Noninvasive Language Mapping in Patients With Epilepsy or Brain Tumors
Bibliographic record
Abstract
BACKGROUND: Functional magnetic resonance imaging (fMRI) has become part of routine brain mapping in patients with epilepsy or tumor undergoing resective surgery. However, robust localization of crucial functional areas is required. OBJECTIVE: To establish a simple, short fMRI task that reliably localizes crucial language areas in individual patients who undergo respective surgery. METHODS: fMRI was measured during an 8-minute auditory semantic decision task in 28 healthy controls and 35 consecutive patients who had focal epilepsy or a brain tumor. Nineteen underwent resective surgery. Group and individual analyses were performed. Results in patients were compared with postsurgical language outcome and electrocortical stimulation when available. RESULTS: fMRI activations concordant with the anterior and posterior language areas were found in 96% and 89% of the controls, respectively. The anterior and posterior language areas were both activated in 93% of the patients. These results were concordant with electrocortical stimulation results in 5 patients. Transient postsurgical language deficits were found in 2 patients in whom surgery was performed in the vicinity of the fMRI activations or who had postsurgical complications implicating areas of fMRI activations. CONCLUSION: The proposed fast fMRI language protocol reliably localized the most relevant language areas in individual subjects. It appears to be a valuable complementary tool for surgical planning of epileptogenic foci and of brain tumors. ABBREVIATIONS: ECS, electrocortical stimulation FLI, frontal lateralization index fMRI, functional magnetic resonance imaging FWE, family-wise error LI, lateralization index MNI, Montreal Neurological Institute SD, standard deviation SEM, standard error of the mean TPLI, temporoparietal lateralization index
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 0.000 |
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 teacher head, 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".