Inhibitory Circuit Compensations in Female and Male Mice: Increased Synaptic Output Offsets Reduced Parvalbumin Interneuron Density
Bibliographic record
Abstract
Summary Parvalbumin inhibitory interneurons (PV-INs) are critical regulators of excitatory/inhibitory balance in the cortex, and their dysfunction has been observed in various neurological disorders. Despite increasing recognition of sex differences in brain function, little is known about how PV-INs differ between males and females under healthy conditions. Previous work has pointed to sex differences in PV-IN vulnerability in disease and injury models. Here, we investigated sex differences in PV-IN characteristics, connectivity, and function in the retrosplenial cortex (RSC) of healthy mice. We found that female mice have significantly fewer PV-INs in the RSC compared to males, yet exhibit comparable memory induced neuronal activation (fos expression). Despite their lower numbers, female PV-INs displayed greater synaptic connectivity, as evidenced by increased synaptotagmin-2 (Syt-2) puncta per PV-IN and higher axonal bouton density. Additionally, fewer female PV-INs were surrounded by perineuronal nets (PNNs), suggesting greater plasticity in female inhibitory networks. From ex vivo slice electrophysiology recordings we observed greater excitability in female PV-INs compared to male PV-INs and, a reduced incidence of IPSCs. These findings indicate that female mice may compensate for reduced PV-IN numbers through enhanced synaptic output, preserving inhibitory function in the RSC. Finally, using spatial transcriptomic profiling of PV-INs we observed a number of differentially expressed genes that are consistent with the observed structural and functional differences between female and male PV-INs. Understanding these sex-specific inhibitory mechanisms is crucial for developing more targeted interventions for conditions involving PV-IN impairment and for understanding sex specific vulnerabilities to certain conditions such as Alzheimer’s 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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.006 | 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".