FLUCTUATIONS AND INTERACTIONS BETWEEN BRAIN WAVES DURING DEEP AND SHALLOW ANESTHESIA
Bibliographic record
Abstract
Using gold plated electrodes, inserted into the rat's head above the dura of the left and right parietal cortex, we recorded EEG during deep and shallow anesthesia with either pentobarbital (PB) or ketamine-xylazine (KX). The fluctuations in time series were then analyzed using wavelet transforms and the spectral power was determined within 7 frequency intervals (slow wave 2, S2, 0.0067–0.0167 Hz; slow wave 1, S1, 0.02–0.19 Hz; δ, 0.2–3.9 Hz; θ, 4–7.9 Hz; α, 8–12.9 Hz; β, 13–24.9 Hz and γ, 25–34.9 Hz). In addition, the coupling strengths between individual oscillatory components during deep and shallow anesthesia were evaluated for both anesthetics. We show specific changes for both anesthetics indicating that during deep anesthesia PB reduces high and low frequency activity (0.2–35 Hz) and enhances coupling especially between δ, θ and α waves, while KX reduces low frequency activity (0.005 to 0.2 Hz) and enhances coupling between frequency waves α, β and γ. Our results, using two anesthetics known to block different ion channels, provide an insight into brain dynamics and could have wide implications in creating biomarkers for detecting various neurophysiological modifications, such as in Alzheimer and Parkinson's disease or Autism spectrum disorder, as well as in providing more realistic models of brain dynamics.
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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 | 0.000 | 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) | 0.000 | 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".