P.064 Preoperative mapping using fMRI and DTI: a multimodal approach to assessing language dominance
Bibliographic record
Abstract
Background: Language mapping is a key goal in neurosurgical planning. With the discontinuation of the Wada test in Canada, neurosurgeons often rely on fMRI and intraoperative techniques for determining language lateralization. Recent studies have also evaluated the utility of diffusion tensor imaging (DTI) for preoperative language lateralization, but further research is needed to confirm its efficacy. We report a patient with a left frontal AVM. fMRI and DTI was used to localize language and motor functioning. Methods: The tasks included word reading, picture naming, pseudohomophones (e.g., dawg) and semantic questions. All fMRI analyses were performed using BrainVoyager. Tensors were tracked from 30-direction diffusion MR images using DSI-Studio. Results: The fMRI results revealed consistent Broca’s and Wernicke’s areas, confirming left hemisphere dominance. There was also a region of activation in the precentral gyrus near the surgical resection. The results were loaded onto the neuronavigation system to help determine safe surgical margins. The DTI results revealed that the left arcuate and uncinate -fasciculus had three times more tracts than the right hemisphere, further supporting left hemisphere dominance. Conclusions: This case highlights the value of a combined, multimodal approach for preoperative language localization, which will further enhance surgical safety by helping preserve regions for essential brain functions.
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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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".