Corticostriatal Structural Connectivity and Integrity in Children with Hydrocephalus and Executive Dysfunction
Bibliographic record
Abstract
Abstract Purpose Infantile hydrocephalus causes diffuse neuronal injury and periventricular white matter damage and is a common neurosurgical indication to limit adverse neurodevelopmental outcomes. Higher-order executive functions which develop throughout childhood and adolescence are not currently well characterized in surgically managed patients. Methods This case-control diffusion-weighted imaging study measured corticostriatal structural connectivity and integrity in school-aged children with ventriculoperitoneal (VP) shunted infantile hydrocephalus and age-matched controls. Probabilistic tractography and diffusion tensor metrics (such as fractional anisotropy, and mean, radial and axial diffusivity) were used to assess microstructural alterations in the executive corticostriatal network. A bootstrapped correlational analysis was used to identify significant dissimilarity between patient structural networks and the mean control network. The Behaviour Rating Inventory for Executive Functions (BRIEF2), the gold standard for assessing clinically elevated and ecologically valid executive dysfunction in children, was used to obtain three domain measures and one global measure of executive function. Results Patients with hydrocephalus showed significant differences of white matter connectivity and integrity within the executive corticostriatal network when compared to the healthy controls. Patients with higher global executive dysfunction demonstrated lower similarity of fractional anisotropy within the executive corticostriatal network when compared to the control network, indicating decreased white matter integrity. Conclusion The current study demonstrates patient structural deviation from a control network and highlights atypical development of corticostriatal white matter and related executive dysfunction in children with VP shunted hydrocephalus.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".