Visual Event-Related Potentials in Mild Cognitive Impairment and Alzheimer’s Disease: A Literature Review
Bibliographic record
Abstract
BACKGROUND: Cognitive deficits are correlated with increasing age and become more pronounced for people with mild cognitive impairment (MCI) and dementia caused by Alzheimer's disease (AD). Conventional methods to diagnose cognitive decline (i.e., neuropsychological testing and clinical judgment) can lead to false positives. Tools such as electroencephalography (EEG) offer more refined, objective measures that index electrophysiological changes associated with healthy aging, MCI, and AD. OBJECTIVE: We sought to review the EEG literature to determine whether visual event-related potentials (ERPs) can distinguish between healthy aging, MCI, and AD. METHOD: We searched Medline and PyscInfo for articles published between January 2005 and April 2018. Articles were considered for review if they included participants aged 60+ who were healthy older adults or people with MCI and AD, and examined at least one visually elicited ERP component. RESULTS: Our search revealed 880 records, of which 34 satisfied the inclusion criteria. All studies compared cognitive function between at least two of the three groups (healthy older adults, MCI, and AD). The most consistent findings related to the P100 and the P3b; while the P100 showed no differences between groups, the P3b showed declines in amplitude in MCI and AD. CONCLUSION: Visually elicited ERPs can offer insight into the cognitive processes that decline in MCI and AD. The P3b may be useful in identifying older adults who may develop MCI and AD, and more research should examine the sensitivity and specificity of this component when diagnosing MCI and AD.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.014 | 0.015 |
| 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".