GraSTI-ACL: Graph spatial–temporal infomax with adversarial contrastive learning for brain disorders diagnosis based on resting-state fMRI

IF 11.8 1区 医学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Biao He , Erni Ji , Xiaofen Zong , Zhen Liang , Gan Huang , Li Zhang
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Abstract

Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in research on brain disorders due to its informative spatial and temporal resolution, and it shows growing potential as a noninvasive tool for assisting clinical diagnosis. Among various methods based on rs-fMRI, graph neural networks have received significant attention because of their inherent structural similarity to functional connectivity networks (FCNs) of the brain. However, constructing FCNs that effectively capture both spatial and temporal information from rs-fMRI remains challenging, as traditional methods often rely on static, fully connected graphs that risk redundancy and neglect dynamic patterns. Based on the information bottleneck principle, this paper proposes a graph augmentation strategy named Graph Spatial–Temporal Infomax (GraSTI) to adaptively preserve both global spatial brain-wide FCNs and local temporal dynamics. We integrate GraSTI with theoretical explanations and design a practical implementation to adapt to our graph augmentation strategy and enhance feature capture capability. Furthermore, GraSTI is incorporated into an adversarial contrastive learning framework to achieve a mutual information equilibrium between graph representation effectiveness and robustness for downstream brain disorders diagnosis tasks. The proposed method is evaluated on datasets from three different brain disorders: Alzheimer’s disease (AD), major depressive disorder (MDD), and bipolar disorder (BD). Extensive experiments demonstrate that the proposed GraSTI-ACL achieves diagnostic accuracy gains of 0.13% to 23.56% for AD, 1.23% to 13.81% for MDD, and 2.53% to 24.53% for BD diagnosis over existing methods. Meanwhile, our method demonstrates strong interpretability in identifying relevant brain regions and connectivities for different brain disorders.
基于静息状态fMRI的敌对对比学习的图时空信息集诊断脑部疾病
静息状态功能磁共振成像(rs-fMRI)因其具有信息丰富的时空分辨率而被广泛应用于脑疾病的研究,作为辅助临床诊断的无创工具显示出越来越大的潜力。在基于rs-fMRI的各种方法中,图神经网络因其与大脑功能连接网络(fnc)固有的结构相似性而受到广泛关注。然而,构建有效捕获rs-fMRI空间和时间信息的fns仍然具有挑战性,因为传统方法通常依赖于静态的、完全连接的图,有冗余的风险,并且忽略了动态模式。基于信息瓶颈原理,提出了一种自适应保存全局空间全脑fns和局部时间动态的图增强策略——图时空信息最大化(GraSTI)。我们将GraSTI与理论解释相结合,并设计了一个实际实现,以适应我们的图形增强策略并增强特征捕获能力。此外,GraSTI被整合到一个对抗性对比学习框架中,在下游脑疾病诊断任务中实现图表示有效性和鲁棒性之间的相互信息平衡。该方法在来自三种不同脑部疾病的数据集上进行了评估:阿尔茨海默病(AD)、重度抑郁症(MDD)和双相情感障碍(BD)。大量实验表明,与现有方法相比,本文提出的GraSTI-ACL对AD的诊断准确率提高了0.13% ~ 23.56%,对MDD的诊断准确率提高了1.23% ~ 13.81%,对BD的诊断准确率提高了2.53% ~ 24.53%。同时,我们的方法在识别不同大脑疾病的相关大脑区域和连接方面具有很强的可解释性。
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来源期刊
Medical image analysis
Medical image analysis 工程技术-工程:生物医学
CiteScore
22.10
自引率
6.40%
发文量
309
审稿时长
6.6 months
期刊介绍: Medical Image Analysis serves as a platform for sharing new research findings in the realm of medical and biological image analysis, with a focus on applications of computer vision, virtual reality, and robotics to biomedical imaging challenges. The journal prioritizes the publication of high-quality, original papers contributing to the fundamental science of processing, analyzing, and utilizing medical and biological images. It welcomes approaches utilizing biomedical image datasets across all spatial scales, from molecular/cellular imaging to tissue/organ imaging.
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