Cortical brain states and corticospinal synchronization influence TMS-evoked motor potentials
Bibliographic record
Abstract
Transcranial magnetic stimulation (TMS) influences cortical processes. Recent findings indicate, however, that, in turn, the efficacy of TMS depends on the state of ongoing cortical oscillations. Whereas power and phase of electromyographic (EMG) activity recorded from the hand muscles as well as neural synchrony between cortex and hand muscles are known to influence the effect of TMS, to date, no study has shown an influence of the phase of cortical oscillations during wakefulness. We applied single-pulse TMS over the motor cortex and recorded motor-evoked potentials along with the electroencephalogram (EEG) and EMG. We correlated phase and power of ongoing EEG and EMG signals with the motor-evoked potential (MEP) amplitude. We also investigated the functional connectivity between cortical and hand muscle activity (corticomuscular coherence) with the MEP amplitude. EEG and EMG power and phase in a frequency band around 18 Hz correlated with the MEP amplitude. High beta-band (∼34 Hz) corticomuscular coherence exhibited a positive linear relationship with the MEP amplitude, indicating that strong synchrony between cortex and hand muscles at the moment when TMS is applied entails large MEPs. Improving upon previous studies, we demonstrate a clear dependence of TMS-induced motor effects on the state of ongoing EEG phase and power fluctuations. We conclude that not only the sampling of incoming information but also the susceptibility of cortical communication flow depends cyclically on neural phase.
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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".