Disrupted network connectivity in pediatric brain tumor survivors is a signature of injury
Bibliographic record
Abstract
Cognition is compromised in pediatric brain tumor survivors but the neurophysiological basis of this compromise remains unclear. We hypothesized that reduced neural synchronization across brain networks is involved. To test this, we evaluated group differences using a retrospective cohort comparison design between 24 pediatric brain tumor survivors [11.81 ± 3.27)] and 24 age matched healthy children [12.04 ± 3.28)] in functional connectivity within a cerebellar network to examine local effects of the tumor, a whole brain network to examine diffuse effects of treatment (i.e., chemotherapy and radiation), and across multiple intrinsic connectivity networks. Neural activity was recorded during magnetoencephalography scanning while participants were at rest and functional connectivity within networks was measured using the phase lag index. We corroborated our findings using a computational model representing the local tumor effects on neural synchrony. Compared to healthy children, pediatric brain tumor survivors show increased functional connectivity for theta and beta frequency bands within the cerebellar network and increased functional connectivity for the theta band within the whole brain network that again localized to the cerebellum. Computational modeling showed that increased synchrony in the theta bad is observed following local clustering as well as sparse interarea brain connectivity. We also observed increased functional connectivity for the alpha frequency band in the ventral attention network and decreased functional connectivity within the gamma frequency band in the motor network within paedatric brain tumor survivors versus healthy children. Notably, increased gamma functional connectivity within the motor network predicted decreased reaction time on behavioral tasks in pediatric brain tumor survivors. Disrupted network synchrony may be a signature of neurological injury and disease.
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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) | 0.001 | 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".