Investigation of the neural basis of expectation-based analgesia in the human brainstem and spinal cord by means of functional magnetic resonance imaging
Bibliographic record
Abstract
PURPOSE: The expected intensity of pain resulting from a noxious stimulus has been observed to have a strong influence on the pain that is perceived. The neural basis of pain reduction, as a result of expecting lower pain, was investigated using functional magnetic resonance imaging (fMRI) in the brainstem and spinal cord. METHODS: Functional MRI studies were carried out in a region spanning the brainstem and cervical spinal cord in healthy participants. Participants were familiarized with a noxious heat stimulus and study procedures in advance, and were informed during each trial that either a heat calibrated to produce moderate pain (Base state), or a temperature 1 °C lower (Low state), would be applied to their hand. However, the Base temperature was applied in every trial. RESULTS: Pain ratings were significantly reduced as a result of expecting lower temperatures. FMRI results demonstrate blood oxygenation-level dependent (BOLD) signal variations in response to participants being informed of the stimulus to expect, in advance of stimulation, and in response to stimulation. Significant coordination of BOLD signals was also detected across specific brainstem and spinal cord regions, with connectivity strengths that varied significantly with the study condition, and with individual pain ratings. The results identify regions that are known to be involved with arousal and autonomic regulation. CONCLUSIONS: Expectation-based analgesia is mediated by descending regulation of spinal cord nociceptive responses. This regulation appears to be related to arousal and autonomic regulation, consistent with the cognitive/affective dimension of pain.
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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".