Post-stroke kinematic analysis in rats reveals similar reaching abnormalities as humans
Bibliographic record
Abstract
A coordinated pattern of multi-muscle activation is essential to produce efficient reaching trajectories. Disruption of these coordinated activation patterns, termed synergies, is evident following stroke and results in reaching deficits; however, preclinical investigation of this phenomenon has been largely ignored. Furthermore, traditional outcome measures of post-stroke performance seldom distinguish between impairment restitution and compensatory movement strategies. We sought to address this by using kinematic analysis to characterize reaching movements and kinematic synergies of rats performing the Montoya staircase task, before and after ischemic stroke. Synergy was defined as the simultaneous movement of the wrist and other proximal forelimb joints (i.e. shoulder, elbow) during reaching. Following stroke, rats exhibited less individuation between joints, moving the affected limb more as a unit. Moreover, abnormal flexor synergy characterized by concurrent elbow flexion, shoulder adduction, and external rotation was evident. These abnormalities ultimately led to inefficient and unstable reaching trajectories, and decreased reaching performance (pellets retrieved). The observed reaching abnormalities in this preclinical stroke model are similar to those classically observed in humans. This highlights the potential of kinematic analysis to better align preclinical and clinical outcome measures, which is essential for developing future rehabilitation strategies following 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) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".