GABAC receptors in the lateral amygdala: a possible novel target for the treatment of fear and anxiety disorders?
Bibliographic record
Abstract
Activation of GABA(A)Rs in the lateral nucleus of the amygdala (LA), a key site of plasticity underlying fear learning, impairs fear learning. The role of GABA(C)Rs in the LA and other brain areas is poorly understood. GABA(C)Rs could be an important novel target for pharmacological treatments of anxiety-related disorders since, unlike GABA(A)Rs, GABA(C)Rs do not desensitize. To detect functional GABA(C)Rs in the LA we performed whole cell patch clamp recordings in vitro. We found that GABA(A)Rs and GABA(B)Rs blockade lead to a reduction of evoked inhibition and an increase increment of excitation, but activation of GABA(C)Rs caused elevations of evoked excitation, while blocking GABA(C)Rs reduced evoked excitation. Based on this evidence we tested whether GABA(C)Rs in LA contribute to fear learning in vivo. It is established that activation of GABA(A)Rs leads to blockage of fear learning. Application of GABA(C) drugs had a very different effect; fear learning was enhanced by activating and attenuated by blocking GABA(C)Rs in the LA. Our results suggest that GABA(C) and GABA(A)Rs play opposing roles in modulation of associative plasticity in LA neurons of rats. This novel role of GABA(C)Rs furthers our understanding of GABA receptors in fear memory acquisition and storage and suggests a possible novel target for the treatment of fear and anxiety disorders.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".