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- Title
Hierarchical Bayesian Causality Network to Extract High-Level Semantic Information in Visual Cortex.
- Authors
Ma, Yongqiang; Zhang, Wen; Du, Ming; Jing, Haodong; Zheng, Nanning
- Abstract
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.
- Subjects
BAYESIAN analysis; VISUAL cortex; VISUAL perception; IMAGE reconstruction; FUNCTIONAL magnetic resonance imaging; CAUSAL inference
- Publication
International Journal of Neural Systems, 2024, Vol 34, Issue 1, p1
- ISSN
0129-0657
- Publication type
Article
- DOI
10.1142/S0129065724500023