Sub-anesthetic ketamine administration decreases deviance detection responses at the cellular, populational and mesoscale levels
Bibliographic record
Abstract
ABSTRACT In the neocortex, neuronal processing of sensory events is significantly influenced by their predictability. A common example is the suppression of responses to repetitive stimuli in sensory cortices, a phenomenon known as habituation. Within a sensory information stream, whenever a novel stimulus deviates from expectations, enhanced brain responses are observed. Mismatch negativity (MMN), the electroencephalographic waveform reflecting rule violations, is a well-established biomarker for auditory deviant detection. MMN has been shown to depend on intact NMDA receptor signaling across species; nevertheless, the underlying mechanisms at the neuronal and mesoscale levels are still not fully understood. Using multi-electrode array recordings in awake mice, we identified a specific biphasic spiking response in a subpopulation of primary auditory cortex (A1) neurons elicited by deviant, but not standard, sounds, wherein the second peak is abolished by acute sub-anesthetic injection of ketamine, a partial non-competitive NMDA receptor antagonist. We further showed that the posterior parietal cortex (PPC), a critical hub for multisensory integration and sensorimotor coordination, responds to deviant, but not repetitive, sounds, and this response is dependent upon intact NMDA receptor-mediated signaling. Finally, to explore the effects of ketamine on inter-cortical communication following deviance detection, we performed Weighted Phase Lag Index (wPLI) analyses during the presentation of deviant and standard sounds. This analysis showed a functional connectivity between A1 and PPC following deviant detection, which is impaired by ketamine administration. Altogether, our findings provide novel insights into the NMDA receptor-dependent mechanisms underlying the processing of novelty in auditory stimuli.
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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".