BrainLinear: A Linear Model for Brain Network Analysis in Sparse Tangent Subspaces
Functional connectome analysis examines brain-region interactions to understand and identify disorders such as autism spectrum disorder and Alzheimer's disease. Existing methods typically use GNNs and Transformers to model the full functional connectivity matrix. However, processing tens of thousands of connections introduces redundancy and noise, increases computational cost, and limits connection-level interpretability. This raises a central question: do we really need complex interaction modeling, or is identifying a small set of disease-relevant connectivity patterns sufficient? To answer this question, we propose BrainLinear, a lightweight geometry-aware framework for mining disease-...
| Authors | Sijing Wu, Dongyuan Li, Miaoting Huang et al. |
|---|---|
| Primary category | cs.GR |
| Published | 15 August 2026 |
| Last updated at source | 15 August 2026 |
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