Self-supervised graph contrastive learning with diffusion augmentation for functional MRI analysis and brain disorder detection.

ABSTRACT: 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 co

SUBMITTER: Wang X 

PROVIDER: S-EPMC11875923 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature