Abstract TMP114: Functional and Structural Network Reorganization in Higher-Order Language Following Neonatal Stroke
Bibliographic record
Abstract
Introduction: Neonatal arterial ischemic stroke (NAIS) is a common form of pediatric stroke often affecting classical language areas in the brain’s left hemisphere. While children with NAIS can acquire basic language skills, adolescence typically reveals the emergence of deficits in higher-order language, such as syntactic language. The reorganization of functional and structural brain networks may provide insight into later-emerging language outcomes and serve as a biomarker in prognostication. Methods: A cross-sectional study of eight children with unilateral NAIS (5F; 12.3±3.3 years) and seven neurotypical children (2F; 13.4±2.7 years) was conducted. Participants listened to syntactically correct and incorrect sentences while magnetoencephalography was recorded, and task-related functional connectivity in the time window and frequency band of interest was determined. Structural connectivity between brain regions was investigated using DTI tractography, and language outcomes were assessed using neuropsychological tests. Results: An analysis of the syntactic language network (4-7 Hz, 1.2-1.4s) indicated that unlike the typical correlation between left-lateralized functional connectivity and language skill ( p <0.01), good outcome in patients is correlated with bilateral frontal connectivity (p<0.01). Furthermore, patients exhibit a significant reduction in structural connectivity between the left and right supplementary motor area, compared with controls ( p =0.007), and the bilateral structural connectivity of this region is positively correlated with measures of working memory and information processing ( p =0.036). Conclusions: The preliminary results suggest that reorganization of functional networks towards bilateral connectivity may support language outcome following early stroke. The supplementary motor area’s role in coordination of interhemispheric functions and in information processing may position it as a key structural region in supporting the compensatory reorganization of functional networks underlying language. Ultimately, measures of functional and structural networks may be used as a prognostic tool for language development in pediatric stroke in order to improve long-term outcomes.
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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 | 0.000 | 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) | 0.004 | 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".