Quantification of gait parameters in freely walking rodents
Bibliographic record
Abstract
BACKGROUND: Qualitative and quantitative measurements of motor performance are essential for characterizing perturbations of motor systems. Although several methods exist for analyzing specific motor tasks, few behavioral assays are readily available to researchers that provide a complete set of kinematic parameters in rodents. RESULTS: Here we present MouseWalker, an integrated hardware and software system that provides a comprehensive and quantitative description of kinematic features in freely walking rodents. Footprints are visualized with high spatial and temporal resolution by a non-invasive optical touch sensor coupled to high-speed imaging. A freely available and open-source software package tracks footprints and body features to generate a comprehensive description of many locomotion features, including static parameters such as footprint position and stance patterns and dynamic parameters, such as step and swing cycle duration, and inter-leg coordination. Using this method, we describe walking by wild-type mice including several previously undescribed parameters. For example, we demonstrate that footprint touchdown occurs instantaneously by the entire paw with no obvious rostral-caudal or lateral-medial bias. CONCLUSIONS: The readily available MouseWalker system and the large set of readouts it generates greatly increases the currently available toolkit for the analysis of wild type and aberrant locomotion in rodents.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".