# Voxel-Level Brain States Prediction Using Swin Transformer

arXiv: https://arxiv.org/abs/2506.11455 · HTML: https://mlsanity.com/paper/2506.11455/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 62 references that are in the map.
- First posted on arXiv 13 Jun 2025.

## Builds on (most cited first, 8 of 8)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Brain-JEPA: Brain Dynamics Foundation Model with Gradient Positioning and Spatiotemporal Masking](https://mlsanity.com/paper/2409.19407/) (2024)
- [SwiFT: Swin 4D fMRI Transformer](https://mlsanity.com/paper/2307.05916/) (2023)
- [Predicting Human Brain States with Transformer](https://mlsanity.com/paper/2412.19814/) (2024)
- [Evaluating phase synchronization methods in fMRI: a comparison study and new approaches](https://mlsanity.com/paper/2009.10126/) (2020)

## Built on by (most cited first, 5 of 5)

- [SLIM-Brain: A Data- and Training-Efficient Foundation Model for fMRI Data Analysis](https://mlsanity.com/paper/2512.21881/) (2025)
- [FlexiBrain: Resolution-Agnostic Voxel-Level Encoding for Native fMRI](https://mlsanity.com/paper/2606.11500/) (2026)
- [BrainWorld: A Structural-Prior-Conditioned Generative Model for Whole-Brain 4D fMRI Dynamics](https://mlsanity.com/paper/2606.17742/) (2026)
- [BrainCast: A Spatio-Temporal Forecasting Model for Whole-Brain fMRI Time Series Prediction](https://mlsanity.com/paper/2603.13361/) (2026)
- [FlatClip: Reusing Image Foundation Models for fMRI Representation Learning via Cortical Flatmaps](https://mlsanity.com/paper/2609.31204/) (2026)

## Abstract (arXiv)

Understanding brain dynamics is important for neuroscience and mental health. Functional magnetic resonance imaging (fMRI) enables the measurement of neural activities through blood-oxygen-level-dependent (BOLD) signals, which represent brain states. In this study, we aim to predict future human resting brain states with fMRI. Due to the 3D voxel-wise spatial organization and temporal dependencies of the fMRI data, we propose a novel architecture which employs a 4D Shifted Window (Swin) Transformer as encoder to efficiently learn spatio-temporal information and a convolutional decoder to enable brain state prediction at the same spatial and temporal resolution as the input fMRI data. We used 100 unrelated subjects from the Human Connectome Project (HCP) for model training and testing. Our novel model has shown high accuracy when predicting 7.2s resting-state brain activities based on the prior 23.04s fMRI time series. The predicted brain states highly resemble BOLD contrast and dynamics. This work shows promising evidence that the spatiotemporal organization of the human brain can be learned by a Swin Transformer model, at high resolution, which provides a potential for reducing the fMRI scan time and the development of brain-computer interfaces in the future.