Language Representation Following Left MCA Stroke in Children and Adults: An fMRI Study
Bibliographic record
Abstract
BACKGROUND: In this case series, functional magnetic resonance imaging was used to examine brain networks that mediate different aspects of language function in 4 young adults (17-22 years) with a history of left middle cerebral artery (MCA) stroke in childhood (40 years of age). Although it is widely believed that altered lateralization patterns are more likely to occur following early brain injuries compared with later brain injuries, the presumed plasticity of the young brain has been challenged in recent years, particularly in the domain of language. METHODS: We explored this issue by contrasting the brain activation patterns of individuals with childhood left MCA stroke and adult left MCA stroke while performing two language tasks: verb generation and picture-word matching. Importantly, both groups showed significant recovery of language function, based on standard clinical indicators. RESULTS: Controls showed left lateralized activation for both tasks, although much more pronounced for verb generation. Adult stroke patients also showed left lateralization for both tasks, though somewhat weaker than controls. Childhood stroke patients exhibited significantly weaker lateralization than the adult group for verb generation, but there was no significant group difference for picture-word matching. CONCLUSIONS: These preliminary findings suggest that successful reorganization of language function is more likely to involve bilateral recruitment following left MCA stroke in childhood than in adulthood. Of importance, although childhood stroke patients had primarily subcortical lesions, there were substantial alterations in cortical activation patterns.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".