We are happy to announce that our paper “An interpretable generative multimodal neuroimaging-genomics framework for decoding Alzheimer’s disease” has been published on Journal of Neural Engineering by IOP. If you’re curious about combining multimodal MRI and genetics for Alzheimer’s prediction, this work proposes an interpretable deep-learning framework that can impute missing modalities (via cycle-GANs in latent space) and then explain which inputs drive decisions. It achieves competitive performance for both AD vs controls and MCI conversion, while highlighting disease-relevant gray matter changes, resting-state network disruptions, and genetic signals linked to processes such as endocytosis, amyloid-beta, and cholesterol pathways.