Predictors of atypical language lateralization in focal epilepsy: A mega‐analysis of <scp>fMRI</scp> evidence
Bibliographic record
Abstract
OBJECTIVE: To identify predictors of language lateralization derived from functional magnetic resonance imaging (fMRI) in children and adults with left- and right-sided focal epilepsy. METHODS: We conducted a mega-analysis of data from 914 individuals from 24 samples. We used multilevel models to identify predictors of language lateralization in left and right hemisphere epilepsy groups. We assigned each participant a clinical predictor score to explore whether there was a cumulative influence of predictors on increasing atypical language lateralization. RESULTS: Left hemisphere epilepsy was a predictor of greater atypical language lateralization in the combined sample. Additional predictors of atypical language lateralization included left/ambidextrous handedness in both the left and right hemisphere groups, and longer duration of epilepsy, frontal lobe involvement, and history of a stroke or other precipitating injury in the left hemisphere group only. There was a cumulative effect of predictors in the left hemisphere groups. Eighty percent of individuals with four or more predictors had atypical language lateralization, compared to 19% of individuals with no predictors, other than left hemisphere epilepsy. SIGNIFICANCE: Consistent with theories of language plasticity, we demonstrated a robust effect of early acquired left hemisphere injury on language lateralization. There was also a subtle effect of duration of epilepsy, perhaps reflecting increasing bilaterality with age in adulthood. The association between left/ambidextrous handedness and atypical language lateralization in the left and right hemisphere groups likely reflects both genetic and epilepsy-associated effects. The total number of predictors identified for an individual could serve as an indication for presurgical language fMRI, when surgical management is considered.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".