Cerebellar language mapping and cerebral language dominance in pediatric epilepsy surgery patients
Bibliographic record
Abstract
OBJECTIVE: Children with epilepsy often have reorganization of language networks and abnormal brain anatomy, making determination of language lateralization difficult. We characterized the proportion and distribution of language task activation in the cerebellum to determine the relationship to cerebral language lateralization. METHODS: Forty-six pediatric epilepsy surgery candidates (aged 7-19 years) completed an fMRI auditory semantic decision language task. Distribution of activated voxels and language laterality indices were computed using: (a) Broca's and Wernicke's areas and their right cerebral homologues; and (b) left and right cerebellar hemispheres. Language task activation was anatomically localized in the cerebellum. RESULTS: Lateralized language task activation in either cerebral hemisphere was highly correlated with lateralized language task activation in the contralateral cerebellar hemisphere (Broca vs. cerebellar: ρ = -0.54, p < 0.01). Cerebellar language activation was located within Crus I/II, areas previously implicated in non-motor functional networks. CONCLUSIONS: Cerebellar language activation occurs in homologous regions of Crus I/II contralateral to cerebral language activation in patients with both right and left cerebral language dominance. Cerebellar language laterality could contribute to comprehensive pre-operative evaluation of language lateralization in pediatric epilepsy surgery patients. Our data suggest that patients with atypical cerebellar language activation are at risk for having atypical cerebral language organization.
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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 | 0.000 | 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".