Influence of the location and type of epileptogenic lesion on scalp interictal epileptiform discharges and high‐frequency oscillations
Bibliographic record
Abstract
OBJECTIVE: To increase the diagnostic power of scalp electroencephalography (EEG) by investigating whether lesion type and location influence the morphology of interictal epileptic discharges (IEDs) and the likelihood that IEDs and high-frequency oscillations (HFOs) are present. METHODS: We studied EEG activity in epilepsy patients with lesional epilepsy. Lesions were classified by type and by location (region and depth). We marked a maximum of 50 IEDs during deep non-rapid eye movement sleep. IEDs were identified as spikes or sharp waves with or without slow waves, or bursts of spikes or sharp waves with or without slow waves. We analyzed HFOs in the studies showing at least 50 IEDs. RESULTS: In 192 scalp EEG studies, the differences in the percentage of studies showing IEDs in each depth-related group were not statistically significant, whereas HFOs (55 studies) predominated in patients exhibiting superficial lesions (p<0.001). Sharp waves, as predominant pattern, were more prevalent in hippocampal abnormalities (p < 0.001), whereas bursts predominated in patients with malformations of cortical development (p < 0.001). SIGNIFICANCE: The depth of the lesion does not influence the presence of IEDs, as one might expect, but it influences that of HFOs. This is explained as follows. HFOs are generated in the epileptogenic region, do not propagate, and hence are only visible on scalp EEG with superficial lesions. IEDs can result from a nearby focus or propagate from a deep generator and are therefore equally present with deep, intermediate, and superficial lesions. Additionally, IED morphology provides information in determining the lesion type.
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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 | 0.001 |
| 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) | 0.000 | 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".