Water-Reaching Platform for Longitudinal Assessment of Cortical Activity and Fine Motor Coordination Defects in a Huntington Disease Mouse Model
Why this work is in the frame
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Bibliographic record
Abstract
Huntington disease (HD), caused by dominantly inherited expansions of a CAG repeat results in characteristic motor dysfunction. Although gross motor defects have been extensively characterized in multiple HD mouse models using tasks such as rotarod and beam walking, less is known about forelimb deficits. We develop a high-throughput alternating reward/nonreward water-reaching task and training protocol conducted daily over approximately two months to simultaneously monitor forelimb impairment and mesoscale cortical changes in GCaMP activity, comparing female zQ175 (HD) and wild-type (WT) littermate mice, starting at ∼5.5 months. Behavioral analysis of the water-reaching task reveals that HD mice, despite learning the water-reaching task as proficiently as wild-type mice, take longer to learn the alternating event sequence as evident by impulsive (noncued) reaches and initially display reduced cortical activity associated with successful reaches. At this age gross motor defects determined by tapered beam assessment were not apparent. Although wild-type mice displayed no significant changes in cortical activity and reaching trajectory throughout the testing period, HD mice exhibited an increase in cortical activity, especially in the secondary motor and retrosplenial cortices, over time, as well as longer and more variable reaching trajectories by approximately seven months. HD mice also experienced a progressive reduction in successful performance. Tapered beam and rotarod tests as well as reduced DARPP-32 expression (striatal medium spiny neuron marker) after water-reaching assessment confirmed HD pathology. The water-reaching task can be used to inform on a daily basis, HD and other movement disorder onset and manifestation, therapeutic intervention windows, and test drug efficacy.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it