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Brain Imaging

Brain Imaging

Brain imaging technologies provide critical insights into both the structure and function of the human brain, enabling medical professionals and researchers to diagnose, study, and treat neurological disorders or psychiatric disorders more effectively.
These technologies include computerized axial tomography (CAT or CT scans), positron-emission tomography (PET scans), magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and Transcranial Magnetic...

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Modeling the Functional Network for Spatial Navigation in the Human Brain

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

Meenu Ajith¹, Vince D Calhoun¹

¹Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, 55 Park Pl NE, Atlanta, 30303, GA, USA.

Computers in Biology and Medicine

|August 29, 2025

Summary

This study introduces TransUNET-DDPM, a novel generative AI framework for creating high-quality intrinsic connectivity networks (ICNs). It enhances neuroimaging analysis and aids in schizophrenia classification by generating realistic synthetic data.

Keywords:

Data augmentation Diffusion models Intrinsic connectivity networks Schizophrenia classification Transformer architectures

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Area of Science:

Artificial Intelligence
Neuroimaging
Computer Vision

Background:

Generative AI, particularly diffusion models, has advanced image synthesis.
Existing methods for intrinsic connectivity networks (ICNs) have limitations.

Purpose of the Study:

Introduce TransUNET-DDPM, a novel framework fusing transformers and denoising diffusion probabilistic models (DDPMs).
Generate high-quality 2D and 3D intrinsic connectivity networks (ICNs).
Enhance neuroimaging analysis and aid in data augmentation for classification tasks.

Main Methods:

Utilized a transformer-based architecture with DDPMs for nonlinear modeling.
Employed an image-conditioned variant with a spatiotemporal encoder for subject-specific 3D ICNs from resting-state fMRI (rs-fMRI).
Implemented transfer learning for efficient training and a class-conditioned version for data augmentation.

Main Results:

TransUNET-DDPM generates anatomically and functionally meaningful ICNs, outperforming existing models.
The image-conditioned variant effectively produces subject-specific 3D ICNs.
Class-conditioned generation improved classifier robustness for schizophrenia detection, especially in data-scarce situations.

Conclusions:

TransUNET-DDPM offers a powerful new approach for generating high-fidelity ICNs.
The framework demonstrates significant potential in advancing neuroimaging research and clinical applications.
Validated generalizability across external datasets, confirming its robust performance.