Short-interval intracortical inhibition (SICI): effect of target tracking on variability of responses for 1 mV and 200µV test-alone targets
Bibliographic record
Abstract
OBJECTIVE: To evaluate whether continuously tracking unconditioned thresholds for maintaining constant motor-evoked potential (MEP) amplitudes improves the variability of amplitude-based short-interval intracortical inhibition (SICI) measurements. METHODS: Fifty-five healthy subjects were tested twice on two days with six SICI protocols. Conditioning stimulus (CS) intensity was set to 70 % of the resting motor threshold for a 200µV target (RMT200), while test stimulus (TS) intensity targeted MEP of either 1 mV or 200µV. Protocols included conventional A-SICI (fixed CS and TS), hybrid A-SICI (fixed CS and updated TS by threshold tracking); tracked A-SICI (both CS and TS updated by threshold tracking). Variability in unconditioned and conditioned responses was analyzed across interstimulus intervals (ISIs) of 1, 2.5, and 3 ms. RESULTS: Threshold-tracking reduced variability of the unconditioned responses measured by geometric standard deviation (expressed as a factor) for 1 mV (×/÷1.61 to 1.39; p<0.0001) and 200µV targets (×/÷2.21 to 1.30; p<0.0001). However, variability of inhibition measures did not differ significantly across protocols. Inhibition with the 200µV MEP target was significantly less than with 1 mV across all ISIs (p<0.001). The A-SICI 200µV tracked protocol showed reliability comparable to A-SICI fixed 1 mV, suggesting it may be a practical alternative in clinical populations where achieving a 1 mV MEP is challenging, such as in patients with severe muscle denervation. CONCLUSIONS: While threshold-tracking enhances unconditioned MEP reproducibility, it does not reduce the variability of SICI, which is highly dependent on target MEP size. These findings point towards two distinct mechanisms underlying conditioned and unconditioned responses and refine understanding of SICI variability.
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 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 | 0.000 | 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".