Deep brain stimulation of midbrain locomotor circuits in the freely moving pig
Bibliographic record
Abstract
BACKGROUND: Deep brain stimulation (DBS) of the mesencephalic locomotor region (MLR) has been studied as a therapeutic target in rodent models of stroke, parkinsonism, and spinal cord injury. Clinical DBS trials have targeted the closely related pedunculopontine nucleus in patients with Parkinson's disease as a therapy for gait dysfunction, with mixed reported outcomes. Recent studies suggest that optimizing the MLR target could improve its effectiveness. OBJECTIVE: We sought to determine if stereotaxic targeting and DBS in the midbrain of the pig, in a region anatomically similar to that previously identified as the MLR in other species, could initiate and modulate ongoing locomotion, as a step towards generating a large animal neuromodulation model of gait. METHODS: We implanted Medtronic 3389 electrodes into putative MLR structures in Yucatan micropigs to characterize the locomotor effects of acute DBS in this region, using EMG recordings, joint kinematics, and speed measurements on a manual treadmill. RESULTS: MLR DBS initiated and augmented locomotion in freely moving micropigs. Effective locomotor sites centered around the cuneiform nucleus and stimulation frequency controlled locomotor speed and stepping frequency. Off-target stimulation evoked defensive and aversive behaviors that precluded locomotion in the animals. CONCLUSION: Pigs appear to have an MLR and can be used to model neuromodulation of this gait-promoting center. These results indicate that the pig is a useful model to guide future clinical studies for optimizing MLR DBS in cases of gait deficiencies associated with such conditions as Parkinson's disease, spinal cord injury, or stroke.
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".