Continuous Monitoring via Tethered Electroencephalography of Spontaneous Recurrent Seizures in Mice
Bibliographic record
Abstract
We describe here a simple, cost-effective apparatus for continuous tethered electroencephalographic (EEG) monitoring of spontaneous recurrent seizures in mice. We used a small, low torque slip ring as an EEG commutator, mounted the slip ring onto a standard mouse cage and connected rotary wires of the slip ring directly to animal's implanted headset. Modifications were made in the cage to allow for a convenient installation of the slip ring and accommodation of animal ambient activity. We tested the apparatus for hippocampal EEG recordings in adult C57 black mice. Spontaneous recurrent seizures were induced using extended hippocampal kindling (≥95 daily stimulation). Control animals underwent similar hippocampal electrode implantations but no stimulations were given. Combined EEG and webcam monitoring were performed for 24 h daily for 5-9 consecutive days. During the monitoring periods, the animals moved and accessed water and food freely and showed no apparent restriction in ambient cage activities. Ictal-like hippocampal EEG discharges and concurrent convulsive behaviors that are characteristics of spontaneous recurrent seizures were reliably recorded in a majority of the monitoring experiments in extendedly kindled but not in control animals. However, 1-2 rotary wires were disconnected from the implanted headset in some animals after continuous recordings for ≥5 days. The key features and main limitations of our recording apparatus are discussed.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".