Distinct EEG seizure patterns reflect different seizure generation mechanisms
Bibliographic record
Abstract
Low-voltage fast (LVF)- and hypersynchronous (HYP)-seizure onset patterns can be recognized in the EEG of epileptic animals and patients with temporal lobe epilepsy. Ripples (80-200 Hz) and fast ripples (250-500 Hz) have been linked to each pattern, with ripples predominating during LVF seizures and fast ripples predominating during HYP seizures in the rat pilocarpine model. This evidence led us to hypothesize that these two seizure-onset patterns reflect the contribution of neural networks with distinct transmitter signaling characteristics. Here, we tested this hypothesis by analyzing the seizure activity induced with the K(+) channel blocker 4-aminopyridine (4AP, 4-5 mg/kg ip), which enhances both glutamatergic and GABAergic transmission, or the GABAA receptor antagonist picrotoxin (3-5 mg/kg ip); rats were implanted with electrodes in the hippocampus, the entorhinal cortex, and the subiculum. We found that LVF onset occurred in 82% of 4AP-induced seizures whereas seizures after picrotoxin were always HYP. In addition, high-frequency oscillation analysis revealed that 4AP-induced LVF seizures were associated with higher ripple rates compared with fast ripples (P < 0.05), whereas picrotoxin-induced seizures contained higher rates of fast ripples compared with ripples (P < 0.05). These results support the hypothesis that two distinct patterns of seizure onset result from different pathophysiological mechanisms.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.001 | 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".