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VRNConnect: A virtual reality immersive environment for exploring brain connectivity data
  • Sepehr Jalayer ,
  • Yiming Xiao ,
  • Marta Kersten-Oertel
Sepehr Jalayer
Concordia University, Concordia University

Corresponding Author:[email protected]

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Yiming Xiao
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Marta Kersten-Oertel
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A brain connectome models the clusters of neurons as inter-connected nodes and can be obtained from different imaging techniques, including diffusion and functional MRI, for structural and functional connectivity, respectively. It offers us the opportunity to gain further insights regarding neural circuitry to better understand the mechanisms of brain functions and diseases. However, visualization, spatial understanding, and analysis of the network’s topology are difficult due to the neuroanatomy’s complex configuration of brain parcellation and 3D nature. Virtual reality (VR) off ers more intuitive visualization andinteraction for 3D data than traditional 2D displays and is a great fi t to tackle the challenges in visualizing andanalyzing brain connectomes. We introduce a novel immersive VR platform for connectomic data exploration, VRNConnect, with a user-friendly interface for quantitative network analysis and exploration. We demonstrated the functionalities of the system with structural connectivity.
The VRNConnect software was built using the Unity 3D game engine (v2021.3) and Oculus integration SDKv38. An Occulus Quest2 VR headset was used for system development. To create the structural connectome, we used the DWI and T1w MRI of a single subject provided by the B.A.T.M.A.N tutorial. Whole braintractography was performed using the iFOD2 and SIFT algorithms in MRtrix3, and the connectivitymatrix was extracted with the HCP-MMP1 atlas, resulting in 360 nodes. To allow interactive networkanalysis, we employed the Brain Connectome Toolbox library (bctpy v0.6) as the backends for our VR userinterface to compute graph-based metrics, such as clustering coefficient and the shortest path betweennodes, which can be calculated on both hop count- or distance-based. Both controller- and hand gesture basednode and edge selection and interaction were implemented to pick the node of interest, and functions,including connectivity strength thresholding and brain model scaling and zooming. Furthermore, thesoftware allows users to import their connectomic data and/or utilize their own toolbox (with minor codingadjustments) for analysis.