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

• Published in: Computers in biology and medicine Vol. 197; no. Pt A; p. 110996
• Main Authors: Ajith, Meenu, Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: United States Elsevier Ltd 01.10.2025
• Subjects: Data augmentation; Diffusion models; Intrinsic connectivity networks; Schizophrenia classification; Transformer architectures; Intrinsic connectivity networks; Diffusion models; Data augmentation; Transformer architectures; Schizophrenia classification
• ISSN: 0010-4825; 1879-0534
• DOI: 10.1016/j.compbiomed.2025.110996

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. •TransUNET-DDPM combines transformers with diffusion models to generate anatomically and functionally accurate 3D brain connectivity networks.•A transfer learning strategy enables efficient training and domain adaptation from large-scale unsupervised data to condition-specific neuroimaging tasks.•Class-conditioned ICN synthesis improves schizophrenia classification by augmenting scarce datasets with diagnostically meaningful synthetic samples.