Pain processing in the human brainstem and spinal cord before, during, and after the application of noxious heat stimuli
Bibliographic record
Abstract
Descending regulation of spinal cord responses to nociceptive signaling has a strong influence on pain perception. Previous studies using functional magnetic resonance imaging (fMRI) have indicated that in addition to reactive responses to nociceptive signals, there is a continuous component to regulation, and that it may vary with differences in pain sensitivity. We hypothesize that this continuous regulation component occurs routinely in fMRI studies before noxious stimulation, as well as during, and after stimulation. This hypothesis was tested by analyzing data from 59 healthy participants in 4 previous fMRI studies in our laboratory using noxious heat stimuli. Analyses included structural equation modeling to identify coordinated blood oxygenation-level-dependent (BOLD) signal variations between regions (ie, connectivity) and Bayesian regression of BOLD time-series responses in relation to pain ratings and stimulus temperatures. The results demonstrate the periaqueductal gray-rostral ventromedial medulla-spinal cord descending modulation pathway, influenced by input from the hypothalamus, parabrachial nucleus, and nucleus tractus solitarius. Connectivity between specific regions is observed to vary in relation to pain sensitivity. The results support the conclusion that homeostatic autonomic control influences the net descending pain regulation, and therefore influences pain sensitivity. The results describe the overall properties of pain processing (specifically pain elicited by heat) in the healthy human brainstem and spinal cord, and mechanisms for variation across individuals. This understanding is expected to be important for studies of how pain processing is altered in chronic pain conditions.
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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".