Bibliographic record
Abstract
Once in a while, I spot a mouse on a trail eagerly tiptoeing by before fleeing as I get closer. Sometimes, as the episode unfolds, I wonder how animals, including humans, control whether to walk in a leisurely fashion or to run for it. This question has also occurred to Nicolas Josset and his colleagues at Université Laval, Canada, and so the team decided to study a midbrain region in mice that sits atop the spinal cord and is highly conserved from fish to humans. When the region is electrically stimulated in animals, it sets the pace at which the animal moves like a volume knob: ramping up the intensity speeds up the animal so that it goes from a walking pattern into a run. While recent work in mice has clarified how neurons in the spinal cord set the speed and pattern of locomotion, the neural input from the brain remains poorly understood. To clarify how this region sets the pace, the team studied neurons in two separate areas of the region: the cuneiform nucleus and the pedunculopontine nucleus.To isolate each area's contribution to locomotor control, the team genetically modified neurons in mice to turn on when blue light was shone on them or to turn off with yellow light. To target each area's neurons, the team surgically inserted a 0.2 mm light probe into either area. In a series of experiments, Josset and his colleagues watched the response of the mice as they shone light in the areas – thereby turning on or off the neurons – for either short 10 ms periods or longer 1 s periods, while the mice sat still or matched the speed of a treadmill. To assess the responses of the mice, the team put reflective markers on the leg joints and filmed the mice on the treadmill to track their speed and whether they walked or ran.When the team stimulated the cuneiform neurons of a stationary mouse with a sequence of 10 ms bursts of blue light for a total of 1 s, the mouse set off on a short bout of running. However, when the team activated pedunculopontine neurons, the mouse remained at rest. This shows that neurons in the cuneiform nucleus are important for initiating locomotion.Next, the team tested how each area modifies locomotion. They activated neurons for 50 ms while the mice were running at a set speed on a treadmill and found that cuneiform neurons briefly sped up the mouse, while pedunculopontine neurons briefly slowed it down. When the team prolonged the activation to a sequence of 10 ms intervals for 1 s, the cuneiform neurons set the mouse into a fast running pattern, while the pedunculopontine neurons halted the mouse altogether. In a final experiment, the team tested whether the areas are necessary for locomotion by turning off the neurons with yellow light for 1 s. Turning off either cuneiform or pedunculopontine neurons slowed down or stopped the mice. This suggests that the cuneiform and pedunculopontine neurons have two different roles: cuneiform neurons initiate locomotion, increase speed and induce running, while pedunculopontine neurons decrease speed and regulate slow walking.Josset and colleagues’ results demonstrate that the midbrain comprises two areas that control either walking or running. As the midbrain region is conserved in species from lamprey to salamander, rabbit and monkeys, the two areas might apply their distinct functions similarly across vertebrates. Also, the team's results suggest that targeting neurons in the cuneiform nucleus, which speeds up locomotion, could help boost the effects of electrical brain stimulation in people with Parkinson's disease, which is used to alleviate the slowed movements that are a common symptom of the 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".