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Abstract #1587

Voxel-wise Brain State Prediction Using Swin Transformer

Yifei Sun1,2, Qinghao Wen1, Tianming Liu3, Fernando Calamante1,2,4, and Jinglei Lv1,2
1School of Biomedical Engineering, University of Sydney, Sydney, Australia, 2Brain and Mind Center, University of Sydney, Sydney, Australia, 3School of Computing, University of Georgia, Athens, GA, United States, 4Sydney Imaging, University of Sydney, Sydney, Australia

Synopsis

Keywords: fMRI Analysis, fMRI (resting state), Brain State Prediction

Motivation: Understanding brain dynamics is important for neuroscience and mental health. fMRI enables the measurement of neural activities, which can be regarded as brain states.

Goal(s): We aim to predict the human resting-state brain state represented as voxel-wise fMRI data.

Approach: We used 100 HCP subjects resting-state fMRI data. To predict the brain state, we adapted a transformer for the 4D fMRI by adding a convolutional decoder.

Results: Our model achieved a mean squared error of 0.069 when predicting the single brain states based on only 23s of prior fMRI time series. The predicted brain states resemble the real fMRI data.

Impact: This work provides critical insights into the temporal organization of the human brain in healthy individuals, showing the potential of swin transformer in predicting brain states on the voxel level, which may reduce the fMRI scan time in the future.

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