Prediction of brain activity response by functional magnetic resonance imaging based on semantic information

This study investigated an improved method for predicting brain activity using 7T fMRI data. The data resolution was 7T, and the stimulus and fMRI measurement data were paired. Text descriptions were used as original features and were deep learning encoded for model training. The results demonstrated that our model effectively predicted brain activity response, surpassing some previous methods. During experiments, increasing the encoding dimension improved the model's fitting performance. Our study enhances the ability to simulate the brain and investigate its cognitive processes.