P.114 When functional neuroimaging is ambiguous for language localization: a case for Wada testing
Bibliographic record
Abstract
Background: To localize cortical speech areas, methods such as fMRI are commonly used, but the Wada test can also determine whether a region is critical to the particular task. We report a case of a left-handed patient with a left frontal tumour in whom fMRI language paradigms produced both left and right Broca’s and Wernicke’s areas. Methods: All imaging used a 3 Tesla Siemens Skyra scanner. The patient performed five speech tasks: word reading, picture naming, semantic questions, pseudohomophone reading, and word generation. All preprocessing and statistical analyses for functional images were performed using Brain Voyager QX. Results: The fMRI results revealed right hemisphere dominance for language processing. A Wada test was performed in order to confirm whether the regions in the left hemisphere were critical to speech. The patient experienced speech arrest during the Wada test, thus confirming that despite bilateral speech activation, the left hemisphere speech regions are required for speech production. Conclusions: This case emphasizes the importance of preoperative fMRI in assessing the location of eloquent cortices adjacent to a tumour and the Wada test is still warranted for examining necessity of left hemisphere language regions when fMRI fails to show clear left-lateralization.
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".