A Connectome Interpreter for Brain Foundation Model: From Direct to Directed Connectivity

Brain foundation models (BFMs) recently demonstrate strong predictive performance across various clinico-demographic traits; yet, what they internally represent remains a black box. Here, we introduce a data-driven connectome interpreter by training a canonical BFM with bias-free learnable embedding tokens on large-scale human lifespan fMRI datasets. This framework allowed us to investigate biological implication of whole-brain spatiotemporal self-attention, a core operation of BFM. Our analyses revealed that the self-attention, driven by learnable-embedding representations naturally emerging from the data, is far more than an intermediate product for downstream prediction but intrinsically embraces key information to recover the brain's "direct" connectivity (unmediated, non-spurious communication backbone). Moreover, integrating these attention weights with an established effective-connectivity method, a framework we formalized as AXON (Attention-informed eXtraction Of directed Networks), accurately resolves "directionality" (sender-receiver relationships) of these connections with high accuracy in both mouse and macaque tract-tracing connectomes as well as in the human-brain simulations where the ground-truth network is synthesized based on the empirical data. Finally, when leveraging the AXON-derived effective connectivity to predict comprehensive downstream tasks including demographics, phenotypes (58 behavioral traits) and brain states (8 task conditions), our approach broadly outperformed all state-of-the-art BFMs as well as classic yet powerful linear regression models that have recently rivaled the BFM accuracy, yielding top performance in most individual traits. These results suggest that extracting a biologically meaningful interpretation of what foundation models internally compute—rather than scaling or hand-designing their architecture—is a critical next step for developing robust BFM-based imaging markers.