P.066 Hemispheric Reorganization of Functional Language Networks Following Neonatal Stroke Supports Language Outcome
Bibliographic record
Abstract
Background: Neonatal Arterial Ischemic Stroke (NAIS) is a common form of paediatric stroke often affecting classical language areas. The post-stroke reorganization of functional language networks may provide insight into later-emerging language deficits and may help to identify at-risk children with NAIS. Methods: A cross-sectional study of fourteen children with left (n=8; 2M; 11.1±2.2 years) or right (n=6; 3M; 12.4±4 years) middle cerebral artery (MCA) NAIS, as well as seven neurotypical children (5M; 13.4±2.7 years), was conducted. Children listened to correct/incorrect syntactic sentences while MEG was recorded, and task-related functional connectivity in the time window and frequency band of interest was determined. Language outcomes were assessed using a battery of neuropsychological tests. Results: A network-based analysis of syntactic language processing (4-7 Hz, 1.2-1.4s) revealed a dysfunctional bilateral frontal-temporal network involving language areas in patients (p=0.01). Patients with right-MCA stroke exhibited a positive correlation between left hemispheric connectivity and measures of language skill (p<0.01), resembling the neurotypical children. In left-MCA stroke patients, greater bilateral connectivity or right laterality in the language network is correlated with good outcome (p<0.05). Conclusions: Depending on the hemispheric location of stroke, certain patterns of language network reorganization may account for impairments in a bilateral frontal-temporal language subnetwork and support language outcome.
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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 | 0.000 | 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) | 0.005 | 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".