Short-latency afferentinhibition in chronic spinalcord injury
Bibliographic record
Abstract
BACKGROUND: Short-latency afferent inhibition (SAI) results when somatosensory afferent input inhibits the corticospinal output from primary motor cortex (M1). The present study examined SAI in the flexor carpi radialis (FCR) muscle in individuals with spinal cord injury (SCI) and uninjured controls. METHODS: Short-latency afferent inhibition (SAI) was evoked by stimulating the median nerve at the elbow at intervals of 15, 20 and 25 ms in advance of a transcranial magnetic stimulation (TMS) pulse over M1. SAI was tested with the FCR at rest and also during ~20% of maximum voluntary contraction. Corticospinal output was assessed through measuring both motor thresholds and motor evoked potential (MEP) recruitment curves. The afferent volley was assessed via the N20-P25 amplitude of the somatosensory evoked potential (SEP) and the amplitude of sensory nerve action potentials (SNAP) recorded over the median nerve at the elbow. RESULTS: SAI is reduced in SCI in both the contracted and non-contracted FCR muscle. MEP recruitment curves and thresholds were decreased in SCI only in the active state and not the resting state. N20-P25 amplitude was similar between groups in both the resting and active states although SNAP was significantly reduced in SCI at rest. CONCLUSIONS: We conclude that reduced SAI in SCI is likely attributed to neuroplasticity altering the intrinsic M1 circuitry mediating SAI and/or reduced afferent input traversing a direct thalamocortical route to M1. These data provide a new avenue of research aimed at identifying therapeutic approaches to alter SAI to improve upper limb function in individuals with SCI.
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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 | 0.000 |
| 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".