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

Zihan Yin; Yun Jiao

doi:10.1117/12.2687768

28 August 2023 Prediction of brain activity response by functional magnetic resonance imaging based on semantic information

Zihan Yin, Yun Jiao

Author Affiliations +

Proceedings Volume 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023); 127241Q (2023) https://doi.org/10.1117/12.2687768
Event: Second International Conference on Biomedical and Intelligent Systems (IC-BIS2023), 2023, Xiamen, China

Abstract

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.

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Zihan Yin and Yun Jiao "Prediction of brain activity response by functional magnetic resonance imaging based on semantic information", Proc. SPIE 12724, Second International Conference on Biomedical and Intelligent Systems (IC-BIS 2023), 127241Q (28 August 2023); https://doi.org/10.1117/12.2687768

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KEYWORDS

Brain

Functional magnetic resonance imaging

Neuroimaging

Semantics

Performance modeling

Data modeling

Matrices

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