Association between electroencephalogram alpha-band oscillations and executive and processing functions in patients with cerebral small vessel diseases
Bibliographic record
Abstract
Electroencephalogram (EEG) alpha-band oscillations may reflect executive and processing function in patients with cerebral small vessel disease (CSVD). We aimed to assess such association and its relationship with CSVD severity, and to identify specific alpha-band parameters and the cut-off values for cognitive screening. We analysed the dispersion of amplitude-frequency characteristics of EEG alpha-band and different alpha-band parameters (PFα , ΔPFα , PPα , NCL) in different brain locations. We also assessed patients’ executive and processing functions using verbal fluency test (VFT) and color trails test (CTT), and CSVD severity using total burden and Fazekas scores. 129 patients were recruited in the study. After adjusting for age, gender and education, PFα(F3), PFα(F4) and NCL were significantly associated with VFT-composite performance ( p < 0.05). CTT-1 time and error were associated with PFα(F3), PFα(F4), ΔPFα(O1;F3) and CSVD severity ( p < 0.05), whereas CTT-2 time was only associated with CSVD severity. Moreover, the correlations between alpha-band oscillations and cognitive function were higher in low than in high disease-severity group (ρ: −0.58 vs. −0.38, p < 0.05). The AUC of selected alpha-band parameters were higher than 0.8 for VFT and CTT. Specific alpha-band parameters in the frontal lobe were identified to correspond to executive and processing function. Assessing EEG alpha-band oscillations may assist in screening cognitive impairment.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".