Cortical thickness and pain sensitivity in zen meditators.
Bibliographic record
Abstract
Zen meditation has been associated with low sensitivity on both the affective and the sensory dimensions of pain. Given reports of gray matter differences in meditators as well as between chronic pain patients and controls, the present study investigated whether differences in brain morphometry are associated with the low pain sensitivity observed in Zen practitioners. Structural MRI scans were performed and the temperature required to produce moderate pain was assessed in 17 meditators and 18 controls. Meditators had significantly lower pain sensitivity than controls. Assessed across all subjects, lower pain sensitivity was associated with thicker cortex in affective, pain-related brain regions including the anterior cingulate cortex, bilateral parahippocampal gyrus and anterior insula. Comparing groups, meditators were found to have thicker cortex in the dorsal anterior cingulate and bilaterally in secondary somatosensory cortex. More years of meditation experience was associated with thicker gray matter in the anterior cingulate, and hours of experience predicted more gray matter bilaterally in the lower leg area of the primary somatosensory cortex as well as the hand area in the right hemisphere. Results generally suggest that pain sensitivity is related to cortical thickness in pain-related brain regions and that the lower sensitivity observed in meditators may be the product of alterations to brain morphometry from long-term practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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) | 0.003 | 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".