Unraveling the Risk Landscape of Mild Cognitive Impairment: A Pilot QEEG Study With Z-Score and Cordance Analysis
Bibliographic record
Abstract
Introduction. Mild cognitive impairment (MCI) is the decline in cognitive function among individuals aged over 60, and the transitional phase between normal aging and dementia. The Mini-Mental State Examination and Montreal Cognitive Assessment (MoCA) may not detect early dementia, hence the importance of identifying MCI or early dementia through biomarkers, such as EEG. Objectives. Evaluating EEG quantification in raw values, EEG quantification in z-scores, and cordance measures as potential differential biomarkers to discriminate MCI. Method. The study involved 20 subjects; 10 healthy individuals and 10 with memory complaints. An EEG was obtained from each participant and raw scores, z-scores, cordance, and three-dimensional data were analyzed. Results. No differences were found in absolute power in raw scores, three-dimensional analysis and cordance variables. A significant difference was found between the groups regarding the Delta1 z-scores at the F7 location, where the memory complaints group exhibited a higher z-score. Conclusions. Normalized EEG quantification data, converted into z-scores, could serve as potential markers to distinguish between cognitively healthy individuals and those at risk of MCI. Using qEEG normative databases may reveal useful differences for identifying subjects at risk of MCI. Further research into intermediate states, between normal cognitive function and established MCI, is needed to clarify this aspect.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".