Characterizing Topological and Topographical Resilience in Structural Networks Supporting Language in Childhood
Bibliographic record
Abstract
Language networks undergo robust changes through childhood. Language is first supported by an extensive network spanning both hemispheres and becomes focal and left-lateralized through development. Here, we assessed language network resilience using diffusion imaging and in silico attacks. We acquired multi-shell diffusion-weighted images of 50 typically-developing children, ages 5-18 years. Automated parcellation was combined with constrained-spherical deconvolution and anatomically-constrained tractography to estimate connectomes. We assayed network resiliency using multiple targeted attacks. Interestingly, resiliency increased with age (p < 0.05); however, spatial extent of nodes removed prior to failure decreased with age (p < 0.05). Also, left-lateralization of nodes increased with age (p < 0.05). From a wiring perspective, adolescents’ language networks are more resilient than those of children. However, critical hubs are more diffuse in younger children, and become increasingly left-lateralized and focal with age. Findings suggest that diffuse representation underlies the paediatric advantage for language outcomes following (focal) injury.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".