Interictal oscillations and focal epileptic disorders
Bibliographic record
Abstract
Neuronal network oscillations represent a main feature of the brain activity recorded in the EEG under normal and pathological conditions such as epilepsy. Specific oscillations occur between seizures in patients and in animal models of focal epilepsy, and thus, they are termed interictal. According to their shape and intrinsic signal frequency, interictal oscillations are classified as spikes and high-frequency oscillations (HFOs). Interictal spikes are recorded in the 'wideband' EEG signal and consist of large-amplitude events that usually last less than 1 s; HFOs, in contrast, are extracted by amplifying the appropriately filtered EEG signal and are usually categorized as ripples (80-200 Hz) and fast ripples (250-500 Hz). Interictal spikes and HFOs are used in clinical practice to localize the seizure onset zone in focal epileptic disorders, which is fundamental for performing successful surgical interventions in epileptic patients not responding to anti-epileptic drug therapy. Both types of interictal oscillations have been widely studied in animal models of focal epilepsy to identify the mechanisms underlying their generation as well as to establish their role in ictogenesis and epileptogenesis. In this review, we will address the cellular mechanisms underlying the generation of interictal spikes and HFOs in animal models of epileptiform synchronization and of focal epilepsy. Moreover, we will highlight in vitro and in vivo evidence indicating that these interictal oscillations mirror specific, dynamic changes in neuronal network excitability causing seizure generation (i.e. ictogenesis) and leading to a chronic epileptic condition (i.e. epileptogenesis).
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".