Hypothalamic deep brain stimulation augments walking after spinal cord injury
Bibliographic record
Abstract
A spinal cord injury (SCI) disrupts the neuronal projections from the brain to the region of the spinal cord that produces walking, leading to various degrees of paralysis. Here, we aimed to identify brain regions that steer the recovery of walking after incomplete SCI and that could be targeted to augment this recovery. To uncover these regions, we constructed a space–time brain-wide atlas of transcriptionally active and spinal cord-projecting neurons underlying the recovery of walking after incomplete SCI. Unexpectedly, interrogation of this atlas nominated the lateral hypothalamus (LH). We demonstrate that glutamatergic neurons located in the LH (LHVglut2) contribute to the recovery of walking after incomplete SCI and that augmenting their activity improves walking. We translated this discovery into a deep brain stimulation therapy of the LH (DBSLH) that immediately augmented walking in mice and rats with SCI and durably increased recovery through the reorganization of residual lumbar-terminating projections from brainstem neurons. A pilot clinical study showed that DBSLH immediately improved walking in two participants with incomplete SCI and, in conjunction with rehabilitation, mediated functional recovery that persisted when DBSLH was turned off. There were no serious adverse events related to DBSLH. These results highlight the potential of targeting specific brain regions to maximize the engagement of spinal cord-projecting neurons in the recovery of neurological functions after SCI. Further trials must establish the safety and efficacy profile of DBSLH, including potential changes in body weight, psychological status, hormonal profiles and autonomic functions. Whole-brain anatomical and activity surveys identify the lateral hypothalamus as a key driver of recovery from spinal cord injury, leading to a deep brain stimulation therapy that augments the recovery of walking 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 | 0.000 |
| 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".