Fusing multi-scale fMRI features using a brain-inspired multi-channel graph neural network for major depressive disorder diagnosis

计算机科学 重性抑郁障碍 人工智能 图形 机器学习 认知心理学 心理学 认知 精神科 理论计算机科学
作者
Shuai Liu,Renzhou Gui
出处
期刊:Biomedical Signal Processing and Control [Elsevier]
卷期号:90: 105837-105837 被引量:2
标识
DOI:10.1016/j.bspc.2023.105837
摘要

Depression stands as one of the most pernicious mental disorders in contemporary society, characterized by a highly intricate pathological mechanism. Specifically, individuals suffering from Major Depressive Disorder (MDD) exhibit heightened vulnerability to suicidal tendencies. Currently, healthcare practitioners often encounter challenges related to the misdiagnosis and underdiagnosis of depression during clinical assessments. Consequently, it is of paramount importance to develop highly accurate auxiliary diagnostic tools for depression. Unfortunately, traditional machine learning and deep learning methodologies frequently neglect the integration of multi-source data and disregard the intricate topological structure and high-order attributes of brain networks. In this study, a multi-scale feature fusion classification framework is proposed to distinguish between MDD patients and healthy controls. Within the proposed model, a novel method, the Cross-Level High-Order Interaction (CLHOI), is introduced and implemented on a low-order functional connectivity (LOFC) matrix to derive two distinct high-order functional connectivity (HOFC) matrices. Subsequently, a Multi-Channel Fusion Graph Convolutional Network (MFGCN) is trained by integrating high-order and low-order brain graph features along with phenotypic information. The results of 10-fold cross-validation experiments conducted on the publicly available REST-meta-MDD dataset indicate that the fusion of multi-scale features improves the average accuracy by approximately 3%, resulting in an accuracy rate of 77.6%. Simultaneously, the hypothesis asserting the existence of intricate information interactions at various levels within brain connectivity networks is validated. Moreover, our model exhibits strong explanatory capabilities, effectively identifying brain regions closely associated with MDD, including the Precentral gyrus, Superior frontal gyrus, Cuneus, Lingual gyrus, and Fusiform gyrus. In comparison to numerous advanced studies within the same domain, our approach has produced competitive results. Furthermore, our proposed method can be readily extended to facilitate the diagnosis of various neurological diseases.
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
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
林上草应助xzn1123采纳,获得10
1秒前
qwt_hello发布了新的文献求助10
2秒前
3秒前
科研虎完成签到,获得积分10
3秒前
大眼的平松完成签到,获得积分10
3秒前
丶呆久自然萌完成签到,获得积分10
3秒前
3秒前
4秒前
淡淡的夜山完成签到,获得积分10
4秒前
SYLH应助阿勒泰采纳,获得10
5秒前
5秒前
5秒前
菊菊关注了科研通微信公众号
6秒前
6秒前
6秒前
水星MERCURY应助雨夜星空采纳,获得10
7秒前
7秒前
7秒前
8秒前
九九完成签到,获得积分10
8秒前
dwl完成签到 ,获得积分10
8秒前
懵懂的尔风完成签到 ,获得积分10
8秒前
8秒前
456完成签到,获得积分10
8秒前
科研通AI5应助以恒之心采纳,获得10
9秒前
易哒哒发布了新的文献求助10
10秒前
10秒前
11秒前
微笑完成签到,获得积分10
11秒前
火星上的映安完成签到 ,获得积分10
11秒前
Microgan完成签到,获得积分10
11秒前
进击的小胳膊完成签到,获得积分10
12秒前
12秒前
科研通AI5应助徐徐采纳,获得80
12秒前
12秒前
Orange应助Joshua采纳,获得10
13秒前
14秒前
14秒前
15秒前
蒋时晏应助陶醉薯片采纳,获得30
15秒前
高分求助中
Continuum Thermodynamics and Material Modelling 3000
Production Logging: Theoretical and Interpretive Elements 2700
Social media impact on athlete mental health: #RealityCheck 1020
Ensartinib (Ensacove) for Non-Small Cell Lung Cancer 1000
Unseen Mendieta: The Unpublished Works of Ana Mendieta 1000
Bacterial collagenases and their clinical applications 800
El viaje de una vida: Memorias de María Lecea 800
热门求助领域 (近24小时)
化学 材料科学 生物 医学 工程类 有机化学 生物化学 物理 纳米技术 计算机科学 内科学 化学工程 复合材料 基因 遗传学 物理化学 催化作用 量子力学 光电子学 冶金
热门帖子
关注 科研通微信公众号,转发送积分 3527742
求助须知:如何正确求助?哪些是违规求助? 3107867
关于积分的说明 9286956
捐赠科研通 2805612
什么是DOI,文献DOI怎么找? 1540026
邀请新用户注册赠送积分活动 716884
科研通“疑难数据库(出版商)”最低求助积分说明 709762