Functional connectivity of language networks after perinatal stroke
Bibliographic record
Abstract
Successful language acquisition during development is imperative for lifelong function. Complex language networks develop throughout childhood. Perinatal stroke may cause significant language disabilities but function can also be remarkably normal. Studying such very early brain injury populations may inform developmental plasticity models of language networks. We examined functional connectivity (FC) of language networks in children with arterial and venous perinatal stroke and typically developing controls (TDC) in a population-based, controlled, cohort study. Resting state functional MRI was performed at 3 T (TR/TE = 2000/30 ms, 150 volumes, 3.6mm 3 voxels). Seed-based analyses used bilateral inferior frontal and superior temporal gyri. A subset of stroke participants completed clinical language testing. Sixty-six children participated (median age: 12.85±3.8y, range 6–19; arterial N = 17; venous N = 15; TDC N = 34]. Children with left hemisphere strokes had comparable FC in their right hemispheres compared to TDC. Inter- and intra-hemispheric connectivity strengths were similar between TDC and PVI but lower for AIS. Reduced FC was associated with poorer language comprehension. Language networks can be estimated using resting-state fMRI in children with perinatal stroke. Altered connectivity may occur in both hemispheres, is more pronounced with arterial lesions, and is associated with clinical function. Our results have implications for therapeutic language interventions after early stroke.
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 | 0.001 | 0.001 |
| 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) | 0.002 | 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".