Identification of discrete functional subregions of the human periaqueductal gray
Bibliographic record
Abstract
The midbrain periaqueductal gray (PAG) region is organized into distinct subregions that coordinate survival-related responses during threat and stress [Bandler R, Keay KA, Floyd N, Price J (2000) Brain Res 53 (1):95-104]. To examine PAG function in humans, researchers have relied primarily on functional MRI (fMRI), but technological and methodological limitations have prevented researchers from localizing responses to different PAG subregions. We used high-field strength (7-T) fMRI techniques to image the PAG at high resolution (0.75 mm isotropic), which was critical for dissociating the PAG from the greater signal variability in the aqueduct. Activation while participants were exposed to emotionally aversive images segregated into subregions of the PAG along both dorsal/ventral and rostral/caudal axes. In the rostral PAG, activity was localized to lateral and dorsomedial subregions. In caudal PAG, activity was localized to the ventrolateral region. This shifting pattern of activity from dorsal to ventral PAG along the rostrocaudal axis mirrors structural and functional neurobiological observations in nonhuman animals. Activity in lateral and ventrolateral subregions also grouped with distinct emotional experiences (e.g., anger and sadness) in a factor analysis, suggesting that each subregion participates in distinct functional circuitry. This study establishes the use of high-field strength fMRI as a promising technique for revealing the functional architecture of the PAG. The techniques developed here also may be extended to investigate the functional roles of other brainstem nuclei.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".