A toolbox for automated video analysis of rodents engaged in string-pulling: Phenotyping motor behavior of mice for sensory, whole-body and bimanual skilled hand function
Bibliographic record
Abstract
Abstract String-pulling in rodents (rats and mice) is a task in which animals make hand-over-hand movements to spontaneously reel in a string with or without a food reward attached to its end. The task elicits bilateral skilled hand movements for which rodents require little training. The task is suitable for phenotyping physiology and pathophysiology of sensorimotor integration in rodent models of neurological and motor disorders. Because a rodent stands in the same location and its movements are repetitive, the task lends itself to quantification of topographical and kinematic parameters for on-line tactile tracking of the string, skilled hand movements for grasping, and rhythmical bilateral forearm movements to advance the string. Here we describe a Matlab ® based software with a graphical user interface to assist researchers in analyzing the video record of string pulling. The software allows global characterization of position and motion using optical flow estimation, descriptive statistics, principal component, and independent component analyses as well as temporal measures of Fano factor, entropy, and Higuchi fractal dimension. Based on image segmentation and object tracking heuristic algorithms, the software also allows independent tracking of the body, ears, nose, and forehands for estimation of kinematic parameters such as body length, body angle, head roll, head yaw, head pitch, movement paths and speed of hand movement. The utility of the task and that of the software is presented by describing mouse strain characteristics in string-pulling behavior of two strains of mice, C57BL/6 and Swiss Webster. Postural and skilled hand kinematic differences that characterize the strains highlight the utility of the task and assessment methods for phenotypic and neurological analysis of healthy and rodent models of diseases such as Parkinson’s, Huntington’s, Alzheimer’s and other neurological and motor disorders. Significance statement Mouse models are used to investigate the physiology and pathophysiology of motor deficits observed in human neurological conditions, for testing substances for therapeutic drug development, and to investigate the role of neural systems and their genetic basis in the expression of behavior. Behavioral tasks involving unconditioned and natural behavior can provide rich insights into motor performance in animal models and analyses can be aided by the automated processing of video data for reliable quantification and high throughput.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.004 |
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".