Three-dimensionally Reconstructed Magnetic Source Imaging and Neuronavigation in Pediatric Epilepsy: Technical Note
Bibliographic record
Abstract
OBJECTIVE: To determine the role of reconstructing three-dimensional magnetic source imaging (MSI) data on cortical resections for children undergoing epilepsy surgery using neuronavigation. METHODS: Magnetoencephalographic recordings were analyzed in 16 children under 18 years of age with intractable epilepsy. The data were transferred to the neuronavigation workstation for intraoperative localization of MSI spike sources in selected patients. With the aid of neuronavigation, the MSI spike sources were resected. Intraoperative electrocorticography was then used to survey the surrounding field for residual epileptiform activity. RESULTS: MSI spike sources were obtained in 13 of 16 patients. MSI spike sources localized the cortical and subcortical discharges before intraoperative electrocorticography in nine patients and before extraoperative subdural grid electroencephalographic monitoring in four patients. The localization of MSI spikes sources was characterized by clustered spike sources in 10 patients. By use of neuronavigation, the clustered spike sources were correlated to the interictal zone indicated by intraoperative electrocorticography in six patients and to the ictal onset zone shown on extraoperative subdural grid electroencephalography in three patients. Cortical excision of the spike cluster focus was then performed in these six patients. The technique used here to resect MSI spike source clusters that correlate with the ictal onset zone by invasive subdural grid monitoring is illustrated in one patient who underwent cortical resection for epilepsy surgery. CONCLUSION: Three-dimensional reconstruction of MSI data linked to neuronavigation is a promising technique to facilitate resections around eloquent cortex in children with epilepsy.
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 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".