README.md

📰 News

• [2026.05.20] Please look forward to our upcoming work!

• [2025.05.18] We have released the code and paper for DSFM !

📄 Abstract

Functional Magnetic Resonance Imaging (fMRI) provides non-invasive access to dynamic brain activity by measuring blood oxygen level-dependent (BOLD) signals over time. However, the resource-intensive nature of fMRI acquisition limits the availability of high-fidelity samples required for data-driven brain analysis models. While modern generative models can synthesize fMRI data, they often remain challenging in replicating their inherent non-stationarity, intricate spatiotemporal dynamics, and physiological variations of raw BOLD signals.

To address these challenges, we propose Dual-Spectral Flow Matching (DSFM), a novel fMRI generative framework that cascades dual frequency representation of BOLD signals with spectral flow matching. Specifically, our framework first converts BOLD signals into a wavelet decomposition map via a discrete wavelet transform (DWT) to capture globalized transient and multi-scale variations, and projects into the discrete cosine transform (DCT) space across brain regions and time to exploit localized energy compaction of low-frequency dominant BOLD coefficients. Subsequently, a spectral flow matching model is trained to generate class-conditioned cosine-frequency representation. The generated samples are reconstructed through inverse DCT and inverse DWT operations to recover physiologically plausible time-domain BOLD signals. This dual-transform approach imposes structured frequency priors and preserves key physiological brain dynamics. Ultimately, we demonstrate the efficacy of our approach through improved downstream fMRI-based brain network classification.

🎯 How to Use

Datasets

1. NetSim

https://www.fmrib.ox.ac.uk/datasets/netsim/index.html

2. MDD

https://rfmri.org/REST-meta-MDD

After preprocessing, place the processed data in the project’s empty /data/<desired_dataset> folder.

Installation

Download and set up the repository:

https://github.com/htew0001/DSFM
cd DSFM

We provide a requirements.yaml file to easily create a Conda environment configured to run the model:

conda env create -f requirements.yaml
conda activate DSFM

Usage

We include three main scripts to perform different tasks:

• Conditional Generation: run_training.py - Executes the training of conditional generative task for disease and healthy-control groups.
• Conditional Sampling: run_inference.py - Executes the sampling of conditional generative task for disease and healthy-control groups.
• Evaluation Metrics: run_evaluation.py - Executes the evaluation of various time-series metrics.

For Training of Conditional Generative Models:

python run_training.py --config ./configs/conditional/<desired_dataset>.yaml

For Sampling of Conditional Generative Models:

python run_inference.py --config ./configs/conditional/<desired_dataset>.yaml

For Evaluation of Conditional Generative Models:

python run_evaluation.py 

❤️ Acknowledgements

This repo is mainly built on T2I-Diff. Thanks for the great work.

📝 Citation

If you find our work useful, please cite our related paper:

# ICLR 2026
@inproceedings{tewfunctional,
  title={Functional MRI Time Series Generation via Wavelet-Based Image Transform and Spectral Flow Matching for Brain Disorder Identification},
  author={Tew, Hwa Hui and Loo, Junn Yong and Yu, Leong Fang and Lau, Julia K and Fan, Ding and Ombao, Hernando and Phan, Raphael CW and Tan, Chee Pin and Ting, Chee-Ming},
  booktitle={The Fourteenth International Conference on Learning Representations}
}

# MICCAI 2025
@inproceedings{tew2025t2i,
  title={T2I-Diff: fMRI Signal Generation via Time-Frequency Image Transform and Classifier-Free Denoising Diffusion Models},
  author={Tew, Hwa Hui and Loo, Junn Yong and Tan, Yee-Fan and Tang, Xinyu and Ombao, Hernando and Noman, Fuad and Phan, Rapha{\"e}l C-W and Ting, Chee-Ming},
  booktitle={International Conference on Medical Image Computing and Computer-Assisted Intervention},
  pages={640--650},
  year={2025},
  organization={Springer}
}

📧 Contact

For any inquiries, please contact at hwa.tew@monash.edu.