Localization of epileptic foci in children with intractable epilepsy secondary to multiple cortical tubers by using synthetic aperture magnetometry kurtosis
Bibliographic record
Abstract
OBJECT: Magnetoencephalography (MEG) has been typically used to localize epileptic activity by modeling interictal activity as equivalent current dipoles (ECDs). Synthetic aperture magnetometry (SAM) is a recently developed adaptive spatial filtering algorithm for MEG that provides some advantages over the ECD approach. The SAM-kurtosis algorithm (also known as SAM[g2]) additionally provides automated temporal detection of spike sources by using excess kurtosis value (steepness of epileptic spike on virtual sensors). To evaluate the efficacy of the SAM(g2) method, the authors applied it to readings obtained in children with intractable epilepsy secondary to tuberous sclerosis complex (TSC), and compared them to localizations obtained with ECDs. METHODS: The authors studied 13 children with TSC (7 girls) whose ages ranged from 13 months to 16.3 years (mean 7.3 years). Video electroencephalography, MR imaging, and MEG studies were analyzed. A single ECD model was applied to localize ECD clusters. The SAM(g2) value was calculated at each SAM(g2) virtual voxel in the patient's MR imaging-defined brain volume. The authors defined the epileptic voxels of SAM(g2) (evSAM[g2]) as those with local peak kurtosis values higher than half of the maximum. A clustering of ECDs had to contain > or = 6 ECDs within 1 cm of each other, and a grouping of evSAM(g2)s had to contain > or = 3 evSAM(g2)s within 1 cm of each other. The authors then compared both ECD clusters and evSAM(g2) groups with the resection area and correlated these data with seizure outcome. RESULTS: Seizures started when patients were between 6 weeks and 8 years of age (median 6 months), and became intractable secondary to multiple tubers in all cases. Ictal onset on scalp video electroencephalography was lateralized in 8 patients (62%). The MEG studies showed multiple ECD clusters in 7 patients (54%). The SAM(g2) method showed multiple groups of epileptic voxels in 8 patients (62%). Colocalization of grouped evSAM(g2) with ECD clusters ranged from 20 to 100%, with a mean of 82%. Eight patients underwent resection of single (1 patient) and multiple (7 patients) lobes, with 6 patients achieving freedom from seizures. Of 8 patients who underwent surgery, in 7 the resection area covered ECD clusters and grouped evSAM(g2)s. In the remaining patient the resection area partially included the ECD cluster and grouped evSAM(g2)s. Six of the 7 patients became seizure free. CONCLUSIONS: The combination of SAM(g2) and ECD analyses succeeded in localizing the complex epileptic zones in children with TSC who had intractable epilepsy secondary to multiple cortical tubers. For the subset of children with TSC who present with early-onset and nonlateralized seizures, MEG studies in which SAM(g2) and ECD are used might identify suitable candidates for resection to control seizures.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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".