Disruption of Coherent Oscillations in Inhibitory Networks With Anesthetics: Role of GABA<sub>A</sub> Receptor Desensitization
Bibliographic record
Abstract
The effect of anesthetic drugs at central synapses can be described quantitatively by developing kinetic models of ligand-gated ion channels. Experiments have shown that the hypnotic propofol and the sedative benzodiazepine midazolam have similar effects on single inhibitory postsynaptic potentials (IPSPs) but very different effects on slow desensitization that are not revealed by examining single responses. Synchronous oscillatory activity in networks of interneurons connected by inhibitory synapses has been implicated in many hippocampal functions, and differences in the kinetics of the GABAergic response observed with anesthetics can affect this activity. Thus we have examined the effect of propofol and midazolam-enhanced IPSPs using mathematical models of self-inhibited one- and two-cell inhibitory networks. A detailed kinetic model of the GABA(A) channel incorporating receptor desensitization is used at synapses in our models. The most dramatic effect of propofol is the modulation of slow desensitization. This is only revealed when the network is driven at frequencies that are thought to be relevant to cognitive tasks performed in the hippocampus. The level of desensitization at synapses with propofol is significantly reduced compared to control synapses. In contrast, midazolam increases macroscopic desensitization at network synapses by altering receptor affinity without concurrently modifying desensitization rates. These differences in gating between the two drugs are shown to alter network activity in stereotypically different ways. Specifically, propofol dramatically increases the amount of excitatory drive necessary for synchronized behavior relative to control, which is not the case for midazolam. Moreover, the range of parameters for which synchrony occurs is larger for propofol but smaller for midazolam, relative to control. This is an important first step in linking alterations in channel kinetics with behavioral changes.
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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 | 0.001 |
| 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 | 0.001 |
| 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".