The Roles of Vertex Sharp Waves and K-Complexes in the Generation of N300 in Auditory and Respiratory-related Evoked Potentials During Early Stage 2 NREM Sleep
Bibliographic record
Abstract
STUDY OBJECTIVES: To determine the scalp topography of the N300 response to stimuli of different modalities and to investigate the relationship of the N300 component to K-complexes and vertex sharp waves seen in the un-averaged EEG. DESIGN: Two experiments were conducted one using auditory; the other using respiratory occlusion stimuli presented during stage 2 sleep. Trials were classified on the basis of whether they produced a K-complex, a vertex sharp wave, or some other response. Auditory stimuli were presented in the form of an oddball paradigm, and averaged separately depending on whether they were "frequent" or "rare". In both experiments, responses were averaged separately based on the appearance of K-complexes, vertex sharps waves, or some "other" response to the stimuli. SETTING: Data were collected in the Melbourne University Sleep Laboratory. PARTICIPANTS: Young healthy male adults, eight in experiment 1 and six in experiment 2. INTERVENTIONS: NA. MEASUREMENTS AND RESULTS: Data were collected from 29 scalp sites. In all cases, N300 amplitude was maximal in the vertex sharp wave averages, despite being clearly present in the averages of K-complexes and "other" responses. The vertex maximal scalp topography of the N300 did not differ across response conditions or as a function of stimulus modality. This is consistent with the N300 being produced by the same intracranial generators in all cases. There were no effects of stimulus or response type on N300 latency. CONCLUSIONS: N300 should be viewed as a multi-modal component with a different underlying generator mechanism than that of the K-complex.
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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 | 0.003 |
| 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".