The Mesencephalic Locomotor Region: Multiple Cell Types, Multiple Behavioral Roles, and Multiple Implications for Disease
Bibliographic record
Abstract
The mesencephalic locomotor region (MLR) controls locomotion in vertebrates. In humans with Parkinson disease, locomotor deficits are increasingly associated with decreased activity in the MLR. This brainstem region, commonly considered to include the cuneiform and pedunculopontine nuclei, has been explored as a target for deep brain stimulation to improve locomotor function, but the results are variable, from modest to promising. However, the MLR is a heterogeneous structure, and identification of the best cell type to target is only beginning. Here, I review the studies that uncovered the role of genetically defined MLR cell types, and I highlight the cells whose activation improves locomotor function in animal models of Parkinson disease. The promising cell types to activate comprise some glutamatergic neurons in the cuneiform and caudal pedunculopontine nuclei, as well as some cholinergic neurons of the pedunculopontine nucleus. Activation of MLR GABAergic neurons should be avoided, since they stop locomotion or evoke bouts flanked with numerous stops. MLR is also considered a potential target in spinal cord injury, supranuclear palsy, primary progressive freezing of gait, or stroke. Better targeting of the MLR cell types should be achieved through optimized deep brain stimulation protocols, pharmacotherapy, or the development of optogenetics for human use.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".