P.122 Unbiased whole brain circuit interrogation reveals neurons restoring walking after spinal cord injury
Bibliographic record
Abstract
Background: There is presently no cure for locomotor deficits after spinal cord injury (SCI). Very few therapies effectively target the brain due to poor understanding of the brain’s role post-SCI. Newly developed tissue clearing techniques have permitted unbiased three-dimensional circuit analysis, opening new opportunities for SCI-related brain interrogation. Methods: We established a novel brain interrogation pipeline by optimizing mouse brain clearing, imaging, and atlas registration. We leveraged a spontaneous recovery lateral hemisection model to analyze whole brain cell activity and connectivity with the lumbar cord using cFos immunolabelling and virus-mediated projection tracing. We identified a functionally and anatomically dynamic region correlating with recovery and interrogated its locomotor role with optogenetics. We assessed deep brain electrical stimulation (DBS) of this region in a more clinically relevant rat contusion SCI using an established bipedal robotic interface. Results: We unexpectedly uncovered the lateral hypothalamus (LH) to functionally and anatomically correlate with recovery. LHVglut2 optogenetic stimulation significantly augmented locomotor function. LH DBS in rats acutely robustly augmented bipedal locomotion post-SCI. Conclusions: This is the first demonstration of the LH’s role in locomotion post-SCI and is a novel DBS target that robustly augmented locomotor function, dependent on LH glutamatergic cells. LH DBS may be a promising intervention in humans.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".