BrainTAL: A Target-Adaptive Brain Token Learner for Cross-Site Brain Network Analysis

Multi-site brain network analysis provides an important tool for characterizing functional connectivity patterns across different medical centers and has attracted increasing attention in brain disease diagnosis. However, existing methods often struggle to adapt to target-site distribution shifts under source-free and label-free settings. Moreover, they usually overlook task-specific semantic differences within the same brain network, which limits their predictive ability. To address these limitations, we propose BrainTAL, a target-adaptive brain token learning framework for cross-site brain network analysis. Specifically, we first construct compact brain network instructions, where salient functional connections are introduced as neurobiological priors for language-guided modeling. Second, we design task-conditioned brain tokens, enabling the model to adaptively capture task-relevant functional connectivity patterns from the same brain network across different neuroimaging tasks. Finally, we propose a brain token adaptive calibration strategy, which performs lightweight adaptation of the source-trained model using unlabeled target-site data, thereby alleviating cross-site distribution shifts and improving prediction robustness on the target site. Experimental results demonstrate that BrainTAL consistently improves disease recognition performance in source-free cross-site scenarios and effectively captures task-relevant functional connectivity patterns, validating its effectiveness and interpretability.