Selective Activation of Cholecystokinin-Expressing GABA (CCK-GABA) Neurons Enhances Memory and Cognition
Bibliographic record
Abstract
Abstract Cholecystokinin-expressing GABAergic (CCK-GABA) neurons are perisomatic inhibitory cells that have been argued to regulate emotion and sculpt the network oscillations associated with cognition. However, no study has selectively manipulated CCK-GABA neuron activity during behavior in freely-moving animals. To explore the behavioral effects of activating CCK-GABA neurons on emotion and cognition, we utilized a novel intersectional genetic mouse model coupled with a chemogenetic approach. Specifically, we generated triple transgenicCCK-Cre;Dlx5/6-Flpe;RC::FL-hM3Dq(CCK-GABA/hM3Dq) mice that expressed the synthetic excitatory hM3Dq receptor in CCK-GABA neurons. Results showed that clozapine-N-oxide (CNO)-mediated activation of CCK-GABA neurons did not alter open field (OF) or tail suspension (TS) performance and only slightly increased anxiety in the elevated plus maze (EPM). Although CNO treatment had only modestly affected emotional behavior, it significantly enhanced multiple cognitive and memory behaviors including social recognition, contextual fear conditioning, contextual discrimination, object recognition, and problem-solving in the puzzle box. Collectively, these findings suggest that systemic activation of CCK-GABA neurons minimally affects emotion but significantly enhances cognition and memory. Our results imply that CCK-GABA neurons are more functionally diverse than originally expected and could serve as a potential therapeutic target for the treatment of cognitive/memory disorders.
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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 | 0.001 |
| 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".