Pilot Study of Associations Among Functional Connectivity and Neurocognition in Survivors of Pediatric Brain Tumor and Healthy Peers
Bibliographic record
Abstract
This pilot study examined the associations among functional connectivity in the salience, central executive, and default mode networks, and neurocognition in pediatric brain tumor survivors and healthy children. Thirteen pediatric brain tumor survivors (9 boys, M = 12.76 years) and 10 healthy children (6 boys, M = 12.70 years) completed magnetic resonance imaging (MRI) and assessment of processing speed and executive function. Pediatric brain tumor survivors performed more poorly than healthy children on measures of processing speed, divided attention, and working memory; parent ratings of day-to-day executive function did not differ significantly by group, though both pediatric brain tumor survivors who underwent only surgical resection and healthy children were rated by parents as having difficulties approaching a standard deviation above the normative mean. Connectivity was lower in the salience network and greater in the default mode network in pediatric brain tumor survivors. Cross-method correlations showed that increased salience network and default mode network connectivity were associated with better task performance and parent-rated executive skills and higher central executive network connectivity with poorer parent-rated executive skills. This perhaps reflects an adaptive pattern of hyperconnectivity in pediatric brain tumor survivors.
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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 | 0.002 |
| 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".