Ascending propriospinal modulation of thoracic sympathetic preganglionic neurons during lumbar locomotor activity
Bibliographic record
Abstract
ABSTRACT Although the autonomic sympathetic system is activated in parallel with locomotion, the underlying neural mechanisms mediating this coordination are not completely understood. Descending exercise or ‘central command’ signals from hypothalamic and brainstem regions are thought to activate thoracic spinal sympathetic neurons in parallel with descending locomotor commands. In turn, subsets of thoracic sympathetic preganglionic neurons (SPNs) then increase activation of a constellation of tissues and organs that provide homeostatic and metabolic support during movement and exercise. It is known that neurons within the spinal cord (propriospinal networks) can generate well-coordinated and sustained locomotor activity but whether these propriospinal networks contribute to coordination between locomotor and autonomic systems is unknown. To investigate this, we applied neurochemicals to elicit whole-cord or lumbar-evoked locomotor activity in an in vitro spinal cord preparation, simultaneously recording lumbar ventral root (VR) activity and changes in calcium fluorescence of pre-labelled SPNs in thoracic segments. Using whole-bath drug application to elicit hindlimb locomotor activity, recorded SPN responses were increased in rostral (T4 – T7) compared to caudal (T8 – T11) segments. When locomotor-inducing neurochemicals were applied only to the lumbar region using a split-bath configuration, SPN population responses were increased in rostral (T4-7) but not caudal (T8-9) segments during both tonic and rhythmic VR activity. In both approaches, the greatest numbers of SPNs with increased fluorescence during rhythmic activity were in T6/7, whereas the greatest numbers with unchanged or decreased fluorescence were in caudal segments (T8-T11). Together these findings reveal a strong ascending lumbar to thoracic integrating communication pathway and may represent a key feature of spinal neural network function normally. Such communication pathways should be further investigated for targeted autonomic function(s) activation and therapeutic benefit after spinal cord injury.
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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 | 0.001 |
| 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".