VRNConnect: A virtual reality immersive environment for exploring brain connectivity data
Bibliographic record
Abstract
Introduction: 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. Methods: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".