BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis

TL;DR

BrainSCL introduces a subtype-guided contrastive learning framework that models patient heterogeneity as latent subtypes, using multi-view representations and prototype graphs to improve brain disorder diagnosis accuracy.

Contribution

The paper proposes a novel contrastive learning approach that incorporates latent subtypes as structural priors, enhancing discriminative representation learning for brain disorder diagnosis.

Findings

Outperforms state-of-the-art methods on MDD, BD, and ASD datasets.

Effectively models patient heterogeneity through latent subtypes.

Demonstrates the utility of subtype prototype graphs in guiding contrastive learning.

Abstract

Mental disorder populations exhibit pronounced heterogeneity -- that is, the significant differences between samples -- poses a significant challenge to the definition of positive pairs in contrastive learning. To address this, we propose a subtype-guided contrastive learning framework that models patient heterogeneity as latent subtypes and incorporates them as structural priors to guide discriminative representation learning. Specifically, we construct multi-view representations by combining patients' clinical text with graph structure adaptively learned from BOLD signals, to uncover latent subtypes via unsupervised spectral clustering. A dual-level attention mechanism is proposed to construct prototypes for capturing stable subtype-specific connectivity patterns. We further propose a subtype-guided contrastive learning strategy that pulls samples toward their subtype prototype graph, reinforcing intra-subtype consistency for providing effective supervisory signals to improve model performance. We evaluate our method on Major Depressive Disorder (MDD), Bipolar Disorder (BD), and Autism Spectrum Disorders (ASD). Experimental results confirm the effectiveness of subtype prototype graphs in guiding contrastive learning and demonstrate that the proposed approach outperforms state-of-the-art approaches. Our code is available at https://anonymous.4open.science/r/BrainSCL-06D7.