Prefrontal parvalbumin neurons as a target for enhancing cognition in non-pathological and 22q11.2 microdeletion syndrome mice
Bibliographic record
Abstract
Abstract A failure of organized communication in the PFC is thought to contribute to the emergence of cognitive impairments in psychiatric diseases, with attentional deficits occurring as a fundamental symptom across various conditions. The 22q11.2 microdeletion syndrome is a rare genetic condition that confers a high risk for developing psychiatric and neurodevelopmental disorders, and mouse models have been shown to display attention impairments and PFC pathology that are relevant to clinical populations. Abnormalities in prefrontal parvalbumin-expressing neurons (PVNs) are part of the observed pathophysiology, and studies in rodents have shown that the direct manipulation of these cells can induce behavioral deficits that align with the cognitive symptoms observed in psychiatric diseases. In the present study, we expanded on the role of PVNs in supporting cognition by investigating their involvement in multiple aspects of attentional functions using a translationally relevant task of focused visual attention, in both non-pathological mice and a model of the 22q11.2 microdeletion syndrome. We observed that task-evoked prefrontal PVN activity was reduced in mice that exhibited poorer attention and in 22q11.2 mutant mice. While PVN activity was shaped across learning in non-pathological mice, mutant mice exhibited a lack of signal dynamics that coincided with attentional deficits. Importantly, we observed that task performance in both poor performing wild-types and 22q11.2 mutants could be alleviated by gamma frequency stimulation of PVNs. Thus, PVNs appear to be involved in the acquisition of task rules and execution of attention and continue to be a promising therapeutic target for cognitive dysfunction in disease.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".