Amygdalar Functional Connectivity Differences Associated With Reduced Pain Intensity in Pediatric Peripheral Neuropathic Pain
Bibliographic record
Abstract
Background There is evidence of altered corticolimbic circuitry in adults with chronic pain, but relatively little is known of functional brain mechanisms in adolescents with neuropathic pain (NeuP). Pediatric NeuP is etiologically and phenotypically different from NeuP in adults, highlighting the need for pediatric-focused research. The amygdala is a key limbic region with important roles in the emotional-affective dimension of pain and in pain modulation. Objective To investigate amygdalar resting state functional connectivity (rsFC) in adolescents with NeuP. Methods This cross-sectional observational cohort study compared resting state functional MRI scans in adolescents aged 11–18 years with clinical features of chronic peripheral NeuP (n = 17), recruited from a tertiary clinic, relative to healthy adolescents (n = 17). We performed seed-to-voxel whole-brain rsFC analysis of the bilateral amygdalae. Next, we performed post hoc exploratory correlations with clinical variables to further explain rsFC differences. Results Adolescents with NeuP had stronger negative rsFC between right amygdala and right dorsolateral prefrontal cortex (dlPFC) and stronger positive rsFC between right amygdala and left angular gyrus (AG), compared to controls (PFDR<0.025). Furthermore, lower pain intensity correlated with stronger negative amygdala-dlPFC rsFC in males (r = 0.67, P = 0.034, n = 10), and with stronger positive amygdala-AG rsFC in females (r = −0.90, P = 0.006, n = 7). These amygdalar rsFC differences may thus be pain inhibitory. Conclusions Consistent with the considerable affective and cognitive factors reported in a larger cohort, there are rsFC differences in limbic pain modulatory circuits in adolescents with NeuP. Findings also highlight the need for assessing sex-dependent brain mechanisms in future studies, where possible.
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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 | 0.000 |
| 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".