Reliability of Long-Interval Cortical Inhibition in Healthy Human Subjects: A TMS–EEG Study
Bibliographic record
Abstract
Cortical inhibition (CI) is measured by transcranial magnetic stimulation (TMS) combined with electromyography (EMG) through long-interval CI (LICI) and cortical silent period (CSP) paradigms. Recently, we illustrated that LICI can be measured from the dorsolateral prefrontal cortex (DLPFC) through combined TMS with electroencephalography (EEG). We further demonstrated that LICI had different effects on cortical oscillations in the DLPFC compared with motor cortex. The purpose of this study was to establish the validity and reliability of TMS-EEG indices of CI and to replicate our previous findings in an extended sample. The validity of TMS-EEG was examined by evaluating its relationship to standard EMG measures of LICI and the CSP in the left motor cortex in 36 and 16 subjects, respectively. Test-retest reliability was examined in 14 subjects who returned for a repeat session within 7 days of the first session. LICI was applied to the left DLPFC in 30 subjects to compare LICI in the DLPFC with that in the motor cortex. In the motor cortex, EEG measures of LICI correlated with EMG measures of LICI and CSP. All indices of LICI showed high test-retest reliability in motor cortex and DLPFC. Gamma and beta oscillations were significantly inhibited in the DLPFC but not in the motor cortex, confirming previous findings in an extended sample. These findings demonstrate that indexing LICI through TMS combined with EEG is a valid and reliable method to evaluate inhibition from motor and prefrontal regions.
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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 | 0.007 |
| 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 | 0.001 |
| 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".