Multi-Connectivity Representation Learning Network for Major Depressive Disorder Diagnosis

计算机科学 功能磁共振成像 代表(政治) 人工智能 模态(人机交互) 特征学习 机器学习 模式识别(心理学) 理论计算机科学 神经科学 政治学 政治 法学 生物
作者
Youyong Kong,Wenhan Wang,Xiaoyun Liu,Shuwen Gao,Zhenghua Hou,Chunming Xie,Zhijun Zhang,Yonggui Yuan
出处
期刊:IEEE Transactions on Medical Imaging [Institute of Electrical and Electronics Engineers]
卷期号:42 (10): 3012-3024 被引量:7
标识
DOI:10.1109/tmi.2023.3274351
摘要

The pathophysiology of major depressive disorder (MDD) has been demonstrated to be highly associated with the dysfunctional integration of brain activity. Existing studies only fuse multi-connectivity information in a one-shot approach and ignore the temporal property of functional connectivity. A desired model should utilize the rich information in multiple connectivities to help improve the performance. In this study, we develop a multi-connectivity representation learning framework to integrate multi-connectivity topological representation from structural connectivity, functional connectivity and dynamic functional connectivities for automatic diagnosis of MDD. Briefly, structural graph, static functional graph and dynamic functional graphs are first computed from the diffusion magnetic resonance imaging (dMRI) and resting state functional magnetic resonance imaging (rsfMRI). Secondly, a novel Multi-Connectivity Representation Learning Network (MCRLN) approach is developed to integrate the multiple graphs with modules of structural-functional fusion and static-dynamic fusion. We innovatively design a Structural-Functional Fusion (SFF) module, which decouples graph convolution to capture modality-specific features and modality-shared features separately for an accurate brain region representation. To further integrate the static graphs and dynamic functional graphs, a novel Static-Dynamic Fusion (SDF) module is developed to pass the important connections from static graphs to dynamic graphs via attention values. Finally, the performance of the proposed approach is comprehensively examined with large cohorts of clinical data, which demonstrates its effectiveness in classifying MDD patients. The sound performance suggests the potential of the MCRLN approach for the clinical use in diagnosis. The code is available at https://github.com/LIST-KONG/MultiConnectivity-master.
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
韭菜盒子发布了新的文献求助10
1秒前
3秒前
刘刘完成签到,获得积分10
3秒前
等待的幼晴完成签到,获得积分10
3秒前
水本无忧87完成签到,获得积分10
3秒前
4秒前
所所应助韭菜盒子采纳,获得10
6秒前
xieyuanxing完成签到,获得积分10
7秒前
9秒前
9秒前
白鹭立雪完成签到,获得积分10
10秒前
书生完成签到,获得积分10
11秒前
刘佳敏完成签到 ,获得积分10
11秒前
mhy完成签到 ,获得积分10
11秒前
12秒前
Chiuchiu完成签到,获得积分10
14秒前
3366ll完成签到 ,获得积分10
14秒前
铎铎铎完成签到 ,获得积分10
14秒前
迷人的天抒应助清修采纳,获得10
15秒前
划水完成签到,获得积分10
15秒前
英吉利25发布了新的文献求助10
16秒前
11完成签到 ,获得积分10
16秒前
静默向上发布了新的文献求助10
18秒前
韭菜盒子完成签到,获得积分20
18秒前
18秒前
831143完成签到 ,获得积分0
18秒前
wwww完成签到 ,获得积分10
21秒前
21秒前
22秒前
my完成签到 ,获得积分10
24秒前
ll完成签到 ,获得积分10
25秒前
大豆终结者完成签到,获得积分10
25秒前
djbj2022发布了新的文献求助10
26秒前
忽忽完成签到,获得积分10
26秒前
28秒前
30秒前
32秒前
wanci应助韭黄采纳,获得10
32秒前
Silieze完成签到,获得积分10
32秒前
郑zhenglanyou完成签到,获得积分10
33秒前
高分求助中
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
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 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 3968578
求助须知:如何正确求助?哪些是违规求助? 3513393
关于积分的说明 11167478
捐赠科研通 3248836
什么是DOI,文献DOI怎么找? 1794499
邀请新用户注册赠送积分活动 875131
科研通“疑难数据库(出版商)”最低求助积分说明 804664