Plasticity of Reflexes From the Foot During Locomotion After Denervating Ankle Extensors in Intact Cats
Bibliographic record
Abstract
Although sensory feedback is important in regulating the timing and magnitude of muscle activity during locomotion few studies have evaluated how it changes after peripheral nerve lesions. To assess this, reflexes evoked by stimulating a nerve before and after denervating other nerves can be quantified to determine changes. The aim of this study was to investigate consequences of denervating ankle extensor muscles, the lateral gastrocnemius, and soleus (LGS) on reflexes from the plantar foot surface evoked by stimulating the tibialis (Tib) nerve. Three cats (n = 3) were trained to walk on a treadmill and chronically implanted with electrodes in 14 hindlimb muscles bilaterally to record EMG activity. A stimulating cuff electrode was placed around the left Tib nerve (Tib) nerve at the ankle to evoke reflexes. Several control values of EMGs, limb kinematics, and Tib nerve reflexes were obtained during locomotion for at least 3 wk before the left LGS nerve was cut. We found that the locomotor EMG bursts of several muscles was altered, with a large increase in amplitude in the early days postneurectomy followed by a gradual decrease toward intact values later on. There were changes in the stimulated locomotor EMG bursts (Tib nerve reflexes) of ipsilateral flexors and extensors and of contralateral ankle extensors, which dissociated from changes in baseline locomotor EMG (e.g., nonstimulated bursts during reflex trials). The functional significance of these changes in muscle activity and reflex pathways on the recovery of locomotion after denervating ankle extensors is discussed.
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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 | 0.001 |
| 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".