Hierarchical Bayesian Causality Network to Extract High-Level Semantic Information in Visual Cortex

• Published in: International journal of neural systems Vol. 34; no. 1; p. 2450002
• Main Authors: Ma, Yongqiang, Zhang, Wen, Du, Ming, Jing, Haodong, Zheng, Nanning
• Format: Journal Article
• Language: English
• Published: Singapore 01.01.2024
• Subjects: hierarchical Bayesian causality network; fMRI; semantic information; Cognitive computing; Bayesian network; visual cognition
• DOI: 10.1142/S0129065724500023

Functional MRI (fMRI) is a brain signal with high spatial resolution, and visual cognitive processes and semantic information in the brain can be represented and obtained through fMRI. In this paper, we design single-graphic and matched/unmatched double-graphic visual stimulus experiments and collect 12 subjects' fMRI data to explore the brain's visual perception processes. In the double-graphic stimulus experiment, we focus on the high-level semantic information as "matching", and remove tail-to-tail conjunction by designing a model to screen the matching-related voxels. Then, we perform Bayesian causal learning between fMRI voxels based on the transfer entropy, establish a hierarchical Bayesian causal network (HBcausalNet) of the visual cortex, and use the model for visual stimulus image reconstruction. HBcausalNet achieves an average accuracy of 70.57% and 53.70% in single- and double-graphic stimulus image reconstruction tasks, respectively, higher than HcorrNet and HcasaulNet. The results show that the matching-related voxel screening and causality analysis method in this paper can extract the "matching" information in fMRI, obtain a direct causal relationship between matching information and fMRI, and explore the causal inference process in the brain. It suggests that our model can effectively extract high-level semantic information in brain signals and model effective connections and visual perception processes in the visual cortex of the brain.