Latent graph encoding of multimodal neuroimaging features with generative AI architectures

Researchers developed a multimodal generative framework for structural and functional MRI features, evaluating VAEs, transformers, GANs, and diffusion models. Their proposed graph VAE (gMMVAE) outperformed other variants in generation fidelity, reconstruction quality, efficiency, and latent space discriminability, demonstrating potential for robust neuroimaging analysis.

arXiv:2607.07027v1 Announce Type: cross Abstract: While generative models enable encoding of complex neuroimaging data for feature generation and reconstruction, developing optimal architectural frameworks with appropriate encoding and latent space processes is crucial for studying structural and functional properties of the brain. We design a multimodal generative framework for structural and functional magnetic resonance imaging MRI features through systematic evaluation of encoding strategies, latent multimodal fusion, and generative model selection. Using structural gray matter volume GMV and static functional network connectivity sFNC features from a large neuroimaging dataset, we analyze generative frameworks involving variational autoencoders VAEs , transformers, generative adversarial networks GANs , and diffusion models. Architectures that employ modality-aware graph encoding of functional connectivity into a lower-dimensional latent space outperform vectorized encoders or direct data space approaches. The proposed multimodal graph VAE gMMVAE surpasses alternative generative variants across multiple metrics for generation fidelity, reconstruction quality, efficiency, and latent space discriminability, highlighting its potential for robust multimodal neuroimaging analysis.