Experimental environment improves the reliability of short-latency afferent inhibition
Bibliographic record
Abstract
Evidence indicates attention can alter afferent inhibition, a Transcranial Magnetic Stimulation (TMS) evoked measure of cortical inhibition following somatosensory input. When peripheral nerve stimulation is delivered prior to TMS, a phenomenon known as afferent inhibition occurs. The latency between the peripheral nerve stimulation dictates the subtype of afferent inhibition evoked, either short latency afferent inhibition (SAI) or long latency afferent inhibition (LAI). While afferent inhibition is emerging as a valuable tool for clinical assessment of sensorimotor function, the reliability of the measure remains relatively low. Therefore, to improve the translation of afferent inhibition within and beyond the research lab, the reliability of the measure must be improved. Previous literature suggests that the focus of attention can modify the magnitude of afferent inhibition. As such, controlling the focus of attention may be one method to improve the reliability of afferent inhibition. In the present study, the magnitude and reliability of SAI and LAI was assessed under four conditions with varying attentional demands focused on the somatosensory input that evokes SAI and LAI circuits. Thirty individuals participated in four conditions; three conditions were identical in their physical parameters and varied only in the focus of directed attention (visual attend, tactile attend, non- directed attend) and one condition consisted of no external physical parameters (no stimulation). Reliability was measured by repeating conditions at three time points to assess intrasession and intersession reliability. Results indicate that the magnitude of SAI and LAI were not modulated by attention. However, the reliability of SAI demonstrated increased intrasession and intersession reliability compared to the no stimulation condition. The reliability of LAI was unaffected by the attention conditions. This research demonstrates the impact of attention/arousal on the reliability of afferent inhibition and has identified new parameters to inform the design of TMS research to improve reliability.
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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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".