Interhemispheric Functional Connectivity following Prenatal or Perinatal Brain Injury Predicts Receptive Language Outcome
Bibliographic record
Abstract
Early brain injury alters both structural and functional connectivity between the cerebral hemispheres. Despite increasing knowledge on the individual hemispheric contributions to recovery from such injury, we know very little about how their interactions affect this process. In the present study, we related interhemispheric structural and functional connectivity to receptive language outcome following early left hemisphere stroke. We used functional magnetic resonance imaging to study 14 people with neonatal brain injury, and 25 age-matched controls during passive story comprehension. With respect to structural connectivity, we found that increased volume of the corpus callosum predicted good receptive language outcome, but that this is not specific to people with injury. In contrast, we found that increased posterior superior temporal gyrus interhemispheric functional connectivity during story comprehension predicted better receptive language performance in people with early brain injury, but worse performance in typical controls. This suggests that interhemispheric functional connectivity is one potential compensatory mechanism following early injury. Further, this pattern of results suggests refinement of the prevailing notion that better language outcome following early left hemisphere injury relies on the contribution of the contralesional hemisphere (i.e., the "right-hemisphere-take-over" theory). This pattern of results was also regionally specific; connectivity of the angular gyrus predicted poorer performance in both groups, independent of brain injury. These results present a complex picture of recovery, and in some cases, such recovery relies on increased cooperation between the injured hemisphere and homologous regions in the contralesional hemisphere, but in other cases, the opposite appears to hold.
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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) | 0.001 | 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".