Using perfusion MRI to measure the dynamic changes in neural activation associated with tonic muscular pain
Bibliographic record
Abstract
Knowledge regarding neural pain processing is primarily the result of studies involving models of brief cutaneous pain; however, clinical pain generally originates in deep tissue and is prolonged. This study measured the dynamic neural activation associated with a muscular pain model incorporating both acute and tonic states. Hypertonic saline (5% NaCl) was infused into the brachioradialis muscle of eleven healthy volunteers for 15min after an initial bolus of 0.5mL. Ten controls followed the same protocol with normal saline (0.9% NaCl). Magnetic resonance images of cerebral blood flow (CBF) were acquired using an arterial spin labelling method. The imaging volume extended from the thalamus to the primary somatosensory cortices, but did not include the brainstem and cerebellum. Using a numerical scale from 0 to 10, ratings of pain intensity peaked at 5.9+/-0.6 and remained near 5 for the remainder of the trial. Controls experienced minimal pain, reporting a peak value of 1.8+/-0.4. Significant CBF increases in rostral and caudal anterior insula bilaterally, anterior mid-cingulate cortex (aMCC), bilateral thalamus, and contralateral posterior insula were observed. The time courses of CBF revealed significant differences in the activation pattern during tonic pain. In particular, a more rapid return to baseline in aMCC versus insula was interpreted as a preferential decrease in the affective component of pain. This conclusion was supported by the strong correlation between pain intensity ratings and CBF in the contralateral insula (R(2)=0.911, p<0.01), which is a region believed to be responsible for pain intensity processing.
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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 | 0.000 |
| 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".