Higher order neurocognition in pediatric brain tumor survivors: What can we learn from white matter microstructure?
Bibliographic record
Abstract
BACKGROUND: Pediatric brain tumor survivors (PBTS) experience neurocognitive late effects, including problems with working memory, processing speed, and other higher order skills. These skill domains are subserved by various white matter (WM) pathways, but not much is known about these brain-behavior links in PBTS. This study examined the anterior corona radiata (ACR), inferior fronto-occipital fasciculi (IFOF), and superior longitudinal fasciculi (SLF) by analyzing associations among diffusion metrics and neurocognition. PROCEDURE: Thirteen PBTS and 10 healthy controls (HC), aged 9-14 years, completed performance-based measures of processing speed and executive function, and parents rated their child's day-to-day executive skills. Children underwent magnetic resonance imaging (MRI) with diffusion weighted imaging that yielded fractional anisotropy (FA) and mean diffusivity (MD) values. Independent samples t-tests assessed group differences on neurocognitive and imaging measures, and pooled within-group correlations examined relationships among measures across groups. RESULTS: PBTS performed more poorly than HC on measures of processing speed, divided attention, and shifting (d = -1.08 to -1.44). WM microstructure differences were significant in MD values for the bilateral SLF and ACR, with PBTS showing higher diffusivity (d = 0.75 to 1.21). Better processing speed, divided attention, and shifting were associated with lower diffusivity in the IFOF, SLF, and ACR, but were not strongly correlated with FA. CONCLUSIONS: PBTS demonstrate poorer neurocognitive functioning that is linked to differences in WM microstructure, as evidenced by higher diffusivity in the ACR, SLF, and IFOF. These findings support the use of MD in understanding alterations in WM microstructure in PTBS and shed light on potential functions of these pathways.
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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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".