Electroencephalogram Global Field Synchronization Analysis
Bibliographic record
Abstract
Alzheimer's disease (AD) is the most common cause of dementia. Global field synchronization (GFS) can measure functional synchronization in frequency-domain electroencephalogram (EEG) data. The aim of this study is to explore GFS values and its clinical significance for severity of cognitive decline in AD. EEGs were recorded from 37 AD patients and 37 age-matched healthy individuals. GFS values were calculated in delta, theta, alpha, beta 1, beta 2, beta 3, gamma, and full frequency bands. The Montreal Cognitive Assessment (MoCA) and Clinical Dementia Rating scale (CDR) were employed to assess symptom severity in AD patients. Correlation analysis, clustering analysis, and concordance analysis were performed to analyze the relationship between GFS values and MoCA scores in AD patients. GFS values of the beta 1, beta 2, beta 3, and full bands were lower in AD patients than in healthy individuals, and positively correlated with MoCA and CDR scores in the combined group (AD patients and healthy individuals). GFS values were positively correlated with MoCA socres in 3 beta bands and full bands, and with CDR scores in the delta band. There was a good concordance between K-means clustering algorithm calculating of GFS values and MoCA scoring (κ = .913, P < .001). In conclusion, the present results indicated that GFS can serve as an indicator of cognitive decline or impairment in AD patients. Furthermore, the GFS method of EEG holds considerable promise to distinguish mild cognitive impairment from serious cognitive impairment in patients with AD.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".