Localization of pain‐related brain activation: A meta‐analysis of neuroimaging data
Bibliographic record
Abstract
A meta-analysis of 140 neuroimaging studies was performed using the activation-likelihood-estimate (ALE) method to explore the location and extent of activation in the brain in response to noxious stimuli in healthy volunteers. The first analysis involved the creation of a likelihood map illustrating brain activation common across studies using noxious stimuli. The left thalamus, right anterior cingulate cortex (ACC), bilateral anterior insulae, and left dorsal posterior insula had the highest likelihood of being activated. The second analysis contrasted noxious cold with noxious heat stimulation and revealed higher likelihood of activation to noxious cold in the subgenual ACC and the amygdala. The third analysis assessed the implications of using either a warm stimulus or a resting baseline as the control condition to reveal activation attributed to noxious heat. Comparing noxious heat to warm stimulation led to peak ALE values that were restricted to cortical regions with known nociceptive input. The fourth analysis tested for a hemispheric dominance in pain processing and showed the importance of the right hemisphere, with the strongest ALE peaks and clusters found in the right insula and ACC. The fifth analysis compared noxious muscle with cutaneous stimuli and the former type was more likely to evoke activation in the posterior and anterior cingulate cortices, precuneus, dorsolateral prefrontal cortex, and cerebellum. In general, results indicate that some brain regions such as the thalamus, insula and ACC have a significant likelihood of activation regardless of the type of noxious stimuli, while other brain regions show a stimulus-specific likelihood of being activated.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.008 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".