Alcohol Impairs N100 Response to Dorsolateral Prefrontal Cortex Stimulation
Bibliographic record
Abstract
Abstract Alcohol is thought to exert its effect by acting on gamma-aminobutyric (GABA) inhibitory neurotransmission. The N100, the negative peak on electroencephalography (EEG) that occurs approximately 100 ms following the transcranial magnetic stimulation (TMS) pulse, is believed to represent GABAB receptor mediated neurotransmission. However, no studies have examined the effect of alcohol on the N100 response to TMS stimulation of the dorsolateral prefrontal cortex (DLPFC). In the present study, we aimed to explore the effect of alcohol on the DLPFC TMS-evoked N100 response. The study was a within-subject cross-over design study. Fifteen healthy alcohol drinkers were administered TMS to the DLPFC before (PreBev) and after consumption (PostBev) of an alcohol or placebo beverage. The amplitude of the N100 before and after beverage was compared for both the alcohol and placebo beverage. Alcohol produced a significant decrease in N100 amplitude (t = 4.316, df = 14, p = 0.001). The placebo beverage had no effect on the N100 amplitude (t = −1.856, df = 14, p = 0.085). Acute alcohol consumption produces a decrease in N100 amplitude to TMS stimulation of the DLPFC, suggesting a decrease in GABAB receptor mediated neurotransmission. Findings suggest that the N100 may represent a marker of alcohol’s effects on inhibitory neurotransmission.
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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) | 0.002 | 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".