NeuroInject: Bridging Brain and Behavior without Paired Subjects via Intermediate Clinical-State Injection for Depression Recognition

Multimodal depression recognition integrates complementary behavioral and neurophysiological evidence to capture different manifestations of depression. Speech and language reflect behavioral and semantic manifestations, whereas EEG captures neurophysiological activity associated with depressive states. Existing multimodal methods typically fuse or align modality representations under naturally paired observations, whereas EEG and behavioral interview datasets are often collected from independent cohorts without subject- or sample-level correspondence. We study this unpaired setting and propose NeuroInject, which preserves the native audio–text pathway of a frozen multimodal large language model while introducing encoded EEG representations into its intermediate computation. Learnable clinical carriers retrieve modality-specific evidence through a shared cross-attention mechanism, accumulate it into a clinical state across layers, and write it back through gated low-rank mappings. EEG and behavioral samples are processed in independent forward passes, with cross-cohort transfer realized through jointly optimized shared parameters and a unified decision space. Experiments on MODMA, EDRA, CMDC, and DAIC-WOZ show that NeuroInject achieves the highest Accuracy among the compared methods on all four datasets, reaching 71.02, 86.81, 91.19, and 65.78, respectively, while AUROC gains are less consistent. Ablations show that the intermediate interaction contributes most of the improvement over a standalone EEG encoder.