Investigating the Associations Between Brain Network Functional Connectivity and Health-Related Quality of Life Following a Pediatric Concussion
Bibliographic record
Abstract
Concussions negatively affect the health-related quality of life (HRQoL) of children and youth for months post-injury.In addition, connectivity within and between the default mode network (DMN), central executive network (CEN) and salience network (SN) has been shown to be altered post-concussion.Few studies have investigated connectivity within and between these 3 networks following a pediatric concussion and none have assessed its associations with HRQoL.The present study explored whether within and between-network functional connectivity (FC) differs between a pediatric concussion and orthopedic injury (OI) group aged 10-18.In the concussion group, associations between FC of these networks and HRQoL 4 weeks post-injury were also assessed.Participants underwent a resting-state functional magnetic resonance imaging (rs-fMRI) scan and HRQoL was measured with the Pediatric Quality of Life Inventory (PedsQL) at 4 weeks post-injury.One-way ANCOVA analyses were conducted between groups with the seed-based FC of the 3 networks.Multivariate linear regressions were conducted to assess the association between connectivity of the 3 networks and HRQoL.A total of 55/72 concussion and 27/30 OI participants were included in the analyses.Increased within-network FC of the CEN and SN, increased between-network FC of the DMN-SN and CEN-SN, and decreased betweennetwork FC of the DMN-CEN was found in the concussion group when compared to the OI group.No significant associations were found between HRQoL and FC within and between the DMN, CEN and SN 4 weeks after concussion.When compared to OI, differential connectivity patterns are present following a pediatric concussion at 4-weeks post-injury, however, these network differences are not associated with HRQoL.
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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 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".