Deficits in forelimb reach learning in a mouse model of Fragile X syndrome
Bibliographic record
Abstract
Fragile X syndrome is a leading cause of intellectual disability and autism spectrum disorder, for which therapies are limited. A mouse model of Fragile X syndrome, the Fmr1 knockout (KO) mouse, has been particularly valuable for interrogating the molecular, cellular, and circuit mechanisms that underlie the neurological deficits seen in this syndrome. Key deficits in Fragile X syndrome include impairments in social behaviors, cognition, and motor learning. Given the difficulties in extrapolating more complex human behaviors to mouse models, simple motor behaviors are a particularly tractable form of learning to study in the mouse. We investigated a form of forelimb reach learning in Fmr1 KO mice, precisely quantifying different parameters of the task using both manual analysis and DeepLabCut-based tracking of reach trajectories. While Fmr1 KO mice show impaired learning overall, our results demonstrated that the presence or absence of a cue that signals reward alleviates some of the deficits. In addition to a single metric of success in learning, we determined the specific parameters of the motor behavior that were responsible for that success or failure. In particular, our results suggested that Fmr1 KO mice showed impaired improvement in the trajectory of the reach, reflected by a greater likelihood of completely missing the target, and in a lower learning index for the optimal reach trajectory. In addition, we fully described the features underlying learning, including categorizing the first attempt during trials, failed reaches where mice make contact with the reward, the number of trials where no attempts were made, as well as how the pattern of these different behaviors varies in Fmr1 KO mice. Our findings provide an essential framework for linking specific behavioral impairments in motor learning to the cellular and circuit mechanisms that support them.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".