Prefrontal parvalbumin neurons facilitate the acquisition and performance of attention in mice
Bibliographic record
Abstract
Parvalbumin (PV) expressing neurons are a subclass of inhibitory cells that strongly regulate the activity of local excitatory neurons. PV neurons are important for maintaining excitation balance in the cortex and are involved in generating synchronous high frequency neuron firing. In the prefrontal cortex (PFC), disrupting the activity of PV neurons is associated with various cognitive impairments. The goal of this study was to assess the role of prefrontal PV neurons during focused visual attention in mice. We used the touchscreen rodent continuous performance task, which allows us to study various aspects of attention, including sustained attention, impulsivity, and visual discrimination. In vivo calcium imaging revealed that PFC PV neurons display increased activity prior to responding to the correct stimulus that increases through training. In vivo optogenetics was used to inactivate PFC PV neurons, or stimulate these neurons at a high (30hz) or low (5hz) frequency during the response phase of the task. When PV neurons were either inactivated or stimulated at 5hz, the animals' ability to attend to a target image was significantly reduced. Alternatively, stimulating PV neurons at 30hz significantly improved the animal's ability to attend to and discriminate the correct image, demonstrating a frequency specific bi-directional effect of PV stimulation. We also observe that an animal's baseline performance (high or low) is predictive of whether optogenetic manipulation can impair or enhance task performance, respectively. This implies that the effectiveness of optogenetic stimulation on altering attention may depend on the baseline organization of PFC activity, and that animals with unoptimized PFC function can be improved by high frequency stimulation of PV neurons.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".