The Effects of Spinal Manipulation on Central Integration of Dual Somatosensory Input Observed After Motor Training: A Crossover Study
Bibliographic record
Abstract
OBJECTIVE: This study sought to investigate the influence of spinal dysfunction and spinal manipulation on the response of the central nervous system to a motor training task. METHODS: The dual peripheral nerve stimulation somatosensory evoked potential (SEP) ratio technique was used in 11 subjects before and after a 20-minute typing task and again when the typing task was preceded with cervical spine manipulation. Somatosensory evoked potentials were recorded after median and ulnar nerve stimulation at the wrist (1 millisecond square wave pulse, 2.47 Hz, 1x motor threshold). The SEP ratios were calculated for the N9, N11, N13, P14-18, N20-P25, and P22-N30 peak complexes from SEP amplitudes obtained from simultaneous median and ulnar (MU) stimulation divided by the arithmetic sum of SEPs obtained from individual stimulation of the median (M) and ulnar (U) nerves. RESULTS: There was a significant increase in the MU/M+U ratio for both cortical (ie, N20-P25 and P22-N30) SEP components after the 20-minute repetitive contraction task. This did not occur when the motor training task was preceded with spinal manipulation. Instead, there was a significant decrease in the MU/M+U ratio for the cortical P22-N30 SEP component. The ratio changes appear to be due to changes in the ability to suppress the dual input as concurrent changes in the MU amplitudes were observed. DISCUSSION: This study suggests that cervical spine manipulation not only alters cortical integration of dual somatosensory input but also alters the way the central nervous system responds to subsequent motor training tasks. CONCLUSION: These findings may help to clarify the mechanisms responsible for the effective relief of pain and restoration of functional ability documented after spinal manipulation and the mechanism involved in the initiation of overuse injuries.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".