The locus of flicker adaptation in the migraine visual system: A dichoptic study
Bibliographic record
Abstract
BACKGROUND: Flickering light has been shown to sensitize the migraine visual system at high stimulus contrast while elevating thresholds at low contrast. The present study employs a dichoptic psychophysical paradigm to ask whether the abnormal adaptation to flicker in migraine occurs before or after the binocular combination of inputs from the two eyes in the visual cortex. METHODS: Following adaptation to high contrast flicker presented to one eye only, flicker contrast increment thresholds were measured in each eye separately using dichoptic viewing. RESULTS: Modest interocular transfer of adaptation was seen in both migraine and control groups at low contrast. Sensitization at high contrast in migraine relative to control participants was seen in the adapted eye only, and an unanticipated threshold elevation occurred in the non-adapted eye. Migraineurs also showed significantly lower aversion thresholds to full field flicker than control participants, but aversion scores and increment thresholds were not correlated. CONCLUSIONS: The results are simulated with a three-stage neural model of adaptation that points to strong adaptation at monocular sites prior to binocular combination, and weaker adaptation at the level of cortical binocular neurons. The sensitization at high contrast in migraine is proposed to result from stronger adaptation of inhibitory neurons, which act as a monocular normalization pool.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".