Unsupervised contrastive graph learning for resting-state functional MRI analysis and brain disorder detection.

ABSTRACT: Resting-state functional magnetic resonance imaging (rs-fMRI) helps characterize regional interactions that occur in the human brain at a resting state. Existing research often attempts to explore fMRI biomarkers that best predict brain disease progression using machine/deep learning techniques. Previous fMRI studies have shown that learning-based methods usually require a large amount of labeled training data, limiting their utility in clinical practice where annotating data is often time-consuming and labor-intensive. To this end, we propose an unsupervised contrastive graph learning (UCGL) framework for fMRI-based brain disease analysis, in which a pretext model is designed to generate informative fMRI representations using unlabeled training data, followed by model fine-tuning to perfo

SUBMITTER: Wang X 

PROVIDER: S-EPMC10619386 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature