VRNConnect: Towards More Intuitive Interaction of 3D Brain Connectivity Data in Virtual Environments
Bibliographic record
Abstract
With more and more availability of both functional Magnetic Resonance Imaging (fMRI) and Diffusion Tensor Imaging (DTI), there is increasing interest and evidence that brain network analysis can enable new possibilities to understand brain function and disease. We have developed an open-source virtual reality (VR) platform, VRNConnect, for exploring brain connectivity data to enable researchers, students, and clinicians to map the different regions of the brain and study their functions in a more interactive, intuitive and engaging way. VRNConnect allows users to interact with the data to view various graph network measurements such as node strength, degree, and clustering coefficient by using the Brain Connectivity Analysis tools during runtime. We performed a user study to de-termine the intuitiveness and usability of the platform as well as looked at differences in controller versus gesture interaction methods. Results showed that VRNConnect is easy to use, easy to learn and intuitive. In terms of the interaction methods, almost all participants performed significantly better, doing tasks with the controller as opposed to hand gestures in both task completion time and task errors. VRNConnect showed promising results in both usability scale (SUS) and cognitive load (NASA TLX), showing the potential to be used as an academic and analytical tool, with a customizable design for importing connectivity data from different modalities and flexible and intuitive interaction methods.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".