Intrinsic functional connectivity of periaqueductal gray subregions in humans
Bibliographic record
Abstract
The periaqueductal gray matter (PAG) is a key brain region of the descending pain modulation pathway. It is also involved in cardiovascular functions, anxiety, and fear; however, little is known about PAG subdivisions in humans. The aims of this study were to use resting-state fMRI-based functional connectivity (FC) to parcellate the human PAG and to determine FC of its subregions. To do this, we acquired resting-state fMRI scans from 79 healthy subjects and (1) used a data-driven method to parcellate the PAG, (2) used predefined seeds in PAG subregions to evaluate PAG FC to the whole brain, and (3) examined sex differences in PAG FC. We found that clustering of the left and right PAG yielded similar patterns of caudal, middle, and rostral subdivisions in the coronal plane, and dorsal and ventral subdivisions in the sagittal plane. FC analysis of predefined subregions revealed that the ventolateral(VL)-PAG was supfunctionally connected to brain regions associated with descending pain modulation (anterior cingulate cortex (ACC), upper pons/medulla), whereas the lateral (L) and dorsolateral (DL) subregions were connected with brain regions implicated in executive functions (prefrontal cortex, striatum, hippocampus). We also found sex differences in FC including areas implicated in pain, salience, and analgesia including the ACC and the insula in women, and the MCC, parahippocampal gyrus, and the temporal pole in men. The organization of the human PAG thus provides a framework to understand the circuitry underlying the broad range of responses to pain and its modulation in men and women.
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) | 0.001 | 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".