HubRL: A Reinforcement Learning Framework for Brain Hub Identification via Dynamic-Static Network Fusion

Identifying the brain hubs that are crucial for integrating information and distribution is key to understanding how the brain works. In recent years, although various hub identification methods have been proposed in the field of brain imaging, they typically rely on static network representations and analyze using univariate node metrics, thereby neglecting the hub nodes that play a critical role in dynamic global information integration. Additionally, there is an urgent need for an efficient learning method to handle complex brain networks. In this paper, we propose a new reinforcement learning framework, named HubRL, to identify hub nodes that play a central role in coordinating information flow and static topological structures. The agent identifies the most critical brain network nodes by combining simulated information propagation to assess dynamic influence with graph theory metrics to evaluate static topological importance. The experimental results demonstrate that we have successfully identified 37 Task-General hubs in the brain network. Topologically, these hubs exhibit a core advantage over nonhub nodes, with a distribution ratio of approximately 2: 1 in the cerebral cortex gyri and sulci. They also feature significantly longer structural connection fiber bundles and overlap with the regions of the brain with the strongest functional connectivity by up to 80%. This work frames hub identification as a data-driven sequential decision-making problem without relying on heuristic rules, representing a powerful new paradigm for exploring brain hubs and understanding the working mechanism of the brain.