Protocol for continuous video-EEG/EMG recording to study brain function in neonatal rats
Bibliographic record
Abstract
The electroencephalogram (EEG) is crucial for real-time brain physiology research in epilepsy. However, maternal care reliance limits its use in immature rodents. Our “pup-in-cup” setup overcomes this, enabling continuous, uninterrupted video-EEG/electromyogram (EMG) recordings in neonatal rats. This protocol details the steps for video-EEG/EMG system setup, EEG headmount implantation, and recording continuous video-EEG/EMG traces from postnatal days 4–12. For complete details on the use and execution of this protocol, please refer to Choudhary et al. 1 • A protocol for recording continuous video-EEG/EMG in neonatal rats, as young as P4 • Utilizing artificial rearing allows for continuous, uninterrupted data acquisition • This system allows for examination of epilepsy neural mechanisms in immature rodents • Adaptable to other neurological diseases requiring video-EEG monitoring Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics. The electroencephalogram (EEG) is crucial for real-time brain physiology research in epilepsy. However, maternal care reliance limits its use in immature rodents. Our “pup-in-cup” setup overcomes this, enabling continuous, uninterrupted video-EEG/electromyogram (EMG) recordings in neonatal rats. This protocol details the steps for video-EEG/EMG system setup, EEG headmount implantation, and recording continuous video-EEG/EMG traces from postnatal days 4–12.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.061 | 0.022 |
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".