# Learning Sequential Information in Task-based fMRI for Synthetic Data Augmentation

arXiv: https://arxiv.org/abs/2308.15564 · HTML: https://mlsanity.com/paper/2308.15564/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 23 references that are in the map.
- First posted on arXiv 29 Aug 2023.

## Builds on (most cited first, 11 of 11)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [Video Swin Transformer](https://mlsanity.com/paper/2106.13230/) (2021)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [Generation of 3D Brain MRI Using Auto-Encoding Generative Adversarial Networks](https://mlsanity.com/paper/1908.02498/) (2019)
- [CovidGAN: Data Augmentation Using Auxiliary Classifier GAN for Improved Covid-19 Detection](https://mlsanity.com/paper/2103.05094/) (2021)
- [Variational Approaches for Auto-Encoding Generative Adversarial Networks](https://mlsanity.com/paper/1706.04987/) (2017)
- [FMRI data augmentation via synthesis](https://mlsanity.com/paper/1907.06134/) (2019)
- [4D Semantic Cardiac Magnetic Resonance Image Synthesis on XCAT Anatomical Model](https://mlsanity.com/paper/2002.07089/) (2020)

## Built on by (most cited first, 2 of 2)

- [Scalable Diffusion Transformer for Conditional 4D fMRI Synthesis](https://mlsanity.com/paper/2511.22870/) (2025)
- [Boosting Brain-to-Image Decoding with TRIBE v2 Data Augmentation](https://mlsanity.com/paper/2606.06345/) (2026)

## Abstract (arXiv)

Insufficiency of training data is a persistent issue in medical image analysis, especially for task-based functional magnetic resonance images (fMRI) with spatio-temporal imaging data acquired using specific cognitive tasks. In this paper, we propose an approach for generating synthetic fMRI sequences that can then be used to create augmented training datasets in downstream learning tasks. To synthesize high-resolution task-specific fMRI, we adapt the α-GAN structure, leveraging advantages of both GAN and variational autoencoder models, and propose different alternatives in aggregating temporal information. The synthetic images are evaluated from multiple perspectives including visualizations and an autism spectrum disorder (ASD) classification task. The results show that the synthetic task-based fMRI can provide effective data augmentation in learning the ASD classification task.