A combined TMS-EEG study of short-latency afferent inhibition in the motor and dorsolateral prefrontal cortex
Bibliographic record
Abstract
Combined transcranial magnetic stimulation and electroencephalography (TMS-EEG) enables noninvasive neurophysiological investigation of the human cortex. A TMS paradigm of short-latency afferent inhibition (SAI) is characterized by attenuation of the motor-evoked potential (MEP) and modulation of N100 of the TMS-evoked potential (TEP) when TMS is delivered to motor cortex (M1) following median nerve stimulation. SAI is a marker of cholinergic activity in the motor cortex; however, the SAI has not been tested from the prefrontal cortex. We aimed to explore the effect of SAI in dorsolateral prefrontal cortex (DLPFC). SAI was examined in 12 healthy subjects with median nerve stimulation and TMS delivered to M1 and DLPFC at interstimulus intervals (ISIs) relative to the individual N20 latency. SAI in M1 was tested at the optimal ISI of N20 + 2 ms. SAI in DLPFC was investigated at a range of ISI from N20 + 2 to N20 + 20 ms to explore its temporal profile. For SAI in M1, the attenuation of MEP amplitude was correlated with an increase of TEP N100 from the left central area. A similar spatiotemporal neural signature of SAI in DLPFC was observed with a marked increase of N100 amplitude. SAI in DLPFC was maximal at ISI N20 + 4 ms at the left frontal area. These findings establish the neural signature of SAI in DLPFC. Future studies could explore whether DLPFC-SAI is neurophysiological marker of cholinergic dysfunction in cognitive disorders.
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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 | 0.000 |
| 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".