Examining the Use of Electroencephalography for the Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment
Bibliographic record
Abstract
Introduction: Alzheimer’s disease is a type of dementia characterized by a buildup of ꞵ-amyloid plaques and neurofibrillary tangles. Prior to the development of Alzheimer’s disease, patients may experience mild cognitive impairment, characterized by a decline in cognitive abilities while maintaining independent function. Electroencephalography has shown promise as a clinical predictor of mild cognitive impairment. The purpose of this study is to review the existing literature on clinical biomarkers using resting-state electroencephalography or event-related potentials to differentiate Alzheimer’s disease or mild cognitive impairment from normal aging. Methods: A search of primary research articles was conducted in PubMed. Selected articles examined mild cognitive impairment and Alzheimer’s disease utilising electroencephalography, event-related potential data, and resting-state data. Reviews, conference abstracts, and studies without human controls were excluded. Results: Our search identified 100 and 125 records on resting-state and event-related potential data, respectively. The most common findings from resting-state studies included a reduction in alpha power, an increase in delta and theta power, a reduction in signal complexity, and differences in functional connectivity. The most common findings from event-related potential studies included reduction in P3 wave amplitude, as well as latency in both P3 and N2 waves. Discussion: Resting-state and event-related potential electroencephalography studies indicate distinct changes in oscillatory brain activity and waveform shape which indicate distinct differences in MCI or AD compared to HC which may be clinically relevant. Conclusion: There is evidence to support the use of certain electroencephalographic biomarkers for the diagnosis of Alzheimer’s disease or mild cognitive impairment. Future research should seek to examine how best to apply these findings in a clinical setting.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".