Quantitative and Qualitative Impairments in Skilled Reaching in the Mouse ( <i>Mus musculus</i> ) After a Focal Motor Cortex Stroke
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: Skilled reaching movements are an important aspect of human motor behavior but are impaired after motor system stroke. The purpose of this study was to document skilled movements in mice before and after a focal motor cortex stroke for the purpose of developing a mouse model of human stroke. METHODS: Male C57/BL6 mice were trained to reach with a forelimb for food pellets and then given a motor cortex stroke, induced by pial stripping, contralateral to their preferred reaching limb. Reaching success and the movements used in reaching were analyzed by frame-by-frame inspection of presurgical and postsurgical video records. RESULTS: Reaching success was severely impaired after the stroke. Improvement in success over 2 postsurgical weeks was moderate. Analysis of 10 movement components comprising reaches pre- and postsurgically indicated that most of the rotatory movements of the limb used for aiming, advancing, pronating, and supinating the paw were impaired. When successful reaches did occur, body movements that compensated for the impairments in limb rotatory movements aided them. CONCLUSIONS: The results indicate that skilled reaching in the mouse is impaired by focal motor cortex stroke and they suggest that the mouse, and the skilled reaching task, provides an excellent model for studying impairments, compensation, and recovery after motor system stroke.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".