Dynamic neuroplasticity of language networks: The intersection of bilingualism and epilepsy
Bibliographic record
Abstract
Are bilingual language networks flexible enough to dynamically adapt to neurological insult? We examined language lateralization in 24 bilingual and 46 monolingual adults with temporal lobe epilepsy using functional MRI. In a group of primarily early sequential bilingual patients, the first acquired language (L1) showed more bilateral lateralization than in monolingual patients, with no effect of seizure onset laterality. In contrast, the second-acquired language (L2) was more bilateral in the presence of left hemisphere epilepsy and more left-lateralized in right hemisphere epilepsy. Most notably, in left hemisphere epilepsy, seizure onset closer to L2 acquisition was associated with more right-lateralized L2 representation. These findings suggest a compensatory process in which L2 networks strengthen in the hemisphere opposite the seizure focus, potentially reflecting neural adaptation in early bilingualism. Conversely, L1 appears to have less dynamic reorganization in response to neurological insult. Together, these findings highlight the importance of timing in both language experience and neurological stress in shaping language network organization. They support the view that the bilingual brain is not simply the sum of two monolingual systems, but a dynamic and unique system marked by high interindividual variability, in which divergence between languages may emerge under certain experience- and context-dependent conditions.
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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) | 0.001 | 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".