Evolution of interictal spiking during the latent period in a mouse model of mesial temporal lobe epilepsy
Bibliographic record
Abstract
Interictal spikes and high-frequency oscillations (HFOs, ripples: 80-200 Hz, fast ripples: 250-500 Hz) occur in epileptic patients and in animal models of mesial temporal lobe epilepsy (MTLE). In this study, we explored how type 1 and type 2 interictal spikes as well as ripples and fast ripples evolve during the latent period in the hippocampus of pilocarpine-treated mice. Depth EEG recordings were obtained from the hippocampus CA3 subfield of adult male mice (n = 5, P60-P100) starting one day before pilocarpine-induced status epilepticus up to the first spontaneous seizure, the so-called latent period. We found that rates of type 1 (n = 1 655) and type 2 (n = 2 309) interictal spikes were significantly lower during the late phase of the latent period compared to its early and mid phase (p < 0.001). However, rates of type 1 spikes associated with ripples (n = 266) or fast ripples (n = 106), as well as rates of type 2 interictal spikes associated with ripples (n = 233), were significantly higher during the late phase compared to the early and mid phases (p < 0.05). Our findings reveal that an increase of type 1 interictal spikes co-occurring with ripples or fast ripples and an increase of type 2 interictal spikes co-occurring with ripples mark the end of the latent period. We propose that changes in the occurrence of interictal spike associated with HFOs represent a biomarker of epileptogenicity in this mouse model of MTLE.
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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 | 0.000 |
| 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 | 0.001 |
| 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".