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

In structural magnetic resonance imaging (MRI), morpholog-ical connectome plays an important role in capturing coordi-nated patterns of region-wise morphological features for brain disorder diagnosis. However, significant challenges remain in aggregating diverse representations from multiple brain at-lases, 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 at-lases. 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.