Alpha to tau: mapping the neuromechanical continuum from α-motor neuron firing behavior to joint torque
Bibliographic record
Abstract
Human movement emerges from the coordinated interaction between the central nervous system (CNS) and the musculoskeletal system. At the heart of this interaction lies the motor unit (MU), the smallest functional entity of musculoskeletal force generation, comprising an α-motor neuron (MN) and the muscle fibers it innervates. While substantial research has been dedicated to both α-MNs and innervated skeletal fibers, the precise mechanisms by which MN activity is converted into skeletal mechanical force remain incompletely understood. An important element hampering progress is the limited understanding of how differences in MU's firing and contractile properties impact musculoskeletal force generation in intact humans in vivo. These variations stem from intrinsic physiological differences among individuals and are further shaped by factors such as physical training, injury, or disease. Therefore, a deeper comprehension of this excitation-contraction coupling and its consequent impact on force capacity is crucial for creating tailored rehabilitation protocols that are finely adjusted to the unique physiological conditions of each individual. This dissertation introduces novel methodologies to explore the response of α-MNs to external electrical stimuli in incomplete spinal cord injury individuals, and their interaction with muscles to produce mechanical force. This research endeavor aims at reconciling neuronal activity with the resultant musculoskeletal mechanics. Moreover, the techniques proposed are designed with computational efficiency in mind, broadening the horizon for their application in real-time settings. Our MU-centered approach comprised four main components. First, we decoded MU firing events via high-density electromyography (EMG) decomposition (Chapters 2-4). Second, we sampled temporal and spectral firing characteristics of the sampled MUs in healthy (Chapters 3 and 4) and motor-impaired individuals (Chapter 2). Third, we established a link between MU firing patterns and their corresponding twitch responses, which facilitates the creation of MU-specific activation dynamics for individual muscles (Chapters 3 and 4). This achievement was central for the integration of MU firing function with personalized neuro-musculoskeletal models, thus providing a comprehensive view of the interplay between neuronal and musculoskeletal systems (Chapter 3 and Chapter 4). Fourth, we extended the state-of-the-art decomposition techniques to enrich the identification of MN firing events in high-force contractions and locomotive activities. This breakthrough not only directs our approaches toward tasks that embody the diverse challenges of daily life but also holds profound implications for their application in rehabilitation settings.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".