Morphometric Identification of Parvalbumin-Positive Interneurons: A Data-Driven Approach
Bibliographic record
Abstract
Abstract Traditionally, anatomical studies of parvalbumin-positive (PV+) labelled interneurons describe them as a homogeneous population of neurons. In contrast, recent single-cell RNAseq studies have identified multiple transcriptomically distinct categories of PV+ cells. That difference between a single anatomical category of PV+ neurons and multiple transcriptomic categories presents a problem in understanding the role of these neurons in cortical function. One gap that might contribute to this discrepancy is that PV+ morphology is typically addressed using qualitative descriptions and simple quantifications, while single-cell RNAseq studies use big data and high dimensional analyses. PV+ neurons play critical roles in the experience-dependent development of the cortex and are often involved in disease-related changes associated with neurodegenerative and neuropsychiatric disorders. Here, we developed a modern data-driven analysis pipeline to quantify PV+ morphology. We quantified 97 morphometric features from 14274 PV+ neurons and applied unsupervised clustering that identified 13 different PV+ morphologies. We extended the analysis to compare PV+ dendritic arbour patterns and cell body morphologies. Finally, we compared the morphologies of PV+ neurons with the cell body morphologies of neurons expressing various genes associated with PV+ transcriptomic cell types. This approach identified a range of PV+ morphologies similar to the number of transcriptomic categories. It also found that the PV+ morphologies have cortical area, laminar, and transcriptomic biases that might contribute to cortical function.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".