Neurobehavioral concomitants of alcohol use in older healthy adults
Bibliographic record
Abstract
Few laboratory studies permitting granular analyses of alcohol use on neurobehavioral processes in older adults have been reported. This study, reporting baseline data from an on-going longitudinal project, seeks to address this gap. Toward that end, working memory (WM) processes were targeted using the continuous recognition version of the Mnemonic Similarity Task (MST). Healthy male and female drinkers aged 65–80 years completed self-report measures of substance use, negative affect and demographics prior to testing. Drinking patterns were quantified on the basis of typical standard drinks/day (D/D). Behavioral data were obtained in a two-button forced choice paradigm. Neurophysiological data were obtained for each stimulus presentation with analyses focusing on a mid-frontal negative shift occurring ∼ 300–500 ms post stimulus (FN400) and a posterior positive shift occurring ∼ 550–800 ms after stimulus presentation (LPC). To constrain the models, for the behavioral analyses correlations between D/D, measures of negative affect, stimulus condition (“new,” “identical,” or “similar”) and performance were conducted. They indicated that only accuracy in labeling “new” items was related to D/D. Subsequent least squares regression revealed that D/D was inversely related to accuracy for new items. In a sensitivity analysis removing THC users, the D/D effect was retained. Correlations incorporating mean amplitudes for the FN400 and LPC failed to reveal identifiable patterns. Consequently, separate mixed models (e.g., stimulus condition) for FN400 and LPC were conducted. D/D was not predictive of the FN400 for any stimulus condition. It was negatively related to the LPC mean amplitude. In post-hoc analyses, the effect was most notable for “new” stimuli. After removing THC users, the magnitude and direction of the D/D effect was retained, although the p value fell short of significance. Primary models failed to reveal sex main or interaction effects. However, exploratory post-hoc analyses justify their continued study. These data lend preliminary support for the hypothesis that sustained drinking among older adults may negatively impact neurobehavioral processes. They are also consistent with expectations that alcohol effects may be modest and constrained by specific process. Importantly, these outcomes will be expanded through on-going longitudinal study, extending investigation to study of alcohol-related cognitive decline.
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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 | 0.001 | 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".