An immersive virtual reality tool for assessing left and right unilateral spatial neglect
Bibliographic record
Abstract
The reported rate of the occurrence of unilateral spatial neglect (USN) is highly variable likely due to the lack of validity and low sensitivity of classical tools used to assess it. Virtual reality (VR) assessments try to overcome these limitations by proposing immersive and complex environments. Nevertheless, existing VR-based tasks are mostly focused only on near space and lack analysis of psychometric properties and/or clinical validation. The present study evaluates the clinical validity and sensitivity of a new immersive VR-based task to assess USN in the extra-personal space and examines the neuronal correlates of deficits of far space exploration. The task was administrated to two groups of patients with right (N = 28) or left (N = 11) hemispheric brain lesions, also undergoing classical paper-and-pencil assessment, as well as a group of healthy participants. Our VR-based task detected 44% of neglect cases compared to 31% by paper-and-pencil tests in the total sample. Importantly, 30% of the patients (with right or left brain lesions) with no clear sign of USN on the paper-and-pencil tests performed outside the normal range in the VR-based task. Voxel lesion-symptom mapping revealed that deficits detected in VR were associated with lesions in insular and temporal cortex, part of the neural network involved in spatial processing. These results show that our immersive VR-based task is efficient and sensitive in detecting mild to strong manifestations of USN affecting the extra-personal space, which may be undetected using standard tools.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".