Evidence for abnormal visuospatial attentional processes in the interictal migraineur
Bibliographic record
Abstract
Research shows decreased brain region activity in the right temporo-parietal junction (rTPJ) in people with migraine headache relative to headache-free controls when performing an orienting visuospatial attention task. Functional inactivation of the rTPJ has been associated with rightward performance deviations on laterality-based attention Landmark (LM) and greyscale (GRE) tasks in individuals with unilateral neglect and heightened activation in the rTPJ is associated with leftward deviation, known as pseudoneglect, in controls on these tasks. Given this, we investigated whether migraineurs would lack the leftward deviation found in headache-free controls on visuospatial attention tasks. 36 migraineurs and 38 controls were presented with LM and GRE tasks. Response bias scores showed a significant difference in responses between groups (p = 0.036) on the GRE, a luminance-based task, but not on the LM, a size-based task (p = 0.826). This study is the first to show laterality-based attentional differences in migraineurs, as compared to controls. Specifically, migraineurs were found to have smaller leftward biases on luminance-based visuospatial attention tasks, as compared to controls, aligning with previous research suggesting that migraine may be having an impact on a variety of attention tasks in migraineurs in between headache attacks.
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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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".