Intracranial electroencephalographic seizure-onset patterns: effect of underlying pathology
Bibliographic record
Abstract
Because seizures originate from different pathological substrates, the question arises of whether distinct or similar mechanisms underlie seizure generation across different pathologies. Better defining intracranial electroencephalographic morphological patterns at seizure-onset could improve the understanding of such mechanisms. To this end, we investigated intracranial electroencephalographic seizure-onset patterns associated with different epileptogenic lesions, and defined high-frequency oscillation correlates of each pattern. We analysed representative seizure types from 33 consecutive patients with drug-resistant focal epilepsy and a structural magnetic resonance imaging lesion (11 mesial temporal sclerosis, nine focal cortical dysplasia, six cortical atrophy, three periventricular nodular heterotopia, three polymicrogyria, and one tuberous sclerosis complex) who underwent depth-electrode electroencephalographic recordings (500 Hz filter, 2000 Hz sampling rate). Patients were included only if seizures arose from contacts located in lesional/peri-lesional tissue, and if clinical manifestations followed the electrographic onset. Seizure-onset patterns were defined independently by two reviewers blinded to clinical information, and consensus was reached after discussion. For each seizure, pre-ictal and ictal sections were selected for high-frequency oscillation analysis. Seven seizure-onset patterns were identified across the 53 seizures sampled: low-voltage fast activity (43%); low-frequency high-amplitude periodic spikes (21%); sharp activity at ≤13 Hz (15%); spike-and-wave activity (9%); burst of high-amplitude polyspikes (6%); burst suppression (4%); and delta brush (4%). Each pattern occurred across several pathologies, except for periodic spikes, only observed with mesial temporal sclerosis, and delta brush, exclusive to focal cortical dysplasia. However, mesial temporal sclerosis was not always associated with periodic spikes nor focal cortical dysplasia with delta brush. Compared to other patterns, low-voltage fast activity was associated with a larger seizure-onset zone (P = 0.04). Four patterns, sharp activity at ≤13 Hz, low-voltage fast activity, spike-and-wave activity and periodic spikes, were also found in regions of seizure spread, with periodic spikes only emerging from mesial temporal sclerosis. Each of the seven patterns was accompanied by a significant increase in high-frequency oscillations upon seizure-onset. Overall, our data indicate that: (i) biologically-distinct epileptogenic lesions share intracranial electroencephalographic seizure-onset patterns, suggesting that different pathological substrates can affect similarly networks or mechanisms underlying seizure generation; (ii) certain pathologies are associated with intracranial electroencephalographic signatures at seizure-onset, e.g. periodic spikes which may reflect mechanisms specific to mesial temporal sclerosis; (iii) some seizure-onset patterns, including periodic spikes, can also be found in regions of spread, which cautions against relying on the morphology of the initial discharge to define the epileptogenic zone; and (iv) high-frequency oscillations increase at seizure-onset, independently of the pattern.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".