Activation of mGluR2/3 receptors in the ventro-rostral prefrontal cortex reverses sensorimotor gating deficits induced by systemic NMDA receptor antagonists
Bibliographic record
Abstract
Prepulse inhibition (PPI) of acoustic startle is an operational measure of sensorimotor gating, which is disrupted in schizophrenia. NMDA receptor (NMDAR) antagonist induced PPI disruption has become an important pharmacological model for schizophrenia; however, knowledge of the underlying mechanism remains incomplete. This study examines the role of NMDAR in the caudal pontine reticular nucleus (PnC) and the medial prefrontal cortex (mPFC) in NMDARs antagonist induced PPI deficits, as well as the NMDA receptor subtypes involved. We administered the NMDA antagonist MK-801 locally into the caudal pontine reticular formation (PnC), where the PPI mediating pathway converges with the primary startle pathway, and into the mPFC prior to behavioural testing. PnC microinjections had no effect on startle and PPI, whereas injections into the ventro-rostral part, but not into the dorso-caudal part of the mPFC, disrupted PPI. These effects could be mimicked by local injection of the NR2B subunit specific antagonist ifenprodil, whereas co-application of MK-801 and the mGluR2/3 agonist LY354740 had no effect on PPI. Moreover, PPI disruptions by systemically administered MK-801 could be reversed by local injections of LY354740 into the ventro-rostral mPFC, but not into the dorso-caudal mPFC. Our results indicate that NR2B subunit containing NMDARs in a specific subregion of the mPFC play a major role in PPI disruptions by systemic NMDAR antagonism. Our results further support the hypothesis that glutamate hyper-function in the mPFC is a main mechanism involved in sensory gating deficits induced by systemic MK-801, supporting the notion that this is an important mechanism in schizophrenia pathology.
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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) | 0.002 | 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".