Disrupting the clustering of GABA <sub>A</sub> receptor α2 subunits in the frontal cortex leads to reduced γ-power and cognitive deficits
Bibliographic record
Abstract
In schizophrenia, cognitive dysfunction is highly predictive of poor patient outcomes and is not responsive to current medications. Postmortem studies have suggested that cognitive deficits in schizophrenia are correlated with modifications in the number and size of inhibitory synapses. To test if these modifications lead to cognitive deficits, we have created a dominant-negative virus [adeno-associated (AAV)-DN1] that disrupts the clustering of γ-aminobutyric acid type A receptors (GABA(A)Rs) at postsynaptic inhibitory specializations. When injected into the frontal cortex of mice, AAV-DN1 impairs GABA(A)R α2 subunit and GABA transporter 1 (GAT-1) clustering, but increases GABA(A)R α1 subunit clustering on the perisomatic region, with no influence on axon-initial segment clustering. Mice expressing AAV-DN1 have prepulse inhibition deficits and impairments in working memory. Significantly, these behavioral deficits are paralleled by a reduction in electroencephalography γ-power. Collectively, our study provides functional evidence revealing that GABAergic synapses in the prefrontal cortex directly contribute to cognition and γ-power.
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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) | 0.000 | 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".