Distinct dual cortico-cortical networks successfully identified between supplemental and primary motor areas during intracranial EEG for drug-resistant frontal lobe epilepsy
Bibliographic record
Abstract
We present a case of drug-resistant focal motor seizures in which separate cortico-cortical epileptic networks within the supplementary motor area (SMA) proper and primary motor area (PMA) were proven by ictal high-frequency oscillation (HFO) and cortico-cortical evoked potential (CCEP). A 12-year-old girl presented with two types seizures: type A, tonic extension and subsequent clonic movements of the right arm; and type B, tonic and clonic movements of the right leg. MRI was normal and karyotype genetic analysis revealed 46,X,t(X;14)(q13;p12). She underwent placement of chronic subdural electrodes over the left hemisphere. We recorded a total of nine seizures during 10 days of epilepsy monitoring. Type A seizures started from the lower part of the left SMA proper and early spread to the hand motor area of the PMA. Type B seizures started from the upper part of the SMA proper and early spread to the leg motor area of the PMA. CCEPs of both SMA proper and PMA activated two identical routes for evoked potentials correlating with separate pathways. Corticectomy of the left SMA proper and PMA achieved seizure-free without hemiparesis. Within a small homunculus of the SMA proper, separate epileptic networks were proven and validated by seizure semiology, ictal HFO, and CCEP.
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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".