Auditory Event-related Potentials in Mild Cognitive Impairment and Alzheimer’s Disease
Bibliographic record
Abstract
BACKGROUND: Mild cognitive deficits are more likely to occur with increasing age, and become more pronounced for people diagnosed with mild cognitive impairment (MCI) and Alzheimer's disease (AD). Conventional methods to identify cognitive declines (i.e., neuropsychological testing and clinical judgment) can lead to false positive diagnoses of cognitive impairment. Tools such as electroencephalography (EEG) offer additional measures of cognitive processing, indexing the electrophysiological changes associated with aging, MCI and AD. OBJECTIVE: We reviewed the literature on EEG to determine if auditory event-related potentials (ERPs) could distinguish between healthy aging, MCI, and AD. METHOD: We searched two electronic databases (Medline and PyscInfo) for articles published between January 2005 and April 2017. Articles were considered for review if they included: i) participants 60 years of age or older; ii) healthy older adults or those diagnosed with MCI or AD; iii) at least one auditory elicited ERP component. RESULTS: Our search revealed 1532 articles (800 after removing duplicates); 719 were excluded through title/abstract review, and of the 81 remaining articles, 30 satisfied inclusion criteria. All studies compared cognitive function between at least two of the three selected populations. Our findings suggest that the P300 and N200 components may distinguish between healthy cognitive aging, MCI, and AD. CONCLUSION: ERPs may be sensitive to progressive cognitive changes due to MCI and AD. The P300 and N200 may help identify patients who are likely to progress from MCI to AD, and could be a valuable clinical tool.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.011 | 0.011 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".