A new conceptual framework for the integrated neural control of locomotor and sympathetic function: implications for exercise after spinal cord injury
Bibliographic record
Abstract
All mammals, including humans, are designed to produce sustained locomotor movements. Many higher centres are involved in movement, but ultimately these centres act upon a core "rhythm-generating" network within the brainstem-spinal cord. In addition, endurance-based locomotor exercise requires sympathetic neural support to maintain homeostasis and to provide needed metabolic resources. This review focuses on the roles and integration of these 2 neural systems. Part I reviews the cardiovascular, thermoregulatory, and metabolic functions under spinal sympathetic control as revealed by spinal cord injury at different levels. Part II examines the integration between brainstem-spinal sympathetic pathways and the neural circuitry producing motor rhythms. In particular, the rostroventral medulla (RVM) contains the neural circuitry that (i) integrates heart rate, contractility, and blood flow in response to postural changes; (ii) initiates and maintains cardiovascular adaptations for exercise; (iii) provides direct descending innervation to preganglionic neurons innervating the adrenal glands, white adipose tissue, and tissues responsible for cooling the body; (iv) integrates descending sympathetic drive for energy substrate mobilization (lipolysis); and (v) is the relay for descending locomotor commands arising from higher brain centres. A unifying conceptual framework is presented, in which the RVM serves as the final descending supraspinal "exercise integration centre" linking the descending locomotor command signal with the metabolic and homeostatic support needed to produce prolonged rhythmic activities. The role and rationale for an ascending sympathetic and locomotor drive from the lower to upper limbs within this framework is presented. Examples of new research directions based on this unifying framework are discussed.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.014 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".