Nigral Glutamatergic Neurons Control the Speed of Locomotion
Bibliographic record
Abstract
The mesencephalic locomotor region (MLR) plays a crucial role in locomotor control. In vertebrates, stimulation of the MLR at increasing intensities elicits locomotion of growing speed. This effect has been presumed to result from higher brain inputs activating the MLR like a dimmer switch. Here, we show in lampreys ( Petromyzon marinus ) of either sex that incremental stimulation of a region homologous to the mammalian substantia nigra pars compacta (SNc) evokes increasing activation of MLR cells with a graded increase in the frequency of locomotor movements. Neurons co-storing glutamate and dopamine were found to project from the primal SNc to the MLR. Blockade of glutamatergic transmission largely diminished MLR cell responses and locomotion. Local blockade of D 1 receptors in the MLR decreased locomotor frequency, but did not disrupt the SNc-evoked graded control of locomotion. Our findings revealed the presence of a glutamatergic input to the MLR originating from the primal SNc that evokes graded locomotor movements. SIGNIFICANCE STATEMENT The mesencephalic locomotor region (MLR) plays a crucial role in the control of locomotion. It projects downward to reticulospinal neurons that in turn activate the spinal locomotor networks. Increasing the intensity of MLR stimulation produces a growing activation of reticulospinal cells and a progressive increase in the speed of locomotor movements. Since the discovery of the MLR some 50 years ago, it has been presumed that higher brain regions activate the MLR in a graded fashion, but this has not been confirmed yet. Here, using a combination of techniques from cell to behavior, we provide evidence of a new glutamatergic pathway activating the MLR in a graded fashion, and consequently evoking a progressive increase in locomotor output.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".