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Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classification

Wei Liang, Lifang He

TL;DR

A unified cross-view contrastive framework for joint imaging-ROI representation learning that provides principled evidence that explicitly integrating global volumetric and ROI-level representations is a promising direction for neuroimaging-based brain disorder classification.

Abstract

Brain imaging classification is commonly approached from two perspectives: modeling the full image volume to capture global anatomical context, or constructing ROI-based graphs to encode localized and topological interactions. Although both representations have demonstrated independent efficacy, their relative contributions and potential complementarity remain insufficiently understood. Existing fusion approaches are typically task-specific and do not enable controlled evaluation of each representation under consistent training settings. To address this gap, we propose a unified cross-view contrastive framework for joint imaging-ROI representation learning. Our method learns subject-level global (imaging) and local (ROI-graph) embeddings and aligns them in a shared latent space using a bidirectional contrastive objective, encouraging representations from the same subject to converge while separating those from different subjects. This alignment produces comparable embeddings suitable for downstream fusion and enables systematic evaluation of imaging-only, ROI-only, and joint configurations within a unified training protocol. Extensive experiments on the ADHD-200 and ABIDE datasets demonstrate that joint learning consistently improves classification performance over either branch alone across multiple backbone choices. Moreover, interpretability analyses reveal that imaging-based and ROI-based branches emphasize distinct yet complementary discriminative patterns, explaining the observed performance gains. These findings provide principled evidence that explicitly integrating global volumetric and ROI-level representations is a promising direction for neuroimaging-based brain disorder classification. The source code is available at https://anonymous.4open.science/r/imaging-roi-contrastive-152C/.

Joint Imaging-ROI Representation Learning via Cross-View Contrastive Alignment for Brain Disorder Classification

TL;DR

A unified cross-view contrastive framework for joint imaging-ROI representation learning that provides principled evidence that explicitly integrating global volumetric and ROI-level representations is a promising direction for neuroimaging-based brain disorder classification.

Abstract

Brain imaging classification is commonly approached from two perspectives: modeling the full image volume to capture global anatomical context, or constructing ROI-based graphs to encode localized and topological interactions. Although both representations have demonstrated independent efficacy, their relative contributions and potential complementarity remain insufficiently understood. Existing fusion approaches are typically task-specific and do not enable controlled evaluation of each representation under consistent training settings. To address this gap, we propose a unified cross-view contrastive framework for joint imaging-ROI representation learning. Our method learns subject-level global (imaging) and local (ROI-graph) embeddings and aligns them in a shared latent space using a bidirectional contrastive objective, encouraging representations from the same subject to converge while separating those from different subjects. This alignment produces comparable embeddings suitable for downstream fusion and enables systematic evaluation of imaging-only, ROI-only, and joint configurations within a unified training protocol. Extensive experiments on the ADHD-200 and ABIDE datasets demonstrate that joint learning consistently improves classification performance over either branch alone across multiple backbone choices. Moreover, interpretability analyses reveal that imaging-based and ROI-based branches emphasize distinct yet complementary discriminative patterns, explaining the observed performance gains. These findings provide principled evidence that explicitly integrating global volumetric and ROI-level representations is a promising direction for neuroimaging-based brain disorder classification. The source code is available at https://anonymous.4open.science/r/imaging-roi-contrastive-152C/.
Paper Structure (7 sections, 7 equations, 3 figures, 2 tables)

This paper contains 7 sections, 7 equations, 3 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Method
  3. Imaging and ROI-Graph Representation Learning
  4. Cross-View Contrastive Alignment
  5. Fusion and Classification Objective
  6. Experiments and Results
  7. Conclusion

Figures (3)

  • Figure 1: Overview of the proposed joint imaging--ROI representation learning framework. (A) Joint representation learning via cross-view contrastive alignment. Subject-level global imaging and local ROI-graph embeddings are extracted by modular encoders and aligned in a shared latent space to enable downstream fusion and classification. (B) Post-hoc contribution analysis. Branch-specific attribution maps quantify complementary discriminative patterns captured by the imaging and ROI-graph representations.
  • Figure 2: Missing-view robustness on ADHD-200. Shaded regions denote $\pm1$ std over five-fold cross-validation.
  • Figure 3: Contribution maps for ADHD and TDC. The AAL parcellation (first column) provides anatomical reference; remaining columns show normalized contribution scores (0--1) for Joint Learning, 3DSC-TF Only, and NeuroGraph Only.