Characterizing Magnetoencephalographic Spike Sources in Children with Tuberous Sclerosis Complex
Bibliographic record
Abstract
PURPOSE: Tuberous sclerosis complex (TSC) often causes medically intractable seizures. Magnetoencephalography (MEG) localizes epileptiform discharges. To evaluate the use of MEG spike sources (MEGSSs) for localizing epileptic zones in TSC patients, we characterized MEGSSs and correlated them to EEG and magnetic resonance imaging (MRI) results. METHODS: We analyzed data from seven children who underwent prolonged video-EEG, MEG, and MRI. We classified MEGSSs as clusters (six or more spike sources, 1 cm between sources regardless of number of sources). RESULTS: A single, unilateral cluster with additional scatters occurred in two patients; these predominantly lateralized dipoles correlated to prominent tubers on MRI and ictal/interictal EEG zones. Bilateral clusters with scatters existed in two patients; cluster locations partly overlapped multiple prominent tubers. These patients also had bilateral or diffuse interictal discharges, bilateral or generalized seizures, and changing seizure types and EEG findings. Only bilateral scatters occurred in three patients; scatters partly overlapped EEG interictal/ictal-onset regions; one patient had coexisting generalized seizures. In one patient with equally bilateral scatters, scatters overlapped a prominent tuber and interictal/ictal-onset zones in the right frontal region. CONCLUSIONS: MEG contributes to information from EEG and MRI for localizing epileptogenic zones in children with TSC. A single cluster with scatters in a unilateral hemisphere predicts a primary epileptogenic zone or hemisphere; bilateral or multiple clusters indicate bilateral primary or potential epileptogenic zones; and bilateral scatters without clusters may indicate epileptogenic zones that are hidden within extensive areas of scattered MEGSSs.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.000 |
| 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".