Generation and On-Demand Initiation of Acute Ictal Activity in Rodent and Human Tissue
Bibliographic record
Abstract
Controlling seizures remains a challenging issue for the medical community. To make progress, researchers need a way to extensively study seizure dynamics and investigate its underlying mechanisms. Acute seizure models are convenient, offer the ability to perform electrophysiological recordings, and can generate a large volume of electrographic seizure-like (ictal) events. The promising findings from acute seizure models can then be advanced to chronic epilepsy models and clinical trials. Thus, studying seizures in acute models that faithfully replicate the electrographic and dynamical signatures of a clinical seizure will be essential for making clinically relevant findings. Studying ictal events in acute seizure models prepared from human tissue is also important for making findings that are clinically relevant. The key focus in this paper is on the cortical 4-AP model due to its versatility in generating ictal events in both in vivo and in vitro studies, as well as in both mouse and human tissue. The methods in this paper will also describe an alternative method of seizure induction using the Zero-Mg2+ model and provide a detailed overview of the advantages and limitations of the epileptiform-like activity generated in the different acute seizure models. Moreover, by taking advantage of commercially available optogenetic mouse strains, a brief (30 ms) light pulse can be used to trigger an ictal event identical to those occurring spontaneously. Similarly, 30 - 100 ms puffs of neurotransmitters (Gamma-Amino Butyric Acid or glutamate) can be applied to the human tissue to trigger ictal events that are identical to those occurring spontaneously. The ability to trigger ictal events on-demand in acute seizure models offers the newfound ability to observe the exact sequence of events that underlie seizure initiation dynamics and efficiently evaluate potential anti-seizure therapies.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".