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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".