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Related Experiment Video

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Co-analysis of Brain Structure and Function using fMRI and Diffusion-weighted Imaging
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From Structure to Function and Back Again: A GAN-Guided Diffusion Framework for Generating Clinically Meaningful

Reihaneh Hassanzadeh1,2, Anees Abrol1, Hamid Reza Hassanzadeh3

  • 1Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS): Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta, GA, USA.

Research Square
|June 29, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a new AI framework to generate missing brain imaging data, improving analysis for conditions like Alzheimer's disease. The method enhances multimodal brain imaging by creating realistic synthetic data for better research insights.

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Area of Science:

  • Neuroimaging
  • Artificial Intelligence
  • Biomedical Data Science

Background:

  • Multimodal brain imaging offers rich insights but is often hampered by missing data.
  • Conventional methods for handling missing data can introduce bias or discard valuable information.
  • Generative models show promise for synthesizing missing neuroimaging modalities.

Purpose of the Study:

  • To develop a novel generative framework for cross-modality translation in brain imaging.
  • To synthesize missing T1-weighted magnetic resonance imaging (MRI) and functional network connectivity (FNC) data.
  • To enable robust multimodal brain imaging analysis even with incomplete datasets.

Main Methods:

  • Introduced a Generative Adversarial Network (GAN)-guided diffusion framework.
  • Integrated conditional diffusion modeling, adversarial learning, and cycle-consistency.
  • Enabled training with both paired and unpaired multimodal brain imaging data.

Main Results:

  • Achieved superior performance in synthesizing T1-weighted MRI, with high PSNR (24.95) and SSIM (0.86).
  • Demonstrated improved correlation (0.65) with real functional network connectivity data.
  • Showcased the model's ability to capture clinical group variability without diagnostic labels.

Conclusions:

  • The GAN-guided diffusion framework effectively synthesizes realistic and clinically meaningful T1-weighted MRI and FNC data.
  • This approach enhances multimodal brain imaging analysis, particularly for conditions like Alzheimer's disease.
  • The method facilitates downstream analysis and biomarker discovery by generating high-quality synthetic modalities.