Bibliographic record
Abstract
PURPOSE OF REVIEW: To present an overview of insights into brain mechanisms of pain perception and analgesia based on human brain imaging. RECENT FINDINGS: The technical advancement made in both functional and structural MRI can be used to delineate the cerebral signature of pain and analgesia, specifically, the brain responses to noxious stimuli and specific pain-related forebrain responses, as well as pain modulatory effects. Neuroimaging has revealed that the brain response to noxious stimuli shares neural resources with other systems that subserve salience detection and reward functions. Recent findings indicate that there is a wide range of individual differences in pain-related brain function and structure due to both pre-existing vulnerabilities and disease-driven factors. Furthermore, several studies now illustrate that the brain is capable of tremendous plasticity both in function and structure due to repeated and ongoing pain. However, emerging data suggest that this plasticity can be reversible after successful pain treatment. SUMMARY: Neuroimaging of pain and plasticity can provide a framework to understand the basic mechanisms of pain regarding function, gray and white matter structure and connectivity. This information may also guide future clinical practice. For instance, the time-course of disease-driven brain plasticity and capacity for reversibility may help decide the optimal time frame for chronic pain treatment. Furthermore, findings from functional and structural connectivity studies may indicate potential side effects of targeting specific brain areas in treating chronic pain. Lastly, the correlation between individual factors and functional/structural MRI data may direct individualized treatment plans.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".