Superior Colliculi Involvement in Poststroke Unilateral Spatial Neglect: A Pilot Study
Bibliographic record
Abstract
PURPOSE: The neural mechanisms underlying unilateral spatial neglect (USN) are unclear. The superior colliculi (SC) may be involved in USN expression, and the spatial summation effect (SSE), where reaction times to bilateral stimuli are faster than to unilateral, may be a behavioral index of SC function. We determined the feasibility of investigating SC contribution to poststroke USN using the SSE in 3 groups. METHODS: Seven participants with left near-extrapersonal space USN (USN+) following right hemisphere stroke, 10 without (USN-), and 10 controls were tested under binocular/monocular (right eye patched) conditions while responding to unilateral/bilateral stimuli. Control and USN- groups completed the SSE paradigm. RESULTS: Most USN+ participants were unable to initiate the SSE paradigm due to poor visual fi xation and demonstrated higher contrast sensitivity for left-sided stimuli. Controls showed an SSE (under both viewing conditions) while the USN- showed an abnormal SSE whereby reaction times to bilateral stimuli were faster than to unilateral-left but not to unilateral-right stimuli (under both binocular/monocular conditions). CONCLUSION: This study is the fi rst to investigate SC contribution in poststroke USN using the SSE; we identifi ed higher contrast sensitivity to left-sided stimuli and poor fi xation in the USN+ group. These fi ndings suggest avenues for research that may lead to novel rehabilitation interventions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".