Associations Between Changes in Psychological Resilience and Resting-State Functional Connectivity Throughout Pediatric Concussion Recovery
Bibliographic record
Abstract
Abstract Purpose This study investigated the association between psychological resilience and resting-state network functional connectivity in pediatric concussion. Methods This was a substudy of a randomized controlled trial, recruiting children with concussion and orthopedic injury. Participants completed the Connor-Davidson Resilience 10 Scale and underwent magnetic resonance imaging at 72 hours and 4-weeks post-injury. Seed-to-voxel analyses were used to explore associations between resilience and connectivity with the default-mode, central executive, and salience networks longitudinally and at both timepoints separately. Regions-of-interest analyses were used to explore associations between resilience and within-network connectivity. Results A total of 69 children with a concussion (median age = 12.81 [IQR: 11.79–14.36]; 46% female) and 30 with orthopedic injury (median age = 12.27 [IQR: 11.19–13.94]; 40% female) were included. Seed-to-voxel analyses detected a positive correlation between 72-hour resilience and central executive network connectivity in the concussion group, and a positive correlation between 72-hour resilience and salience network connectivity in the orthopedic injury group. Group was a moderator of 72-hour resilience and salience network connectivity, and a moderator of longitudinal resilience and default-mode network connectivity. Regions-of-interest analyses identified group as a moderator of longitudinal resilience and within-default-mode network connectivity. In the orthopedic injury group, longitudinal resilience was associated with within-default-mode network connectivity, while 72-hour resilience was associated with within-salience network connectivity. Conclusions These results suggest that resilience may be implicated in functional neuroimaging outcomes in pediatric concussion and should further be investigated for its clinical utility as a protective or restorative factor following injury.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".