Evaluation of epileptogenic networks in children with tuberous sclerosis complex using EEG‐fMRI
Bibliographic record
Abstract
PURPOSE: Ninety percent of patients with tuberous sclerosis complex (TSC) have epilepsy. Identification of epileptogenic areas can be difficult and studies are needed to characterize the epileptogenic network in more detail. METHODS: Five children with TSC and focal epilepsy were studied using simultaneous EEG and functional MRI recordings. Tubers were marked by a neuroradiologist on the anatomical MRI. Spike-associated BOLD (blood oxygenation level-dependent) responses were superimposed with lesions. RESULTS: Thirteen different types of interictal epileptiform discharges (IED) were analyzed with 12 showing a BOLD response, all involving more than one tuber. Five studies had tubers with activations exclusively within the lesion, three studies had lesional activations extending to perilesional areas, and two studies had activations involving exclusively perilesional areas of at least one tuber. Deactivations exclusively within a tuber were found in six studies, lesional deactivations extending to perilesional areas were found in four studies, and tubers with exclusively perilesional deactivations were found in five studies. A BOLD response was found in at least one tuber in the lobe of IED generation and presumed seizure onset (according to telemetry) in all patients. In four patients, the same tubers were involved following different IED localizations. The observed changes were always multifocal, sometimes involving tubers distant from the IED field. DISCUSSION: These findings suggest extended epileptogenic networks in patients with TSC, which exceed networks described in PET and SPECT studies. It was possible to identify specific interictally active tubers. EEG-fMRI provides a noninvasive method to select tubers and areas at their borders for further presurgical investigations.
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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 | 0.001 | 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".