Event-Related Potential Measures of the Passive Processing of Rapidly and Slowly Presented Auditory Stimuli in MCI
Bibliographic record
Abstract
Much research effort is currently devoted to the development of a simple, low-cost method to determine early signs of Alzheimer's disease (AD) pathology. The present study employs a simple paradigm in which event-related potentials (ERPs) were recorded to a single auditory stimulus that was presented rapidly or very slowly while the participant was engaged in a visual task. A multi-channel EEG was recorded in 20 healthy older adults and 20 people with mild cognitive impairment (MCI). In two different conditions, a single 80 dB sound pressure level (SPL) auditory stimulus was presented every 1.5 s (fast condition) or every 12.0 s (slow condition). Participants were instructed to watch a silent video and ignore the auditory stimuli. Auditory processing thus occurred passively. When the auditory stimuli were presented rapidly (every 1.5 s), N1 and P2 amplitudes did not differ between the two groups. When the stimuli were presented very slowly, the amplitude of N1 and P2 increased in both groups and their latencies were prolonged. The amplitude of N1 did not significantly differ between the two groups. However, the subsequent positivity was reduced in people with MCI compared to healthy older adults. This late positivity in the slow condition may reflect a delayed P2 or a summation of a composite P2 + P3a. In people with MCI, the priority of processing may not be switched from the visual task to the potentially much more relevant auditory input. ERPs offer promise as a means to identify the pathology underlying cognitive impairment associated with MCI.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".