The McGill-Mouse-Marmoset Platform: A Standardized Approach for High-throughput Imaging of Neuronal Dynamics During Behavior
Bibliographic record
Abstract
Abstract Understanding the rules that govern neuronal dynamics throughout the brain to subserve behavior and cognition remain one of the biggest challenges in neuroscience research. Recent technical advances enable the recording of increasingly larger neuronal populations to produce increasingly more sophisticated datasets. Despite bold and important open-science and data-sharing policies, these datasets tend to include unique data acquisition methods, behavior, and file structures. Discrepancies between experimental protocols present several key challenges including the analysis of the data itself, comparison of data collected between laboratories, and for the comparison of dynamics between brain regions and species. Here, we discuss our recent efforts to create a standardized and high-throughput research platform to address these issues. The McGill-Mouse-Marmoset (M3) platform combines miniscope calcium imaging recording in both mice and marmosets with standardized touchscreen-based behavioral testing. The goal is to curate an open-source and standardized framework for acquiring, analyzing, and accessing high-quality data of the neuronal dynamics that underly cognition throughout the brain in mice, marmosets, and models of disease. We end with a discussion of future developments and a call for users to adopt this standardized approach.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.005 |
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".