Resting-state functional magnetic resonance imaging (rs-fMRI) provides a non-invasive imaging technique to study patterns of brain activity, and is increasingly used to facilitate automated brain disorder analysis. Existing fMRI-based learning methods often rely on labeled data to construct learning models, while the data annotation process typically requires significant time and resource investment. Graph contrastive learning offers a promising solution to address the small labeled data issue, by augmenting fMRI time series for self-supervised learning. However, data augmentation strategies employed in these approaches may damage the original blood-oxygen-level-dependent (BOLD) signals, thus hindering subsequent fMRI feature extraction. In this paper, we propose a self-supervised graph contrastive learning framework with diffusion augmentation (GCDA) for functional MRI analysis. The GCDA consists of a pretext model and a task-specific model. In the pretext model, we first augment each brain functional connectivity network derived from fMRI through a graph diffusion augmentation (GDA) module, and then use two graph isomorphism networks with shared parameters to extract features in a self-supervised contrastive learning manner. The pretext model can be optimized without the need for labeled training data, while the GDA focuses on perturbing graph edges and nodes, thus preserving the integrity of original BOLD signals. The task-specific model involves fine-tuning the trained pretext model to adapt to downstream tasks. Experimental results on two rs-fMRI cohorts with a total of 1230 subjects demonstrate the effectiveness of our method compared with several state-of-the-arts.

中文翻译：

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使用弥散增强进行自我监督图对比学习，用于功能 MRI 分析和脑部疾病检测


静息态功能磁共振成像 （rs-fMRI） 提供了一种非侵入性成像技术来研究大脑活动模式，并越来越多地用于促进自动脑部疾病分析。现有的基于 fMRI 的学习方法通常依赖于标记数据来构建学习模型，而数据注释过程通常需要大量的时间和资源投入。图对比学习通过增强 fMRI 时间序列进行自我监督学习，为解决小标记数据问题提供了一种很有前途的解决方案。然而，这些方法中采用的数据增强策略可能会破坏原始的血氧水平依赖性 （BOLD） 信号，从而阻碍随后的 fMRI 特征提取。在本文中，我们提出了一个带有弥散增强 （GCDA） 的自监督图对比学习框架，用于功能 MRI 分析。GCDA 由一个前置模型和一个特定于任务的模型组成。在前置模型中，我们首先通过图扩散增强 （GDA） 模块增强从 fMRI 得出的每个大脑功能连接网络，然后使用两个具有共享参数的图同构网络以自监督对比学习方式提取特征。无需标记训练数据即可优化 pretext 模型，而 GDA 则专注于扰动图边和节点，从而保持原始 BOLD 信号的完整性。特定于任务的模型涉及微调经过训练的 pretext 模型以适应下游任务。两个 rs-fMRI 队列（共 1230 名受试者）的实验结果表明，与几种最先进的方法相比，我们的方法的有效性。