Quantification of pain-induced changes in cerebral blood flow by perfusion MRI
Bibliographic record
Abstract
The purpose of this study was to assess if the functional activation caused by painful stimuli could be detected with arterial spin labeling (ASL), which is a non-invasive magnetic resonance imaging (MRI) technique for measuring cerebral blood flow (CBF). Because ASL directly measures blood flow, it is well suited to pain conditions that are difficult to assess with current functional MRI, such as chronic pain. However, the use of ASL in neuroimaging has been hampered by its low sensitivity. Recent improvements in MRI technology, namely increased magnetic field strengths and phased array receiver coils, should enable ASL to measure the small changes in CBF associated with pain. In this study, healthy volunteers underwent two ASL imaging sessions, during which a painful thermal stimulus was applied to the left hand. The results demonstrated that the ASL technique measured changes in regional CBF in brain regions that have been previously identified with pain perception. These included bilateral CBF changes in the insula, secondary somatosensory, and cingulate cortices, as well as the supplementary motor area (SMA). Also observed were contralateral primary somatosensory and ipsilateral thalamic CBF changes. The average change in CBF for all regions of interest was 3.68ml/100g/min, ranging from 2.97ml/100g/min in ipsilateral thalamus to 4.91ml/100g/min in contralateral insula. The average resting global CBF was 54+/-9.7ml/100g/min, and there was no change in global CBF due to the noxious thermal stimulus.
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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".