Origins of Abnormal Excitability in Biceps Brachii Motoneurons of Spastic-Paretic Stroke Survivors
Bibliographic record
Abstract
Stroke survivors often exhibit abnormal motoneuron excitability, manifested clinically as spasticity with exaggerated stretch reflexes in resting muscles. We examined whether this abnormal excitability is a result of increased activation of intrinsic voltage-dependent persistent inward currents (PICs) or whether it is a result of enhanced synaptic inputs to the motoneuron. This distinction was made by recording firing rate profiles of pairs of motor units during isometric contractions of elbow flexor muscles. To estimate PIC amplitude, the discharge of the lower-threshold (reporter) motor unit of the pair was used to estimate the synaptic input to the higher-threshold (test) motor unit. The estimated synaptic input required to recruit the test unit was compared with the synaptic input when the test unit was derecruited (DeltaF) and this served as an estimate of the intrinsic (PIC) contribution to motoneuron firing. We found that PIC estimates were not larger in spastic-paretic motoneurons (DeltaF = 4.0 +/- 1.6 pps) compared with contralateral (4.6 +/- 1.4 pps) and age-matched healthy control motoneurons (3.8 +/- 1.7, all P > 0.1). Instead, following the voluntary contractions, the majority of lower-threshold motor units in spastic-paretic muscles (83%) exhibited spontaneous discharge, compared with 14% of contralateral and 0% of control motor units. Furthermore, there was strong co-modulation of simultaneously active units in spastic muscle. The presence of ongoing, correlated unit activity at "rest," coupled with firing behavior at recruitment unique to lower-threshold motor units in spastic muscles, suggested that firing changes are likely a result of a low-level depolarizing synaptic drive to the resting motoneuron pool.
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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) | 0.001 | 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".