Comparative density of CCK- and PV-GABA cells within the cortex and hippocampus
Bibliographic record
Abstract
Cholecystokinin (CCK)- and parvalbumin (PV)-expressing neurons constitute the two major populations of perisomatic GABAergic neurons in the cortex and the hippocampus. As CCK- and PV-GABA neurons differ in an array of morphological, biochemical and electrophysiological features, it has been proposed that they form distinct inhibitory ensembles which differentially contribute to network oscillations and behavior. However, the relationship and balance between CCK- and PV-GABA neurons in the inhibitory networks of the brain is currently unclear as the distribution of these cells has never been compared on a large scale. Here, we systemically investigated the distribution of CCK- and PV-GABA cells across a wide number of discrete forebrain regions using an intersectional genetic approach. Our analysis revealed several novel trends in the distribution of these cells. While PV-GABA cells were more abundant overall, CCK-GABA cells outnumbered PV-GABA cells in several subregions of the hippocampus, medial prefrontal cortex and ventrolateral temporal cortex. Interestingly, CCK-GABA cells were relatively more abundant in secondary/association areas of the cortex (V2, S2, M2, and AudD/AudV) than they were in corresponding primary areas (V1, S1, M1, and Aud1). The reverse trend was observed for PV-GABA cells. Our findings suggest that the balance between CCK- and PV-GABA cells in a given cortical region is related to the type of processing that area performs; inhibitory networks in the secondary cortex tend to favor the inclusion of CCK-GABA cells more than networks in the primary cortex. The intersectional genetic labeling approach employed in the current study expands upon the ability to study molecularly defined subsets of GABAergic neurons. This technique can be applied to the investigation of neuropathologies which involve disruptions to the GABAergic system, including schizophrenia, stress, maternal immune activation and autism.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".