String-Pulling as a Behavioral Assessment of Skilled Forelimb Motor Function in a Middle Cerebral Artery Occlusion Rat Model
Bibliographic record
Abstract
Abstract Stroke is a leading cause of long-term disability in humans and frequently results in bilateral impairments in fine motor control. Many behavioral tasks used to assess rodent models of stroke evaluate a single limb; however, recent work has demonstrated that bilateral hand-over-hand movements used to pull in a string assess skilled movement of both hands. Devascularization focused on the forelimb portion of sensorimotor cortex has been observed to produce persistent disruptions in the topographical organization of string-pulling behavior. The current study examined changes in string-pulling after a more clinically relevant rodent model of stroke via middle cerebral artery occlusion (MCAO). Detailed movement analyses revealed disruptions in the bilateral organization of string-pulling and fine motor control of both hands. Rats missed the string more often with both hands, and when the string was missed on the impaired side, rats continued to cycle through subcomponents of string-pulling behavior as if the string were grasped in the hand. Rats also failed to make a grasping motion with the impaired hand when the string was missed and instead, demonstrated an open-handed raking-like motion. No differences were found in time to approach or to complete the string-pulling task to obtain a reward, demonstrating the importance of using a detailed functional analysis of movement to detect changes in performance. String-pulling behavior is sensitive at detecting changes in bilateral rhythmical hand control following MCAO providing a foundation for future work to investigate other models of stroke and to evaluate the efficacy of therapeutic interventions that enhance neuroplasticity.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".