Memorization Test and Resting State EEG Components in Mild and Subjective Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Mild (MCI) and Subjective Cognitive Impairment (SCI) are conditions at risk of developing Alzheimer's disease (AD). Differential between normal aging at early stages can be really challenging; available biomarkers need to be combined and can be quite invasive and expensive. OBJECTIVE: The aim of this pilot study is to examine possible EEG alterations in MCI and SCI compared to controls, analyzing if a cognitive task could highlight early AD hallmarks. METHOD: We recruited 11 MCI, 8 SCI and 7 healthy subjects as controls (CS), all matched for age and education. Neuropsychological assessment and EEG recording, at resting state and during a mental memory task, were performed. Classical spectral measures and nonlinear parameters were used to characterize EEGs. RESULTS: During cognitive task, α-band power reduction was found predominantly in frontal regions in SCI and CS, diffused to all regions in MCI; moreover, decreased EEG complexity was found in SCI compared to controls. The α -band power attenuation restricted to frontal regions in SCI during a free recall task (involving frontal areas), suggests that MCI patients compensate for encoding deficit by activating different brain networks to perform the same task. Furthermore, EEG complexity reduction - that has been found already in SCI - could be a possible early hallmark of AD. CONCLUSION: This study draws attention on the importance of nonlinear approach in EEG analysis and the potential role of cognitive task in highlighting EEG alterations at very early stages of cognitive impairment; EEG could therefore have a practical impact on dementia diagnosis.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".