Functional Contribution of Mesencephalic Locomotor Region Nuclei to Locomotor Recovery After Spinal Cord Injury
Bibliographic record
Abstract
Abstract Spinal cord injury (SCI) results in a disruption of information between the brain and the spinal locomotor circuit. Although the spinal cord contains all the neural circuits to generate locomotion, people with SCI are unable to walk due to the absence of descending commands from the brain. Electrical stimulation of supraspinal locomotor centers, such as the Mesencephalic Locomotor Region (MLR), can promote locomotor recovery in acute and chronic SCI rodent models. Although clinical trials are currently underway in SCI patients, there is still debate about the organization of this supraspinal locomotor center and which anatomical correlate of the MLR should be targeted to promote functional recovery. Combining kinematics, electromyographic recordings, anatomical analysis, and mouse genetics, our study reveals that glutamatergic neurons of the cuneiform nucleus contribute to locomotor recovery by enhancing motor efficacy in flexor and extensor hindlimb muscles, and by increasing locomotor rhythm and speed on a treadmill, over ground, and during swimming in mice with chronic SCI. In contrast, glutamatergic neurons of the pedunculopontine nucleus slow down locomotion. Therefore, our study identifies the cuneiform nucleus and its glutamatergic neurons as a therapeutical target to improve locomotor recovery in patients living with SCI. One Sentence Summary Glutamatergic neurons of the mesencephalic locomotor region contribute to spontaneous locomotor recovery following spinal cord injury and selective activation of a discrete glutamatergic subpopulation in this region can further improve functional outcome in chronic spinal cord injury.
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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) | 0.004 | 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".