SemanticBN: From Individual Brain Networks To Evidence-Grounded Semantic Units

Brain disorder identification from resting-state functional MRI requires traceable connections between individual connectivity measurements, model decisions, and language descriptions. We propose SemanticBN, which links attribute-level prediction to claim-level language generation through traceable brain-state records. Within shared functional units, SemanticBN learns subject-specific within- and between-unit connectivity representations through joint disease identification, attribute preservation, and bidirectional brain–text alignment. A decomposable predictor quantifies each measured attribute’s contribution to the decision score relative to an explicit baseline. Each contribution is linked to its underlying measurement, training-derived reference, and data source, enabling decision-related statements to be checked against the predictor’s computations. These linked records also govern the language interface: claim-level permissions specify supported statements, and a frozen language model uses authorized evidence to answer questions and describe individual brain states. An output verifier checks numerical consistency, evidence citations, and interpretive scope. By coupling attribute-level prediction with claim-level evidence control, SemanticBN provides a unified basis for disease identification and auditable language description, connecting what is measured, how the model uses it, and which statements the evidence supports.