Voxel-Level Brain States Prediction Using Swin Transformer
Journal:
arXiv
Published Date:
Jun 13, 2025
Abstract
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.
