Latent graph encoding of multimodal neuroimaging features with generative AI architectures

2026-07-08

AI Summary

This study addresses the challenge of effectively integrating structural and functional neuroimaging data by proposing a multimodal graph variational autoencoder (gMMVAE). The method introduces a modality-aware graph encoding mechanism that maps gray matter volume and static functional connectivity into a unified low-dimensional latent space. It further provides a systematic comparison of diverse generative architectures—including VAEs, Transformers, GANs, and diffusion models—in modeling graph-structured brain data. Experimental results demonstrate that gMMVAE consistently outperforms existing approaches in terms of generation fidelity, reconstruction quality, computational efficiency, and discriminative power of the latent representation. This work thus establishes an efficient and interpretable generative modeling paradigm for multimodal brain network analysis.

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.

Problem

• multimodal neuroimaging
• latent space
• graph encoding
• generative AI
• functional connectivity

Innovation

• graph VAE
• multimodal neuroimaging
• latent space fusion
• generative AI
• functional connectivity encoding