Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning
Masked autoencoders (MAEs) have recently shown promise for self-supervised representation learning of resting-state brain functional connectivity (FC). However, a fundamental question remains unresolved: how should FC matrices be tokenized to align with the intrinsic modular organization of large-scale brain networks? Existing approaches typically adopt region-centric or graph-based schemes that treat FC as structurally homogeneous elements and overlook the large-scale network brain organization. We introduce NERVE (Network-Aware Representations of Brain Functional Connectivity via Bilinear Tokenization), a self-supervised learning framework that redefines FC tokenization by partitioning ...
| Authors | Leo Milecki, Qingyu Hu, Bahram Jafrasteh et al. |
|---|---|
| Primary category | cs.AI |
| Published | 13 May 2026 |
| Last updated at source | 19 May 2026 |
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