BrainSCL: Subtype-Guided Contrastive Learning for Brain Disorder Diagnosis
arXiv:2603.19295v1 Announce Type: new 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
arXiv:2603.19295v1 Announce Type: new 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.
Executive Summary
This article proposes BrainSCL, a subtype-guided contrastive learning framework for brain disorder diagnosis. It addresses the challenge of heterogeneity in mental disorder populations by modeling patient heterogeneity as latent subtypes and incorporating them as structural priors to guide discriminative representation learning. The framework combines clinical text with graph structure adaptively learned from BOLD signals to uncover latent subtypes via unsupervised spectral clustering. It also proposes a dual-level attention mechanism and a subtype-guided contrastive learning strategy that pulls samples toward their subtype prototype graph, reinforcing intra-subtype consistency. The proposed approach is evaluated on Major Depressive Disorder, Bipolar Disorder, and Autism Spectrum Disorders, demonstrating its effectiveness in outperforming state-of-the-art approaches. The code is available online, enabling reproducibility and further research.
Key Points
- ▸ Subtype-guided contrastive learning framework for brain disorder diagnosis
- ▸ Addressing heterogeneity in mental disorder populations through latent subtypes
- ▸ Combination of clinical text and graph structure learned from BOLD signals
Merits
Strength in Addressing Heterogeneity
The proposed framework effectively models patient heterogeneity as latent subtypes, addressing a significant challenge in contrastive learning for brain disorder diagnosis.
Improved Representation Learning
The incorporation of structural priors guides discriminative representation learning, leading to improved accuracy in brain disorder diagnosis.
Dual-Level Attention Mechanism
The proposed dual-level attention mechanism enables the capture of stable subtype-specific connectivity patterns, enhancing the framework's effectiveness.
Demerits
Limitation in Generalizability
The proposed approach is evaluated on a limited set of disorders (Major Depressive Disorder, Bipolar Disorder, and Autism Spectrum Disorders), and its generalizability to other disorders remains uncertain.
Dependence on High-Quality Data
The framework's performance may be sensitive to the quality of clinical text and BOLD signal data, which can be challenging to obtain and preprocess.
Expert Commentary
The article presents a well-motivated and technically sound approach to addressing the challenge of heterogeneity in mental disorder populations. The proposed framework demonstrates promising results in brain disorder diagnosis, and its potential to improve patient outcomes is significant. However, the approach's generalizability to other disorders and dependence on high-quality data are limitations that must be addressed in future research. The article's contributions to the field of deep learning in medical imaging and personalized medicine are substantial, and its implications for policy and practice are far-reaching.
Recommendations
- ✓ Further evaluation of the proposed framework on a broader range of disorders is necessary to establish its generalizability and potential for widespread adoption.
- ✓ Investigation into the framework's performance on diverse patient populations, including those with different demographic and socioeconomic characteristics, is essential to ensure its fairness and equity.
Sources
Original: arXiv - cs.LG
