High frequency oscillations in intracranial EEGs mark epileptogenicity rather than lesion type
Bibliographic record
Abstract
High frequency oscillations (HFOs) called ripples (80-250 Hz) and fast ripples (FR, 250-500 Hz) can be recorded from intracerebral EEG macroelectrodes in patients with intractable epilepsy. HFOs occur predominantly in the seizure onset zone (SOZ) but their relationship to the underlying pathology is unknown. It was the aim of this study to investigate whether HFOs are specific to the SOZ or result from pathologically changed tissue, whether or not it is epileptogenic. Patients with different lesion types, namely mesial temporal atrophy (MTA), focal cortical dysplasia (FCD) and nodular heterotopias (NH) were investigated. Intracranial EEG was recorded from depth macroelectrodes with a sampling rate of 2000 Hz. Ripples (80-250 Hz) and Fast Ripples (250-500 Hz) were visually marked in 12 patients: five with MTA, four with FCD and three with NH. Rates of events were statistically compared in channels in four areas: lesional SOZ, non-lesional SOZ, lesional non-SOZ and non-lesional non-SOZ. HFO rates were clearly more linked to the SOZ than to the lesion. They were highest in areas in which lesion and SOZ overlap, but in patients with a SOZ outside the lesion, such as in NHs, HFO rates were clearly higher in the non-lesional SOZ than in the inactive lesions. No specific HFO pattern could be identified for the different lesion types. The findings suggest that HFOs represent a marker for SOZ areas independent of the underlying pathology and that pathologic tissue changes alone do not lead to high rates of HFOs.
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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".