DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research

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.

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Publication Details

Journal
IEEE Transactions on Visualization and Computer Graphics
Published
2025-05-05
DOI
https://doi.org/10.1109/tvcg.2025.3567135
Citations
1
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
1.28
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DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research

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article

DTBIA: An Immersive Visual Analytics System for Brain-Inspired Research

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article en
1 citations

Abstract

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.

IEEE Transactions on Visualization and Computer GraphicsVol. 31(6)
Hong Kong University of Science and Technology (HK), Fudan University (CN), Shanghai Center for Brain Science and Brain-Inspired Technology (CN), The Ohio State University (US), University of Hong Kong (HK)
Openalex Percentile: Top 21%
Data Visualization and Analytics
1.28
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