Age- and speed-dependent modulation of gaits in DSCAM<sup>2J</sup>mutant mice
Bibliographic record
Abstract
Gaits depend on the interplay between distributed spinal neural networks, termed central pattern generators, generating rhythmic and coordinated movements, primary afferents, and descending supraspinal inputs. Recent studies demonstrated that the mouse displays a rich repertoire of gaits. Changes in gaits occur in mutant mice lacking particular neurons or molecular signaling pathways implicated in the normal establishment of these neural networks. Given the role of the Down syndrome cell adherence molecule (DSCAM) to the formation and maintenance of spinal interneuronal circuits and sensorimotor integration, we have investigated its functional contribution to gaits over a wide range of locomotor speeds using freely walking mice. We show in this study that the DSCAM2Jmutation, while not precluding any gait, impairs the age- and speed-dependent modulation of gaits. It impairs the ability of mice to maintain their locomotion at high treadmill speeds. DSCAM2Jmutation induces the dominance of lateral walk over trot and the emergence of aberrant gaits for mice, such as pace and diagonal walk. Gaits were also more labile in DSCAM2Jmutant mice, i.e., less stable, less attractive, and less predictable than in their wild-type littermates. Our results suggest that the DSCAM mutation affects the behavioral repertoire of gaits in an age- and speed-dependent manner. NEW & NOTEWORTHY Gaits evolve throughout development, up to adulthood, and according to the genetic background. Using mutant mice lacking DSCAM (a cell adherence molecule associated with Down syndrome), we show that the DSCAM2Jmutation alters the repertoire of gaits according to the mouse’s age and speed, and prevents fast gaits. Such an incapacity suggests a reorganization of spinal, propriospinal, and supraspinal neuronal circuits underlying locomotor control in DSCAM2Jmutant mice.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".