A Novel Eigenvector-based Method to Detect Mild Alzheimer's Disease Using Event-Related Potentials.
Bibliographic record
Abstract
Event-related potentials (ERPs) are a physiological measure of cognitive function that have shown diagnostic and prognostic utility in Alzheimer's disease (AD). In this study, we used a novel eigenvector-based technique to better understand brain electrophysiological differences between subjects with mild AD and healthy controls (HC). Using ERPs from 75 subjects with mild AD and 95 HC, we first calculated cognitive task eigenvectors within each subject from three conditions and then calculated second-order eigenvector components to compare the AD group to the HC group. A MANOVA of the three second-level components discriminated between AD and HC multivariately (Wilks' lambda=.4297, p<0.0001, R2 = .5703), and also on each of the three components univariately (all 3 p-values<0.0001). The eigenvector-based technique used in this study accurately discriminated between the mild AD group and HC. As such, this analysis method adds to our understanding of the differences in ERP signal between AD and HC, and could provide a sensitive biomarker for diagnosis and monitoring of AD progression.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".