PRESURGICAL LOCALIZATION OF PRIMARY MOTOR CORTEX IN PEDIATRIC PATIENTS WITH BRAIN LESIONS BY THE USE OF SPATIALLY FILTERED MAGNETOENCEPHALOGRAPHY
Bibliographic record
Abstract
OBJECTIVE: The objective of this study was to confirm the efficacy of spatially filtered magnetoencephalography for the preoperative localization of primary motor cortex in pediatric patients with focal lesions in the region of the sensorimotor cortex. METHODS: We recorded movement-related magnetoencephalographic activity in 10 pediatric patients (age range, 7-18 years; mean age, 12.5 years) undergoing presurgical evaluation for focal brain lesion resection. Participants made transient movements of the right and left index finger in response to a visual cue. The premovement motor field component in the averaged brain response was localized with a newly developed beamformer spatial filter algorithm. Cortical mapping of motor cortex intraoperatively was conducted in 5 of the 10 patients. RESULTS: The motor field time-locked to electromyography onset was successfully localized to cortical areas corresponding to the hand region primary motor cortex in 95% of cases (9 of 10 from nonlesional hemisphere; 10 of 10 from lesional hemisphere). Intraoperative electrocortical stimulation activated the expected muscles at motor field coregistered cortical source locations in all cases tested (n = 5). Using these methods, we also found that displacement of the sensorimotor cortex by space-occupying tumors did not interfere with the localization of motor cortex. CONCLUSION: We conclude that noninvasive localization of the primary motor cortex can be reliably performed by using spatially filtered magnetoencephalography techniques, which provide a robust and accurate measurement of motor cortical function for the purpose of surgical guidance.
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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 | 0.000 | 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) | 0.001 | 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 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".