Ketamine Increases the Function of γ-Aminobutyric Acid Type A Receptors in Hippocampal and Cortical Neurons
Bibliographic record
Abstract
BACKGROUND: The "dissociative " general anesthetic ketamine is a well-known N-methyl-D-aspartate receptor antagonist. However, whether ketamine, at clinically relevant concentrations, increases the activity of inhibitory γ-aminobutyric acid (GABA) receptor type A (GABAA) receptors in different brain regions remains controversial. Here, the authors studied the effects of ketamine on synaptic and extrasynaptic GABAA receptors in hippocampal neurons. Ketamine modulation of extrasynaptic GABAA receptors in cortical neurons was also examined. METHODS: Whole cell currents were recorded from cultured murine neurons. Current evoked by exogenous GABA, miniature inhibitory postsynaptic currents, and currents directly activated by ketamine were studied. RESULTS: Ketamine did not alter the amplitude, frequency, or kinetics of postsynaptic currents but increased a tonic inhibitory current generated by extrasynaptic GABAA receptors in hippocampal neurons. For example, ketamine (100 µM) increased the tonic current by 33.6 ± 6.5% (mean ± SEM; 95% CI, 18.2 to 48.9; n = 8, P < 0.001). Ketamine shifted the GABA concentration-response curve to the left, but only when GABAA receptors were activated by low concentrations of GABA (n = 6). The selective increase in tonic current was attributed to ketamine increasing the apparent potency of GABA at high-affinity extrasynaptic GABAA receptors. Ketamine also increased a tonic current in cortical neurons (n = 11). Ketamine directly gated the opening of GABAA receptors, but only at high concentrations that are unlikely to occur during clinical use. CONCLUSIONS: Clinically relevant concentrations of ketamine increased the activity of high-affinity extrasynaptic GABAA receptors in the hippocampus and cortex, an effect that likely contributes to ketamine's neurodepressive properties.
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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) | 0.001 | 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".