TransUNET-DDPM: A transformer-enhanced diffusion model for subject-specific brain network generation and classification.

Generative AI for image synthesis has significantly progressed with the advent of advanced diffusion models. These models have set new benchmarks in creating high-quality and meaningful visual information. In this paper, we introduce TransUNET-DDPM, a novel framework that fuses transformer-based architectures with denoising diffusion probabilistic models (DDPMs) to generate high-quality, 2D and 3D intrinsic connectivity networks (ICNs). This architecture addresses limitations of traditional linear methods like independent component analysis (ICA) by leveraging the nonlinear modeling capabilities of DDPMs, further enhanced through transformer blocks that enable attention-driven feature encoding. To produce subject-specific 3D ICNs, an image-conditioned variant of TransUNET-DDPM is employed, utilizing a spatiotemporal encoder to incorporate resting-state fMRI (rs-fMRI) conditional information. Efficient training is achieved through a transfer learning strategy in which a large-scale, unconditional TransUNET-DDPM is first pretrained to capture general spatial and temporal patterns, followed by fine-tuning on a smaller, condition-specific neuroimaging dataset. Additionally, a class-conditioned version of the model is introduced for data augmentation in schizophrenia classification. By generating synthetic ICNs based on diagnostic labels, this variant enhances the robustness of classifiers, particularly in data-scarce scenarios. Furthermore, quantitative and qualitative evaluations demonstrate that our framework surpasses existing generative models in producing anatomically and functionally meaningful ICNs, with external dataset validation confirming its generalizability.