Functional connectivity of the anterior cingulate cortex with pain-related regions in children with post-traumatic headache
Bibliographic record
Abstract
Introduction: Post-traumatic headaches (PTH) are common following mild traumatic brain injury (mTBI). There is evidence of altered central pain processing in adult PTH; however, little is known about how children with PTH process pain. The anterior cingulate cortex (ACC) plays a critical role in descending central pain modulation. In this study, we explored whether the functional connectivity (FC) of the ACC is altered in children with PTH. Methods: In this case-control study, we investigated resting-state FC of 5 ACC seeds (caudal, dorsal, rostral, perigenual, and subgenual) in children with PTH ( n = 73) and without PTH ( n = 29) following mTBI, and healthy controls ( n = 27). Post-concussion symptoms were assessed using the Post-Concussion Symptom Inventory and the Child Health Questionnaire. Resting-state functional Magnetic Resonance Imaging (fMRI) data were used to generate maps of ACC FC. Group-level comparisons were performed within a target mask comprised of pain-related regions using FSL Randomise. Results: We found decreased FC between the right perigenual ACC and the left cerebellum, and increased FC between the right subgenual ACC and the left dorsolateral prefrontal cortex in children with PTH compared to healthy controls. The ACC FC in children without PTH following mTBI did not differ from the group with PTH or healthy controls. FC between rostral and perigenual ACC seeds and the cerebellum was increased in children with PTH with pre-injury headaches compared to those with PTH without pre-injury headaches. There was a positive relationship between PTH severity and rostral ACC FC with the bilateral thalamus, right hippocampus and periaqueductal gray. Conclusions: Central pain processing is altered in children with PTH. Pre-existing headaches help to drive this process. Trial registration: The PlayGame Trial was registered in ClinicalTrials.gov database ( ClinicalTrials.gov Identifier: NCT01874847).
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".