Differential fMRI neural synchrony associated with migraine during naturalistic stimuli with negative emotional valence
Bibliographic record
Abstract
Abstract Migraine is a common neurological disorder that impacts approximately 12% of the general population and is characterized by moderate to severe headaches, nausea, mood changes, and fatigue. It impacts lower-level visual and auditory processing, causing hypersensitivities that lead to heightened audiovisual multisensory integration. However, the impact of migraine on the processing of complex, audiovisual stimuli is still unclear. Additionally, migraine may induce hypersensitivities to emotional arousal and valence, though the relative significance of these factors remains unknown. The current study seeks to identify how migraine impacts synchronous neural processing of complex, audiovisual stimuli, and how this differs based on the emotional arousal and valence of the stimulus. To do so, we collected functional magnetic resonance imaging data from 22 migraineurs and 21 healthy controls during the passive viewing of three audiovisual films of differing emotional arousal and valence. We identified that, in response to a negative valence, high arousal emotional stimulus, the migraine group showed greater neural synchrony in regions associated with multisensory integration, including the bilateral posterior superior temporal gyrus (pSTG), superior parietal lobule (SPL), and left middle temporal gyrus (MTG). There were no significant differences in neural synchrony between the migraine and control groups in response to positive valence, high arousal and neutral valence, low arousal stimuli. These findings suggest that migraine involves hypersensitivity to audiovisual movies as a function of negative emotional valence, where negative/aversive emotional states may drive greater synchrony in multisensory integration. Overall, this research highlights distinct pathways through which emotion and arousal impact neural processing in migraine.
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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".