Characteristics and Mechanisms of Locomotion Induced by Intraspinal Microstimulation and Dorsal Root Stimulation in Spinal Cats
Bibliographic record
Abstract
Intraspinal microstimulation (ISMS) through a single microelectrode can induce locomotion in cats spinalized at T(13) 1 wk before (untrained) or after 3-5 wk of treadmill training. Here we study the optimal parameters of ISMS and the characteristics of locomotion evoked. ISMS was applied in the dorsal region of segments L(3)-S(1) at different lateralities (midline to 2.5 mm) and after an intravenous injection of clonidine (noradrenergic agonist). Kinematics and electromyographic recordings were used to characterize locomotion. ISMS could induce a bilateral locomotor pattern similar to that obtained with perineal stimulation, and the characteristics of locomotion varied according to the spinal segment stimulated. Mechanisms by which ISMS could evoke locomotion were then investigated by stimulating, inactivating, or lesioning different spinal structures. Dorsal root stimulation (DRS), just like ISMS, could evoke a variety of ipsi- and bilateral nonlocomotor movements as well as locomotor responses. This suggests that sensory afferent pathways are involved in the production of locomotion by ISMS. Microinjections of yohimbine (noradrenergic antagonist) in L(3) and L(4) segments or a complete second spinal lesion at L(3)-L(4) abolished all locomotor activity evoked by ISMS applied at more caudal segments. Progressive dorsoventral spinal lesions at L(3) or L(4) and restricted ventral lesions at L(4) further suggest that the integrity of the ventral or ventrolateral funiculi as well as the L(3)-L(4) segments are critical for the induction of locomotion by ISMS at L(5) to S(1) or by DRS at these caudal segments.
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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) | 0.000 | 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 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".