Brain Network Connectivity and Executive Function in Children with Infantile Hydrocephalus
Bibliographic record
Abstract
Introduction: Infantile hydrocephalus (HCP) is a condition in which there is an abnormal buildup of cerebrospinal fluid in the ventricles within the first few months of life, which puts pressure on surrounding brain tissues. Compression of the developing brain increases the risk of secondary brain injury and cognitive disabilities. Methods: In this study, we used diffusion-weighted imaging and resting-state functional magnetic resonance imaging to investigate the effects of ventricle dilatation on structural and functional brain networks in children with shunted infantile HCP and examined how these brain changes may impact executive function. Results: We found that children with HCP have altered structural and functional connectivity between and within large-scale networks. Moreover, hyperconnectivity between the ventral attention and default mode network in children with HCP correlated with reduced executive function scores. Compared with typically developing age-matched control participants, our patient population also had lower fractional anisotropy in posterior white matter. Discussion: Overall, these findings suggest that infantile HCP has long-term effects on brain network connectivity, white matter development, and executive function in children at school age. Future work will examine the relationship between ventricular volumes before shunt placement in infancy and brain network development throughout childhood.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".