Multiscale Spectral Augmentation for Graph Contrastive Learning for fMRI analysis to diagnose psychiatric disease

Abstract

Due to the complexity and incompleteness of cognitive tests, as well as subjective biases in humans, using functional magnetic resonance imaging (fMRI) data for accurate diagnosis of psychiatric disease is a challenging task. In addition, existing contrastive methods are also limited by graph augmentation and negative sampling methods in population-based classification. In order to improve the representation learning and classification of fMRI under limited labeled data, we propose a new contrastive self-supervised learning method based on spectral augmentation, namely Multiscale Spectral Augmentation for Graph Contrastive Learning (MSA-GCL) for fMRI Analysis. Concretely, we adopt a two-stage spectral augmentation method by initialization and fine-tuning to mine features of multimodal data. This approach effectively addresses the limitations faced by models that solely rely on coarse-grained spectral augmentation, which leads to weak robustness and limited generalization on medical datasets. Besides, we add a semantic module to fully utilize non-imaging data. Our method is tested on ABIDE I and ADHD-200 datasets, demonstrating superior performance in diagnosis of autism spectrum disorders(ASD) and attention deficit and hyperactivity disorder(ADHD).