Baseline resting-state functional connectivity determines subsequent pain ratings to a tonic ecologically valid experimental model of orofacial pain
Bibliographic record
Abstract
ABSTRACT: Pain is a subjective experience with significant individual differences. Laboratory studies investigating pain thresholds and experimental acute pain have identified structural and functional neural correlates. However, these types of pain stimuli have limited ecological validity to real-life pain experiences. Here, we use an orthodontic procedure-the insertion of an elastomeric separator between teeth-which typically induces mild to moderate pain that peaks within 2 days and lasts several days. We aimed to determine whether the baseline structure and resting-state functional connectivity of key regions along the trigeminal nociceptive and pain modulatory pathways correlate with subsequent peak pain ratings. Twenty-six healthy individuals underwent structural and resting-state functional MRI scanning before the placement of a separator between the first molar and second premolar, which was kept in place for 5 days. Participants recorded pain ratings 3 times daily on a 100-mm visual analogue scale. Peak pain was not significantly correlated with diffusion metrics of the trigeminal nerve or gray matter volume of any brain region. Peak pain did, however, positively correlate with baseline resting-state functional connectivity between the thalamus contralateral to the separator and bilateral insula, and negatively correlated with connectivity between the periaqueductal gray (PAG) and core nodes of the default mode network (medial prefrontal and posterior cingulate cortices). The ascending (thalamic) nociceptive and the descending (PAG) pain modulatory pathways at baseline each explained unique variation in peak pain intensity ratings. In sum, preinterventional functional neural architecture of both systems determined the individual pain experience to a subsequent ecologically valid pain stimulus.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.011 |
| 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) | 0.000 | 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 teacher head, 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".