AR NeuroVison – Explaining Brain Functions in 3D
Presented During: Poster Session 1
Wednesday, June 25, 2025: 01:15 PM - 03:15 PM
Wednesday, June 25, 2025: 01:15 PM - 03:15 PM
Presented During: Poster Session 2
Thursday, June 26, 2025: 01:45 PM - 03:45 PM
Thursday, June 26, 2025: 01:45 PM - 03:45 PM
Presented During:
Wednesday, June 25, 2025: 5:45 PM - 7:00 PM
Brisbane Convention & Exhibition Centre
Room:
M4 (Mezzanine Level)
Poster No:
583
Submission Type:
Abstract Submission
Authors:
Michael Marxen1, Kelsang Mende1, Garvit Joshi1, Ranim Zarour1, Krishnan Chandran1, Daniel Zeidler1, Matthew McGinity1
Institutions:
1Technische Universität Dresden, Dresden, Sachsen
First Author:
Co-Author(s):
Introduction:
Theories on brain function and pathology often involve complex networks of brain regions. A multitude of brain atlases exist, that define such regions in a standard space and a large body of literature has been published on the structural and functional connections between these regions. Visualizing these findings has been challenging because of the amount of data and the intrinsic 3-dimensional (3D) nature of this data. Specifically, visualizing brain connections in an anatomical space is challenging. Thus, our objective is to use virtual and augmented reality goggles to address these difficulties and to create visually appealing and engaging presentations. To this end, we have been developing a novel toolbox to visualize the brain, its regions and functions, different kinds of images of the brain, and other educational materials in a shared, multi-user mixed reality 3D environment. This toolbox is to be used for educational purposes, digital science communication, and data exploration by expert scientists.
Methods:
The system is developed for Meta Quest 3 mixed-reality headsets. Brain models are based on the Allen Human Brain Atlas (Hawrylycz, 2012). ITK-SNAP (http://www.itksnap.org/) (Yushkevich, 2006) is used to produce polygonal parcels, which are further refined and optimised for VR in Blender (https://www.blender.org/). The mixed-reality application is built on the ViewR framework developed at TU Dresden IXLAB (Schier, 2024), a Unity-based framework for co-located mixed reality applications (https://unity.com/). A live connection to the ConnExplore brain connectivity viewer developed in-house (https://connexplore.med.tu-dresden.de) allows viewing of brain connectivities in both 2D and 3D from within the augmented reality (AR) experience.
Results:
The current toolkit includes the following features (see Fig. 1):
1) a 3D model of a parcellated brain
2) a tool to view parcel labels and descriptions
3) a brain explosion tool (see Fig. 1A)
4) a slicing tool, which allows to view registered slice of an anatomical MRI image
5) an interface to the ConnExplore website for a given MNI coordinate to view connectivities
6) A central menu to choose settings for certain features of the toolbox
7) A simple viewer for connectivities using curved lines of variable color and thickness between brain regions
These features will be demonstrated on site using Meta Quest 3 headsets as part of the OHBM 2025 Software Demonstrations.
1) a 3D model of a parcellated brain
2) a tool to view parcel labels and descriptions
3) a brain explosion tool (see Fig. 1A)
4) a slicing tool, which allows to view registered slice of an anatomical MRI image
5) an interface to the ConnExplore website for a given MNI coordinate to view connectivities
6) A central menu to choose settings for certain features of the toolbox
7) A simple viewer for connectivities using curved lines of variable color and thickness between brain regions
These features will be demonstrated on site using Meta Quest 3 headsets as part of the OHBM 2025 Software Demonstrations.
·Figure 1
Conclusions:
We have developed a useable prototype of a toolbox to visualize in multi-user mixed-reality, neuroscientific findings for teaching, digital publication, public outreach and data exploration purposes. The multi-user aspect allows for live teaching and presentations, and collaboration between students. Further improvement will include: a) an improved connectivity viewer, b) a 2D slide viewer in the 3D augmented space, c) a recording tool to save life presentations, and d) a web-interface. Additionally, we will produce content initially relating brain connectivity and alcohol use disorder and other topics. A project course with medical and computer science students in teams will work on different neuropathological topics.
Education, History and Social Aspects of Brain Imaging:
Education, History and Social Aspects of Brain Imaging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Anatomy and Functional Systems
White Matter Anatomy, Fiber Pathways and Connectivity
Neuroinformatics and Data Sharing:
Informatics Other
Keywords:
Design and Analysis
Modeling
Open-Source Code
Open-Source Software
Other - Data Visualization, Augmented Reality, Virtual Reality, Mixed Reality
1|2Indicates the priority used for review
Abstract Information
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Please indicate below if your study was a "resting state" or "task-activation” study.
Healthy subjects only or patients (note that patient studies may also involve healthy subjects):
Was this research conducted in the United States?
Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
Were any animal research approved by the relevant IACUC or other animal research panel? NOTE: Any animal studies without IACUC approval will be automatically rejected.
Please indicate which methods were used in your research:
For human MRI, what field strength scanner do you use?
Which processing packages did you use for your study?
Provide references using APA citation style.
Schier, F. et al. (2024). ViewR: Architectural-Scale Multi-User Mixed Reality With Mobile Head-Mounted Displays. IEEE Transactions on Visualization and Computer Graphics 30(8):5609-5622. https://dl.acm.org/doi/10.1109/TVCG.2023.3299781
Yushkevich, P. et al. (2006). User-guided 3D active contour segmentation of anatomical structures: Significantly improved efficiency and reliability, NeuroImage 31(3):1116-28. https://doi.org/10.1016/j.neuroimage.2006.01.015