NI-59 * TABLET TECHNOLOGY FOR IMPROVED PREOPERATIVE SPEECH MAPPING USING FUNCTIONAL MRI IN PATIENTS WITH LOW-GRADE GLIOMA
Bibliographic record
Abstract
INTRODUCTION AND METHODS: The survival benefit afforded by surgical resection for patients with low-grade gliomas (LGG) is becoming increasingly clear. Preoperative functional mapping with functional MRI and awake craniotomy with intraoperative mapping of speech and motor function have improved the safety and efficacy of surgery for LGG. We sought to expand the ability of preoperative fMRI to identify centers of functional eloquence by utilizing more sophisticated functional testing paradigms through the design of an MRI-compatible electronic tablet, which enables us to perform advanced language and motor testing during fMRI. RESULTS: Group analysis of healthy subjects produced activation in predicted locations (Broca's area for rhyming, Broca and Wernike's area for semantic decisions and the superior parietal lobule for word copying). Imaging results for the brain tumor patient showed bilateral language activation with active areas touching the tumor boundary. Conjunction analysis performed between rhyming and word copying tasks was found to substantially increase the specificity of the activation maps while maintaining the same sensitivity when compared with direct cortical simulation. CONCLUSIONS: Our preliminary studies demonstrate that functional data for speech mapping generated by use of the tablet during fMRI is replicable at surgery, with an increased sensitivity without loss of specificty for speech cortex mapping. We have begun to identify advanced language testing paradigms for its further development.
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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 | 0.000 |
| 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) | 0.004 | 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".