Multi-modal graph neural network for early diagnosis of Alzheimer's disease from sMRI and PET scans

计算机科学 卷积神经网络 模态(人机交互) 神经影像学 人工智能 深度学习 图形 情态动词 正电子发射断层摄影术 人工神经网络 机器学习 模式识别(心理学) 医学 放射科 理论计算机科学 精神科 化学 高分子化学
作者
Yanteng Zhang,Xiaohai He,Yi Hao Chan,Qizhi Teng,Jagath C. Rajapakse
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:164: 107328-107328 被引量:24
标识
DOI:10.1016/j.compbiomed.2023.107328
摘要

In recent years, deep learning models have been applied to neuroimaging data for early diagnosis of Alzheimer's disease (AD). Structural magnetic resonance imaging (sMRI) and positron emission tomography (PET) images provide structural and functional information about the brain, respectively. Combining these features leads to improved performance than using a single modality alone in building predictive models for AD diagnosis. However, current multi-modal approaches in deep learning, based on sMRI and PET, are mostly limited to convolutional neural networks, which do not facilitate integration of both image and phenotypic information of subjects. We propose to use graph neural networks (GNN) that are designed to deal with problems in non-Euclidean domains. In this study, we demonstrate how brain networks are created from sMRI or PET images and can be used in a population graph framework that combines phenotypic information with imaging features of the brain networks. Then, we present a multi-modal GNN framework where each modality has its own branch of GNN and a technique that combines the multi-modal data at both the level of node vectors and adjacency matrices. Finally, we perform late fusion to combine the preliminary decisions made in each branch and produce a final prediction. As multi-modality data becomes available, multi-source and multi-modal is the trend of AD diagnosis. We conducted explorative experiments based on multi-modal imaging data combined with non-imaging phenotypic information for AD diagnosis and analyzed the impact of phenotypic information on diagnostic performance. Results from experiments demonstrated that our proposed multi-modal approach improves performance for AD diagnosis. Our study also provides technical reference and support the need for multivariate multi-modal diagnosis methods.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
刚刚
顺利完成签到,获得积分10
2秒前
小蘑菇应助矮小的柠檬采纳,获得10
2秒前
Jahen完成签到,获得积分10
3秒前
今年一定离开癫胡完成签到,获得积分10
3秒前
4秒前
研友_VZG7GZ应助11采纳,获得10
4秒前
苹果萧完成签到 ,获得积分10
5秒前
Yxy2021发布了新的文献求助10
5秒前
衡阳完成签到,获得积分10
5秒前
乐闻发布了新的文献求助10
7秒前
alna完成签到,获得积分10
8秒前
微微发布了新的文献求助10
8秒前
阿江shk完成签到,获得积分10
8秒前
wddsf完成签到,获得积分10
8秒前
leadsyew完成签到,获得积分10
8秒前
长安完成签到,获得积分10
8秒前
hyekyo完成签到,获得积分10
9秒前
ren完成签到,获得积分10
9秒前
析渊完成签到,获得积分10
10秒前
11完成签到,获得积分10
11秒前
水濑心源发布了新的文献求助10
11秒前
Zero140发布了新的文献求助10
11秒前
W1ll完成签到,获得积分10
12秒前
12秒前
13秒前
13秒前
Owen应助俭朴从安采纳,获得10
13秒前
13秒前
完美世界应助fireking_sid采纳,获得10
13秒前
啊啊啊完成签到,获得积分10
13秒前
24K纯帅完成签到,获得积分0
13秒前
14秒前
大个应助炸药采纳,获得10
14秒前
干净的问寒完成签到,获得积分20
14秒前
小月完成签到,获得积分10
15秒前
15秒前
一盒火柴完成签到,获得积分10
15秒前
哈h完成签到,获得积分20
15秒前
高分求助中
Nickel, Cobalt and Palladium Catalysed Infarction with Ventricular following rich structural diversity 1000
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3968608
求助须知:如何正确求助?哪些是违规求助? 3513486
关于积分的说明 11168243
捐赠科研通 3248926
什么是DOI,文献DOI怎么找? 1794540
邀请新用户注册赠送积分活动 875188
科研通“疑难数据库(出版商)”最低求助积分说明 804676