fMRI for language: how can it replace the Wada test?
Bibliographic record
Abstract
Background: The goal of our project is to assess the feasibility of replacing the invasive Wada test considered as the gold standard with non-invasive fMRI test for assessment of language dominance preoperatively. Methods: fMRI test with three language paradigm tasks (verb generation, sentence completion and naming) were conducted on our cohort of patients. fMRI laterality indices (LI) were then defined as a ratio (L-R)/(L+R) between the number of activated voxels in the left and right ROIs for Anterior Language Area (ALA) and Posterior Language Area (PLA). fMRI results were divided into the right (LI < -0.2), left (LI > 0.2) or bilateral (-0.2 < LI <0.2) hemispheric language dominance and compared to the results of the Wada test. Results: 28 patients were studied. The concordance rate between Wada and fMRI tests for the ALA and PLA was 68.2% and 52.2% for sentence completion; 56% and 52% for verb generation and 25% and 35% for naming paradigm, respectively. Conclusions: Sentence completion and verb generation fMRI paradigms showed higher concordance with Wada test than naming paradigm. The higher discordance between the Wada test and fMRI was related to bilateral results suggestive of less stringent thresholds used for either test.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".