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This paper introduces SABER, a novel framework that integrates large language model (LLM) semantics directly into brain network analysis for improved disease diagnosis. By employing multi-scale hypergraphs and a decision-level semantic alignment mechanism, the framework enhances node representations and captures complex interactions among brain regions. Experiments on brain network datasets show that SABER achieves state-of-the-art performance, particularly in small-sample scenarios, while also improving stability and interpretability of predictions.
Directly integrating LLM semantics into brain network analysis leads to unprecedented stability and interpretability in disease diagnosis.
Effective brain disease diagnosis requires the synergy of brain connectivity patterns and high-level semantic knowledge. Existing methods, however, largely treat semantics from large language models (LLMs) as auxiliary features or supervision, limiting their direct role in decision-making and constraining classification stability and robustness. To overcome this, we propose a semantic-aligned brain network framework that actively integrates LLM-derived semantics into the prediction process. Specifically, ROI-level semantics are first incorporated via global self-attention to enrich node representations and provide whole-brain context. Multi-scale hypergraphs are then constructed to explicitly model functional subnetworks and multi-ROI interactions, addressing the locality limitations of traditional GNNs and capturing high-order dependencies. Finally, a decision-level semantic alignment mechanism selectively injects patient-specific textual embeddings into graph representations, enabling semantics to directly guide predictions without perturbing the underlying network structure. Experiments on public brain network datasets ABIDE and ADHD-200 demonstrate state-of-the-art performance, enhanced stability, and improved interpretability, particularly in small-sample settings.
