10.1109/bibm52615.2021.9669504
摘要
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.
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