Abstract TMP103: Neuroplasticity of Functional Connectivity in Language Networks in Children After Perinatal Stroke
Bibliographic record
Abstract
Background: Acquisition of language during development is imperative for lifelong functioning. Perinatal stroke is an ideal human model of developmental neuroplasticity. Many children with perinatal stroke (arterial ischemic stroke [AIS] or periventricular venous infarction [PVI]) have intact language function despite damage to language areas. We examined the strength of functional connectivity of language networks in children with perinatal stroke. Methods: Participants were recruited from a population-based perinatal stroke cohort and compared to right-handed typically developing controls (TDC). All were scanned at rest using a 3T GE MRI (36 slices, 3.6mm isotropic, repetition/echo time=2000/30ms, 150 volumes, ~6:00). Language networks were identified using a seed based technique measuring blood oxygen level dependent responses in bilateral inferior frontal gyrus (IFG) and posterior superior temporal gyrus (pSTG). Seed-to-seed temporal correlations quantified connectivity. Standardized language outcomes for a subset included measures of vocabulary (WISC-IV Vocabulary) and fluency (NEPSY-II Word Generation Initial Letter). Results: The population was 68 children aged 6-19 (17 AIS [mean age 14.0±4.1], 15 PVI [12.8±4.0], 36 TDC [12.9±3.6]). Seven of 13 stroke children (54%) scored below the 10 th percentile on the word generation task. TDC showed stronger interhemispheric connectivity between frontal (LIFG-RIFG r=0.82±0.3) and temporal (LpSTG-RpSTG r=0.79±0.2) areas compared to intrahemispheric (LIFG-LpSTG r=0.46±0.2; RIFG-RpSTG r=0.39±0.3). For AIS, interhemispheric connectivity between left and right IFG was lower than TDC regardless of stroke side [p<0.001]. For AIS with a left lesion, intrahemispheric connectivity in the right hemisphere appeared higher than TDC [p=0.051]. Connectivity for PVI participants was comparable to TDC. Neither intra- nor interhemispheric connectivity appeared to relate to language function in this simple network. Conclusions: Functional strength of language networks is altered after AIS but not PVI. Connectivity of larger language networks needs further investigation to explore compensatory mechanisms and may help target language rehabilitation.
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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".