Pathologic substrates of focal epilepsy influence the generation of high‐frequency oscillations
Bibliographic record
Abstract
OBJECTIVE: Although a clear correlation has been observed between high-frequency oscillations (HFOs) and the seizure-onset zone in distinct lesions, the role of the underlying pathologic substrates in the generation of HFOs is not well established. We aimed to investigate HFO correlates of different pathologic substrates in patients with drug-resistant epilepsy, and to examine the relation of HFOs with the anatomic location of the dysplastic lesion and surrounding tissue in patients with focal cortical dysplasia (FCD). METHODS: We studied consecutive patients with drug-resistant epilepsy who underwent intracranial electroencephalography (iEEG) investigations with depth electrodes at the Montreal Neurological Institute and Hospital, between November 2004 and May 2013. Inclusion criteria were the following: a focal lesion documented by magnetic resonance imaging (MRI); EEG recording at a 2,000 Hz sampling rate; and seizures starting from depth electrode contacts placed in lesion and perilesional tissue. RESULTS: Thirty-seven patients (13 FCD, 12 mesial temporal sclerosis, five cortical atrophy, three polymicrogyria, three nodular heterotopia, and one tuberous sclerosis) were included; 18 were women (median age 34). Ripples and fast ripples were found in all lesion types, except tuberous sclerosis, which showed no fast ripples. There was a significant difference in rates of ripples and fast ripples across different lesions (p < 0.001), with higher rates in FCD, mesial temporal sclerosis, and nodular heterotopia than in atrophy, polymicrogyria, and tuberous sclerosis. Regarding patients with FCD, HFOs rates differed significantly across the three types of tissue (lesional, perilesional, and nonlesional; p < 0.001), being higher within the borders of the MRI-visible dysplastic lesion, followed by the surrounding area, and rare in the remote cortex. SIGNIFICANCE: Our findings suggest that in patients who are all intractable, the HFO rates vary with different pathologies, and reflect different types of neuronal derangements. Our results also emphasize the potential usefulness of HFOs as an additional method to better define the extent of the epileptogenic dysplastic tissue in FCD.
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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".