Prior Text-Informed Gate Attention Framework for Multimodal Psychiatric Disorder Diagnosis

Diagnosis of psychiatric disorders from functional magnetic resonance imaging (fMRI) is hindered by low-resource phenotypes, high inter-subject variability, and weakly interpretable multimodal fusion. We propose a prior text-informed gated attention framework (PTGA) that converts region-of-interest (ROI) level neuroimaging measures into statistically grounded textual priors via percentile-based soft binning. PTGA combines hemodynamic response function (HRF) aware spatiotemporal encoding with static graph topology modeling, and unifies variable-length fMRI time series through masked global temporal aggregation. It further uses text semantics as an inductive bias for prior-guided gated fusion, improving robustness under cross-modal noise. We compare PTGA with eight strong graph and multimodal baselines under a unified protocol. PTGA achieves up to 10.46% and 8.01% absolute improvements in autism spectrum disorder (ASD) and major depressive disorder (MDD) diagnosis, respectively, outperforming SOTA baselines. The learned gating also provides biologically plausible evidence: MDD related signals are concentrated in sensorimotor/visual/default-mode systems, while ASD related evidence is centered on the cerebellar network with joint contributions from default-mode, limbic, and dorsal-attention networks.