Method on Alzheimer’s Disease Classification Utilizing Graph Neural Network with Nearest Neighborhood Aggregation

HAN Liang; LIU Yuan; PU Xiu-juan; TAN Yun-fan; REN Qing

doi:10.12263/DZXB.20240770

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Method on Alzheimer’s Disease Classification Utilizing Graph Neural Network with Nearest Neighborhood Aggregation

PAPERS | 更新时间：2025-12-08

- Method on Alzheimer’s Disease Classification Utilizing Graph Neural Network with Nearest Neighborhood Aggregation
- ACTA ELECTRONICA SINICA Vol. 53, Issue 3, Pages: 1000-1013(2025)
- 作者机构：
  
  1.重庆大学微电子与通信工程学院，重庆 401331
  2.生物感知与多模态智能信息处理重庆市重点实验室，重庆 401331
- 作者简介：
- 基金信息：
- DOI：10.12263/DZXB.20240770
  CLC： TN911.7;
- Received：21 August 2024，
  
  Revised：2025-01-10，
  
  Published：25 March 2025
- 稿件说明：
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韩亮, 刘媛, 蒲秀娟, 等. 使用最近邻域聚合图神经网络的阿尔茨海默病分类方法[J]. 电子学报, 2025, 53(03): 1000-1013.

HAN Liang, LIU Yuan, PU Xiu-juan, et al. Method on Alzheimer’s Disease Classification Utilizing Graph Neural Network with Nearest Neighborhood Aggregation[J]. Acta Electronica Sinica, 2025, 53(03): 1000-1013.
韩亮, 刘媛, 蒲秀娟, 等. 使用最近邻域聚合图神经网络的阿尔茨海默病分类方法[J]. 电子学报, 2025, 53(03): 1000-1013. DOI：10.12263/DZXB.20240770

HAN Liang, LIU Yuan, PU Xiu-juan, et al. Method on Alzheimer’s Disease Classification Utilizing Graph Neural Network with Nearest Neighborhood Aggregation[J]. Acta Electronica Sinica, 2025, 53(03): 1000-1013. DOI：10.12263/DZXB.20240770

摘要

阿尔茨海默病（Alzheimer’s Disease，AD）是一种慢性神经系统退行性疾病，其准确分类有助于实现AD的早期诊断，从而及时采取针对性的治疗和干预措施.本文提出了一种最近邻域聚合图神经网络（Graph neural network with nearest Neighborhood AgGrEgation，GraphNAGE）的AD分类新方法.首先进行图数据建模，将AD数据样本表示为图数据.采用基于互信息（Mutual Information，MI）的特征选择方法，从样本的114维大脑皮层与皮层下感兴趣区域（Cerebral Cortex and Subcortical Regions Of Interest，CCS-ROI）的体积特征中选取重要性高的体积特征，并将其用于节点建模.提出基于相似性度量的关系建模方法，利用重要性高的体积特征、遗传基因、人口统计信息和认知评分对样本之间的关系进行建模.进而构建GraphNAGE，针对每个节点，基于与该节点相关的边的权重进行最近邻域采样，然后使用均值聚合方法对采样得到的邻居节点和中心节点的数据进行聚合，最后通过一个全连接层和一个Softmax层实现AD分类.在TADPOLE（The Alzheimer’s Disease Prediction Of Longitudinal Evolution）数据集上进行实验，结果表明：本文提出的AD分类方法的准确率（ACCuracy，ACC）为98.20%，

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3.21733332

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分数为97.34%，曲线下面积（Area Under Curve，AUC）为97.80%.实验结果表明：本文提出的AD分类方法充分利用了AD数据样本之间的相关性，其性能优于传统的基于机器学习、深度学习和图神经网络（Graph Neural Network，GNN）的AD分类方法.

Abstract

Alzheimer’s disease (AD) is a chronic neurodegenerative disease

and its accurate classification is advantage to achieve early diagnosis of AD so as to take timely treatment and intervention. In this paper

a novel method on AD Classification utilizing graph neural network with nearest neighborhood aggregation (GraphNAGE) is proposed. Firstly

the graph data modeling is performed to represent AD samples as graph data. By feature selection method based on mutual information (MI)

the high-importance volume features are selected from the 114 dimensional volume features of cerebral cortex and subcortical regions of interest (CCS-ROI) in the sample

and used for node modeling. Meanwhile

a relationship modeling method based on similarity measurement

modeling the relationships between samples using high importance volume features

genetic genes

demographic information

and cognitive scores

is presented. Subsequently

the graph neural network with nearest neighborhood aggregation is constructed. For each node in the graph data

the nearest neighbor sampling is performed based on the weights of edge related to it. Then

the sampled data of neighboring nodes and central node are aggregated using the mean aggregation method. At last

a full-connected layer and a softmax layer are used to implement AD classification. The proposed AD classification method is eva

luated on the Alzheimer’s disease prediction of longitudinal evolution (TADPOLE) dataset. The accuracy (ACC)

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=83543399&type=

3.21733332

https://html.publish.founderss.cn/rc-pub/api/common/picture?pictureId=83543390&type=

3.04800010

score and area under curve (AUC) of proposed AD classification method are 98.20%

97.34% and 97.80%

respectively. The experimental results show that the proposed AD classification method fully exploits the correlation between AD samples. Its performance is superior to conventional AD classification methods based on machine learning

deep learning and graph neural network.

关键词

Keywords

references

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