Bibliographic record
Abstract
In 1966, Shik, Severin and Orlovskii discovered that electrical stimulation of a region at the junction between the midbrain and hindbrain elicited controlled walking and running in the cat. The region was named Mesencephalic Locomotor Region (MLR). Since then, this locomotor center was shown to control locomotion in various vertebrate species, including the lamprey, salamander, stingray, rat, guinea-pig, rabbit or monkey. In human subjects asked to imagine they are walking, there is an increased activity in brainstem nuclei corresponding to the MLR (i.e. pedunculopontine, cuneiform and subcuneiform nuclei). Clinicians are now stimulating (deep brain stimulation) structures considered to be part of the MLR to alleviate locomotor symptoms of patients with Parkinson's disease. However, the anatomical constituents of the MLR still remain a matter of debate, especially relative to the pedunculopontine, cuneiform and subcuneiform nuclei. Furthermore, recent studies in lampreys have revealed that the MLR is more complex than a simple relay in a serial descending pathway activating the spinal locomotor circuits. It has multiple functions. Our goal is to review the current knowledge relative to the anatomical constituents of the MLR, and its physiological role, from lamprey to man. We will discuss these results in the context of the recent clinical studies involving stimulation of the MLR in patients with Parkinson's disease.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".