MAGNET: Manifold-Aware Graph Diffusion Network for Connectome Generation
We propose MAGNET, a Manifold-Aware Graph Diffusion Network which uses a normalized-Cholesky representation of the manifold of correlation matrices that guarantees validity.
ProofPaper ↗
Key points
- Functional brain connectome represents neural connectivity as a matrix of pairwise interactions between brain regions.
- Generation of functional connectomes is not only a question of validity; rather, having satisfied the constraints on the correlation matrix, the next step is to recover the class-conditional geometry buried under coarse labels.
- MAGNET lifts noisy latent states into ROI-level region tokens and performs denoising with a relational inductive bias over brain atlas regions.
- Moreover, efforts in making the diffusion denoising class-conditional manifold aware finds utility beyond the highly curved brain connectome generation as this is a critical problem in various general settings.
Sources (1)
- [1]MAGNET: Manifold-Aware Graph Diffusion Network for Connectome GenerationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 12, 03:56 PM
We propose MAGNET, a Manifold-Aware Graph Diffusion Network which uses a normalized-Cholesky representation of the manifold of correlation matrices that guarantees validity.
Functional brain connectome represents neural connectivity as a matrix of pairwise interactions between brain regions.
Extractive summary: sentences quoted from the sources.
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