SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs

• URL: http://arxiv.org/abs/2607.01901v1
• Date: Thu, 02 Jul 2026 08:58:47 GMT
• Title: SABER: A Semantic-Aligned Brain Network Analysis Framework via Multi-scale Hypergraphs
• Abstract summary: Brain disease diagnosis requires synergy of brain connectivity patterns and high-level semantic knowledge.<n>Existing methods treat semantics from large language models (LLMs) as auxiliary features or supervision.<n>We propose a semantic-aligned brain network framework that actively integrates LLM-derived semantics into the prediction process.
• Score: 2.525239819116781
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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