Control of Forelimb and Hindlimb Movements and Their Coordination during Quadrupedal Locomotion across Speeds in Adult Spinal Cats
Bibliographic record
Abstract
Coordinating the four limbs is critical for terrestrial mammalian locomotion. Thoracic spinal transection abolishes neural communication between the brain and spinal networks controlling hindlimb/leg movements. Several studies have shown that animal models of spinal transection (spinalization), such as mice, rats, cats, and dogs recover hindlimb locomotion with the forelimbs stationary or suspended. We know less on the ability to generate quadrupedal locomotion after spinal transection, however. We collected kinematic and electromyography data in four adult cats during quadrupedal locomotion at five treadmill speeds before (intact cats) and after low-thoracic spinal transection (spinal cats). We show that adult spinal cats performed quadrupedal treadmill locomotion and modulated their speed from 0.4 m/sec to 0.8 m/sec but required perineal stimulation. During quadrupedal locomotion, several compensatory strategies occurred, such as postural adjustments of the head and neck and the appearance of new coordination patterns between the forelimbs and hindlimbs, where the hindlimbs took more steps than the forelimbs. We also observed temporal changes, such as shorter forelimb cycle/swing durations and shorter hindlimb cycle/stance durations in the spinal state. Forelimb double support periods occupied a greater proportion of the cycle in the spinal state, and hindlimb stride length was shorter. Coordination between the forelimbs and hindlimbs was weakened and more variable in the spinal state. Changes in muscle activity reflected spatiotemporal changes in the locomotor pattern. Despite important changes in the pattern, our results indicate that biomechanical properties of the musculoskeletal system play an important role in quadrupedal locomotion and offset some of the loss in neural communication between networks controlling the forelimbs and hindlimbs after spinal transection.
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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) | 0.000 | 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) | 0.001 | 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".