Learning the Hierarchical Organization in Brain Network for Brain Disorder Diagnosis
Brain network analysis based on functional Magnetic Resonance Imaging (fMRI) is pivotal for diagnosing brain disorders. Existing approaches typically rely on predefined functional sub-networks to construct sub-network associations. However, we identified many cross-network interaction patterns with high Pearson correlations that this strict, prior-based organization fails to capture. To overcome this limitation, we propose the Brain Hierarchical Organization Learning (BrainHO) to learn inherently hierarchical brain network dependencies based on their intrinsic features rather than predefined sub-network labels. Specifically, we design a hierarchical attention mechanism that allows the mod...
| Authors | Jingfeng Tang, Peng Cao, Guangqi Wen et al. |
|---|---|
| Primary category | cs.LG |
| Published | 10 March 2026 |
| Last updated at source | 10 March 2026 |
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