Network-Aware Bilinear Tokenization for Brain Functional Connectivity Representation Learning

arXiv:2605.14048v3 Announce Type: replace Abstract: 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 Representati

Source: arXiv cs.AI — read the full report at the original publisher.

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