Delineation of the Trigeminal-Lateral Parabrachial-Central Amygdala Tract in Humans: An Ultra-High Field Diffusion MRI Study
Bibliographic record
Abstract
ABSTRACT Orofacial pain is thought to be more unpleasant than pain elsewhere in the body due to the importance of the face in social, feeding, and exploratory behaviors. Nociceptive information from the orofacial region is carried to the brain via the trigeminal nerve (CNV) via the trigeminal brainstem sensory nuclear complex (VBSNC). Pre-clinical evidence revealed a monosynaptic circuit from CNV to the lateral parabrachial nucleus (latPB), which underlies the greater unpleasantness elicited by orofacial pain. The latPB further projects to the central amygdala (CeA), which contributes to the affective component of pain in rodents. However, this circuit has yet to be delineated in humans. Here, we aimed to resolve this circuit using 7T diffusion-weighted imaging from the Human Connectome Project (HCP). We performed probabilistic tractography in 80 participants to resolve the CNV-latPB-CeA circuit. The basolateral amygdala (BLAT) was used as a negative control, given that we did not anticipate CNV-latPB-BLAT connectivity. Connectivity strengths were compared using a repeated-measures ANOVA with factors ‘hemisphere’ (left; right), and ‘target’ (CeA; BLAT), with sex included in the model for both pilot and validation samples. Only the ‘target’ factor was significant in both samples ( F Pilot = 11.4804, p = 0.005; F Validation = 69.113, p < .001). Post hoc tests showed that the CeA had significantly stronger connectivity strength than the BLAT ( p Tukey-Pilot = 0.005; p Tukey-Validation < 0.001). □This study delineates the human CNV-latPB-CeA circuit for the first time in vivo. This circuit may provide a neuroanatomical substrate for the affective dimensions of orofacial pain. SUMMARY This study delineates the human trigeminal-parabrachio-amygdalar circuit in vivo. This circuit may provide a neuroanatomical substrate for the affective dimension of orofacial pain.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".