Changes in hand muscle synergies in subjects with spinal cord injury: Characterization and functional implications
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: In order to guide and improve rehabilitation interventions for grip function after spinal cord injury (SCI), it is important to have a detailed understanding of the motor control strategies that the central nervous system uses to control the hand. We examined whether changes in the motor control of the hand after SCI are manifested in the form of changes to muscle synergies. We further sought to determine a correlation between functional ability and the extent of muscle synergy disruption. METHODS: Surface electromyographic (EMG) data were recorded from 8 hand muscles in 10 able-bodied subjects and 6 subjects with SCI as they performed various functional tasks using grip types relevant to activities of daily living. Muscle synergies were extracted using non-negative matrix factorization. Functional performance in each task was quantified using a 5-point clinical scale. RESULTS: The synergies most commonly observed in able-bodied subjects were co-activation of extensor digitorum communis and extensor indicis proprius, as well as of flexor digitorum superficialis with flexor carpi ulnaris. The proportion of subjects in which particular synergies occurred was significantly different for subjects with SCI compared to able-bodied subjects (P < 0.001). Deviations from the average able-bodied synergies in subject with SCI were found to be poorly correlated (r = -0.04) with functional ability. CONCLUSIONS: Results suggest that the disruptions and re-organizations of neural circuitry after SCI are reflected by the extracted muscle synergies, but the question of how muscle synergies can guide rehabilitation interventions remains open.
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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 | 0.000 |
| 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) | 0.002 | 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".