Increased hippocampal efficiency is associated with greater headache frequency in adolescents with chronic headache
Bibliographic record
Abstract
Abstract Adults with chronic headache have altered brain hippocampal efficiency networks. Less is known about the mechanisms underlying chronic headache in youth. Twenty-nine youth with chronic headache (10–18 years), and twenty-nine healthy, age- and sex-matched controls tracked their headache attacks daily for one-month period. Following this, they underwent a resting state functional magnetic resonance imaging (rs-fMRI) scan and self-reported on their pubertal status, PTSD, anxiety, and depression symptoms. Graph-based topological analyses of brain networks, rendering hippocampal efficiency values were performed. T-tests were used to compare hippocampal efficiency metrics between patients and controls. Linear regression was used to examine significant hippocampal efficiency metrics in relation to headache frequency in patients, controlling for age, sex, PTSD, pubertal status, anxiety, and depression symptoms. Patients had higher right hippocampal global efficiency, shorter right hippocampal path length, and higher right hippocampal clustering coefficient compared to controls (P < 0.05). Higher right hippocampal global efficiency, shorter right hippocampal path length, and higher right hippocampal clustering coefficients were positively associated with greater headache frequency (P < 0.05). The hippocampus is largely involved in memory formation and retrieval, and this data provides additional support for previous findings demonstrating the importance of the hippocampus and pain memories for the chronification of 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 | 0.001 | 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) | 0.002 | 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".