Home cage-based insights into motor learning and strategy adaptation in a Huntington disease mouse model
Bibliographic record
Abstract
Huntington disease (HD) is a genetic neurodegenerative disorder characterized by progressive motor dysfunction, cognitive decline, and neuropsychiatric symptoms. Assessing early motor skill deficits in HD mouse models is challenging with traditional behavioral tasks. This study uses a home cage-based lever-pulling task, PiPaw2.0, to evaluate motor learning in 6-7 months-old zQ175 knock-in HD mice in a more naturalistic environment. In this task, mice learn to pull a lever for a water reward, with the requirement to hold the lever within a specific goal range for a required hold time. As the mice improved, the required hold time increased, thereby gradually increasing the task demands. Both wild type (WT) and zQ175 mice initially showed similar task engagement, but zQ175 mice had significant deficits in adapting to increasing hold time. The WT mice refined their strategies over time, shifting from random to more precise lever pulls, while zQ175 mice failed to make this adjustment, maintaining erratic performance. Additionally, in group-housing WT mouse lever performance benefited from peer interactions, an effect absent in zQ175 mice. Post-task neural assessments revealed that WT mice developed experience-mediated synaptic plasticity in the left striatum (contralateral to lever-pulling paw), while zQ175 mice showed no significant changes, consistent with known corticostriatal plasticity impairments in HD mouse models. In conclusion, our findings demonstrate the effectiveness of group-housed, home cage-based assessments for evaluating motor learning and adaptation in HD mouse models. This study provides insights into the motor control and adaptive learning deficits in HD, emphasizing the value of automated home cage systems in advancing neurodegenerative disease research and highlighting the importance of peer influences on performance.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".