SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs
For brain disease diagnosis, this work addresses the limitation of treating semantics as auxiliary by directly incorporating them into predictions, enhancing classification robustness.
SABER integrates LLM-derived semantics into brain network analysis via multi-scale hypergraphs, achieving state-of-the-art performance on ABIDE and ADHD-200 datasets with improved stability and interpretability, especially in small-sample settings.
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.