Dual Multi-Atlas Representation Alignment for Brain Disorder Diagnosis Using Morphological Connectome

In structural magnetic resonance imaging (MRI), morphological connectome plays an important role in capturing coordinated patterns of region-wise morphological features for brain disorder diagnosis. However, significant challenges remain in aggregating diverse representations from multiple brain atlases, stemming from variations in the definition of regions of interest. To effectively integrate complementary information from multiple atlases while mitigating possible biases, we propose a novel dual multi-atlas representation alignment approach (DMAA) for brain disorder diagnosis. Specifically, we first minimize the maximum mean discrepancy of multi-atlas representations to align them into a unified distribution, reducing inter-atlas variability and enhancing effective feature fusion. Then, to further manage the anatomical variability, we apply optimal transport to capture and harmonize region-wise differences, preserving plausible relationships across atlases. Extensive experiments on ADNI, PPMI, ADHD200, and SchizConnect datasets demonstrate the effectiveness of our proposed DMAA on brain disorder diagnosis using multi-atlas morphological connectome.