Atypical language localization in right temporal lobe epilepsy: An fMRI case report
Bibliographic record
Abstract
We report a 41- year-old, left-handed patient with drug-resistant right temporal lobe epilepsy (TLE). Presurgical fMRI was conducted to examine whether the patient had language functioning in the right hemisphere given that left-handedness is associated with a higher prevalence of right hemisphere dominance for language. The fMRI results revealed bilateral activation in Broca's and Wernicke's areas and activation of eloquent cortex near the region of planned resection in the right temporal lobe. Due to right temporal language-related activation, the patient underwent an awake right-sided temporal lobectomy with intraoperative language mapping. Intraoperative direct cortical stimulation (DCS) was conducted in the regions corresponding to the fMRI activation, and the patient showed language abnormalities, such as paraphasic errors, and speech arrest. The decision was made to abort the planned anterior temporal lobe procedure, and the patient instead underwent a selective amygdalohippocampectomy via the Sylvian fissure at a later date. Post-operatively the patient was seizure-free with no neurological deficits. Taken together, the results support previous findings of right hemisphere language activation in left-handed individuals, and should be considered in cases in which presurgical localization is conducted for left-hand dominant patients undergoing neurosurgical procedures.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".