Acutely increasing δGABAA receptor activity impairs memory and inhibits synaptic plasticity in the hippocampus
Bibliographic record
Abstract
Extrasynaptic γ-aminobutyric acid type A (GABA(A)) receptors that contain the δ subunit (δGABA(A) receptors) are expressed in several brain regions including the dentate gyrus (DG) and CA1 subfields of the hippocampus. Drugs that increase δGABA(A) receptor activity have been proposed as treatments for a variety of disorders including insomnia, epilepsy and chronic pain. Also, long-term pretreatment with the δGABA(A) receptor-preferring agonist 4,5,6,7-tetrahydroisoxazolo[5,4-c]pyridin-3-ol (THIP) enhances discrimination memory and increases neurogenesis in the DG. Despite the potential therapeutic benefits of such treatments, the effects of acutely increasing δGABA(A) receptor activity on memory behaviors remain unknown. Here, we studied the effects of THIP (4 mg/kg, i.p.) on memory performance in wild-type (WT) and δGABA(A) receptor null mutant (Gabrd(-/-)) mice. Additionally, the effects of THIP on long-term potentiation (LTP), a molecular correlate of memory, were studied within the DG and CA1 subfields of the hippocampus using electrophysiological recordings of field potentials in hippocampal slices. The results showed that THIP impaired performance in the Morris water maze, contextual fear conditioning and object recognition tasks in WT mice but not Gabrd(-/-) mice. Furthermore, THIP inhibited LTP in hippocampal slices from WT but not Gabrd(-/-) mice, an effect that was blocked by GABA(A) receptor antagonist bicuculline. Thus, acutely increasing δGABA(A) receptor activity impairs memory behaviors and inhibits synaptic plasticity. These results have important implications for the development of therapies aimed at increasing δGABA(A) receptor activity.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".