DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research
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arXiv
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| Format: | Preprint |
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2025
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| author | Yao, Jun-Hsiang Li, Mingzheng Liu, Jiayi Li, Yuxiao Feng, Jielin Han, Jun Zheng, Qibao Feng, Jianfeng Chen, Siming |
| author_facet | Yao, Jun-Hsiang Li, Mingzheng Liu, Jiayi Li, Yuxiao Feng, Jielin Han, Jun Zheng, Qibao Feng, Jianfeng Chen, Siming |
| contents | The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges, we developed DTBIA, an Immersive Visual Analytics System for Brain-Inspired Research. In collaboration with domain experts, we identified key requirements for effectively visualizing spatiotemporal and topological patterns at multiple levels of detail. DTBIA incorporates a hierarchical workflow - ranging from brain regions to voxels and slice sections - along with immersive navigation and a 3D edge bundling algorithm to enhance clarity and provide deeper insights into both functional (BOLD) and structural (DTI) brain data. The utility and effectiveness of DTBIA are validated through two case studies involving with brain research experts. The results underscore the system's role in enhancing the comprehension of complex neural behaviors and interactions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2505_23730 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research Yao, Jun-Hsiang Li, Mingzheng Liu, Jiayi Li, Yuxiao Feng, Jielin Han, Jun Zheng, Qibao Feng, Jianfeng Chen, Siming Human-Computer Interaction The Digital Twin Brain (DTB) is an advanced artificial intelligence framework that integrates spiking neurons to simulate complex cognitive functions and collaborative behaviors. For domain experts, visualizing the DTB's simulation outcomes is essential to understanding complex cognitive activities. However, this task poses significant challenges due to DTB data's inherent characteristics, including its high-dimensionality, temporal dynamics, and spatial complexity. To address these challenges, we developed DTBIA, an Immersive Visual Analytics System for Brain-Inspired Research. In collaboration with domain experts, we identified key requirements for effectively visualizing spatiotemporal and topological patterns at multiple levels of detail. DTBIA incorporates a hierarchical workflow - ranging from brain regions to voxels and slice sections - along with immersive navigation and a 3D edge bundling algorithm to enhance clarity and provide deeper insights into both functional (BOLD) and structural (DTI) brain data. The utility and effectiveness of DTBIA are validated through two case studies involving with brain research experts. The results underscore the system's role in enhancing the comprehension of complex neural behaviors and interactions. |
| title | DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2505.23730 |