Distinct networks of periaqueductal gray columns in pain and threat processing
Bibliographic record
Abstract
Noxious events that can cause physical damage to the body are perceived as threats. In the brainstem, the periaqueductal gray (PAG) ensures survival by generating an appropriate response to these threats. Hence, the experience of pain is coupled with threat signaling and interfaces in the dl/l and vlPAG columns. In this study, we triangulate the functional circuits of the dl/l and vlPAG by using static and time-varying functional connectivity (FC) in multiple fMRI scans in healthy participants (n = 37, 21 female). The dl/l and vlPAG were activated during cue, heat, and rating periods when the cue signaled a high threat of experiencing heat pain and when the incoming intensity of heat pain was unknown. Responses were significantly lower after low threat cues. The two regions responded similarly to the cued conditions but showed prominent distinctions in the extent of FC with other brain regions. Thus, both static and time-varying FC showed significant differences in the functional circuits of dl/l and vlPAG in rest and task scans. The dl/lPAG consistently synchronized with the salience network and the thalamus, suggesting a role in threat detection, while the vlPAG exhibited more widespread synchronization and frequently connected with memory/language and sensory regions. Hence, these two PAG regions process heat pain when stronger pain is expected or when it is uncertain, and preferentially synchronize with distinct brain circuits in a reproducible manner. The dl/lPAG seems more directly involved in salience detection, while the vlPAG seems engaged in contextualizing threats.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".