Assessing the Topographic EEG Changes Associated with Aging and Acute/Long-Term Effects of Smoking
Bibliographic record
Abstract
As neuroelectric research into the smoking/nicotine habit has focused exclusively on young and middle-aged adults, this electroencephalographic (EEG) study was conducted to determine whether a long-term smoking history alters the aging brain and/or whether the aging brain demonstrates an altered sensitivity to acute smoking/nicotine. Forty healthy adults, 20 young, aged 18-39 years, and 20 elderly, aged 64-81 years, volunteered for participation. Half of the young and elderly were nonsmokers with no previous smoking history and the remaining half of the young and elderly were current smokers with average smoking histories of 9.3 and 52.0 years, respectively. Smokers attended the laboratory for two randomized test sessions during which multisite EEG recordings were collected pre and post sham and cigarette smoking. Nonsmokers attended the laboratory for one nonsmoking EEG recording session. Spectral power indices showed aging to be associated with significant reductions in absolute delta and theta power and increases in relative beta power and faster mean total band frequency. Aging effects varied with recording region but not with smoker versus nonsmoker status. Smokers did exhibit a faster mean beta frequency. Acute cigarette smoking decreased absolute delta power in young smokers and increased relative alpha 2, beta power and mean alpha frequency in both young and elderly smokers. Only the young smokers showed increase in mean theta and total frequency. The results are discussed in relation to cognition in normal and pathological aging.
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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 | 0.000 |
| 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".