Subclinical Neck Pain Leads to Differential Changes in Early Somatosensory Evoked Potentials in Response to a Novel Force Matching Tracking Task
Bibliographic record
Abstract
Background: Neural adaptions in response to sensorimotor tasks are impaired in those with untreated, recurrent mild-to-moderate neck pain (subclinical neck pain (SCNP)), due to disordered central processing of afferent information (e.g., proprioception). Neural adaption to force modulation, a sensorimotor skill reliant on accurate proprioception, is likely to be impaired in those with SCNP. This study examined changes in somatosensory evoked potential (SEP) peak amplitudes following the acquisition of a novel force matching tracking task (FMTT) in those with SCNP compared to non-SCNP. Methods: 40 (20 female (F) & 20 male (M); average age (standard deviation, SD): 21.6 (3.01)) right-handed participants received controlled electrical stimulation at 2.47 Hz and 4.98 Hz (averaged 1000 sweeps/frequency) over the right-median nerve, to elicit SEPs before and after FMTT acquisition. Participants used their right thumb to match a series of force profiles that were calibrated to their right thumb (abductor pollicis brevis muscle) strength. To determine if motor learning was impacted, retention was assessed 24 to 48 hours later. Outliers were removed before running independent t-tests on normalized SEP peak amplitudes, and repeated measures analysis of variance (ANOVA) with planned contrasts on absolute and normalized motor performance accuracy. Benjamini-hochberg test was used to correct for multiple independent SEP comparisons. Results: SEP peaks: N18 (t(29.058) = 2.031, p = 0.026), N20 (t(35) = –5.460, p < 0.001), and P25 (t(33) = –2.857, p = 0.004) had group differences. Motor performance: Absolute error (n = 38) had a main effect of time, and significant pre-and post-acquisition contrast for time (both p < 0.001). Conclusions: Group differences in the olivary-cerebellar pathway (N18), and cortical processing at the somatosensory cortex (N20 and P25), suggests that SCNP alters cortical and cerebellar processing compared to non-SCNP in response to FMTT acquisition. The sensory-motor integration differences in the SCNP group suggests that those with SCNP may rely more on feedback loops for discrete sensorimotor tasks dependent on proprioception. Early SEP changes may be used as a marker for altered neuroplasticity in the context of motor skill acquisition of a novel discrete FMTT in those with SCNP.
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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 | 0.002 |
| 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) | 0.002 | 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".