Hi-Brain: Brain-Inspired Bidirectional Hierarchical Learning for Dynamic Brain Connectivity Analysis

Modeling the brain’s dynamic information-processing mechanisms is a core challenge in neuroscience and artificial intelligence. Existing methods typically first construct dynamic functional brain connectivity (DFBC), and further use topological modeling or prior constraints to characterize such dynamic information transmission. However, they fail to comprehensively model the hierarchical organization of the brain, leading to learned features with limited biological plausibility. To address this issue, we propose a brain-inspired bidirectional hierarchical network (Hi-Brain) for DFBC analysis. Hi-Brain effectively simulates bottom-up functional aggregation and top-down functional regulation in the brain. Specifically, we first segment fMRI signals into multiple subsequences to model the brain's dynamic patterns. For each subsequence, we design a neuro-inspired node activation learner to capture the adaptive activation of neural nodes under different states. Building on this, we further introduce a high-order subnetwork learner to describe the functional differentiation and global collaboration of the sub-networks, enabling bottom-up high-order functional aggregation. Finally, the aggregated information is used to guide feature optimization in lower-level neural organizations, thereby simulating top-down functional regulation in the brain. Experiments on the ADNI and Parkinson’s disease datasets demonstrate that Hi-Brain not only achieves superior predictive performance, but also effectively explains the neural mechanisms underlying its predictions. More importantly, Hi-Brain provides a new perspective for developing brain-inspired machine learning models.