Stimulation mapping and whole-brain modeling reveal gradients of excitability and recurrence in cortical networks
Bibliographic record
Abstract
Transcranial magnetic stimulation-electroencephalography (TMS-EEG) is increasingly being used to study human cortical physiology.However, the method suffers from a considerable challenge posed by the presence of peripherally evoked potentials (PEPs) in the response signal, which are elicited by auditory and somatosensory stimuli from the TMS device activation.Consequently, it is unclear whether results observed in TMS-EEG measurements reflect responses to direct cortical activation by TMS or simply unspecific cortical responses to sensory input.To overcome this limitation, we developed two sham methods that reliably reproduce PEPs elicited by TMS, allowing the removal of PEP components and revealing the true TMS-evoked potentials (TEPs).The first method involves delivering high-intensity somatosensory stimulus to both sham and real TMS conditions, creating subjectively indistinguishable inputs and eliciting matching PEPs.By applying this procedure to a pharmacological TMS-EEG experiment, it was shown that both TEPs and PEPs can be modulated by CNS agents, highlighting the importance of proper sham control in TMS-EEG.The second method involves individually titrating the somatosensory stimulus intensity of the sham condition, guided by real-time comparison with the real TMS condition's EEG response.The resulting TEPs from both sham methods are identical, suggesting that high-intensity somatosensory stimulus does not modulate the underlying TEPs, thus making both methods valid.Given the current evidence, the use of reliable and tested sham control methods in TMS-EEG measurements is imperative.This can be reliably achieved with either of the two methods presented.The use of the titration method is more time consuming but may be a better alternative when tolerability to high intensity stimuli is an issue.
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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 | 0.001 |
| 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 | 0.001 |
| 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".