Language Mapping With rTMS in Healthy Pediatric Patients
Bibliographic record
Abstract
PURPOSE: Repetitive transcranial magnetic stimulation (rTMS) is a potentially effective, noninvasive tool for language mapping. However, there is a paucity of data in pediatric patients. In this study, we aimed to map language sites in healthy pediatric participants with navigated rTMS. METHODS: Children aged 5 to 18 years underwent bilateral language mapping. Stimulation was delivered at 5 Hz during visual-naming and auditory verb-generation tasks in 1 to 2 second bursts. We targeted 33 standardized sites per hemisphere. In total, 34 participants completed the visual-naming task, and 27 participants completed the verb-generation task. Lateralization index (LI) and Wilcoxon signed-rank test were used to assess language lateralization. A difference of least squares means model was developed to determine the prevalence of visual-naming and verb-generation errors within lobar and hemispheric regions. RESULTS: Weak left lateralization was observed for visual naming (LI 0.14; p = 0.038), and no lateralization was observed for verb generation (LI 0.08; p = 0.269). Using multiple least squares regression, left hemisphere errors were more likely to occur than right hemisphere errors for visual naming (OR 1.23; 95% CI 1.06-1.44), but no lateralization effect was observed for verb-generation errors (OR 1.11; 95% CI 0.93-1.27). CONCLUSIONS: rTMS is likely to identify bilateral or weakly left-lateralized language sites in pediatric patients during language tasks. Although rTMS can be a useful noninvasive method for identifying potential language-positive sites, our results in healthy controls suggest that it cannot be used as a singular method for language mapping in the preoperative setting.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".