Measuring the neural response to continuous intramuscular infusion of hypertonic saline by perfusion MRI
Bibliographic record
Abstract
PURPOSE: To determine the extent to which arterial spin labeling (ASL), a functional magnetic resonance imaging technique that directly measures cerebral blood flow (CBF), is able to measure the neural activation associated with prolonged experimental muscle pain. MATERIALS AND METHODS: Hypertonic saline (HS) (5% NaCl) was infused into the brachioradialis muscle of 19 healthy volunteers for 15 min. The imaging volume extended from the dorsal side of the pons to the primary somatosensory cortices, covering most of the cortical and subcortical regions associated with pain perception. RESULTS: Using a numerical scale from 0 to 10, ratings of pain intensity peaked at 5.9 ± 0.5 (mean ± SE). Group activation maps showed that the slow infusion of HS evoked CBF increases primarily in bilateral insula, with additional activation in right frontal regions. In the activated areas, CBF gradually increased at the onset of HS infusion and was maintained at relatively constant levels throughout the remainder of the infusion period. However, the level and extent of activation were smaller than observed in previous studies involving acute muscle pain. CONCLUSION: This study demonstrates the ability of ASL to measure changes in CBF over extended periods of time and that the neural activation caused by muscle pain is paradigm specific.
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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 | 0.001 |
| 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".