Abstract 53: Reorganization of Functional Language Networks Following Neonatal Arterial Ischemic Stroke
Bibliographic record
Abstract
Introduction: Neonatal arterial ischemic stroke (NAIS) is the most common form of childhood stroke. Unlike adults with stroke, children with left middle cerebral artery (MCA) NAIS seldom become aphasic, although adolescence often reveals the emergence of higher-order language deficits. Functional language networks may be predictive of later-emerging language outcomes, and can aid to identify at-risk children with NAIS. Methods: Five neurotypical children (2F; 5 RH; mean 12.7 ± 2.6 years), and five children with unilateral-MCA NAIS (3F; 5 RH; mean 11.3 ± 2.0 years) listened to semantically correct and incorrect sentences while magnetoencephalography (MEG) was recorded. Task-related functional connectivity was calculated using the phase lag index (PLI) across regions of interest. The relationship between the functional brain networks and language skill was examined. Results: Neurotypical children showed increased global functional connectivity for semantically correct sentences 1.8 to 2.0 seconds from stimulus onset in the theta band (4-7 Hz; p <0.05). The top connections in the theta band and time window of interest involved a significantly greater number of nodes in the left frontal lobe for controls compared to patients ( p <0.05), while patients recruited a greater number of temporal lobe nodes than controls ( p <0.05). Furthermore, patient language networks demonstrated a more bilateral distribution than those of typically-developed children (37.5% vs. 30% of top connections). The mean connectivity strength in the language network was positively correlated with vocabulary skill (r=0.84) for patients, and with word reading ability for both patients and neurotypical children (r=0.95, r=0.88, respectively). Conclusions: These results suggest reorganization of expected unilateral and frontal language networks towards a bilateral and temporal distribution following stroke, with less reliance on traditional language nodes. Such reorganization may underlie the language ability trajectory of children with neonatal MCA stroke. Reorganization of functional brain networks may be used as a predictive marker for language development following neonatal stroke, which can ultimately guide precision medicine and improve long-term outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.003 | 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".