Relationship between phase lag index measured by electroencephalography and cognitive dysfunction in patients with cerebral small vessel disease
Bibliographic record
Abstract
OBJECTIVES: Cerebral small vessel disease (CSVD) is a primary cause of cognitive impairment (CI) in the elderly. This study aims to explore the relationship between the phase lag index (PLI), derived from electroencephalography (EEG), and cognitive dysfunction in patients with CSVD. METHODS: This retrospective study included patients diagnosed with CSVD from May 2020 to December 2023. EEG data were recorded using 64 electrodes and analyzed for PLI across four frequency bands. Cognitive function was assessed using the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA). Blood pressure variability was monitored using a 24-hour portable device. RESULTS: The study included 264 patients, categorized into two groups: CI group (n = 102) and no CI group (n = 162). The CI group exhibited significantly lower global alpha-band PLI (0.28 vs. 0.31, P = 0.006) and reduced alpha-PLI across multiple electrode pairs (0.27 vs. 0.30, P = 0.004). Cognitive scores were also lower in the CI group (MMSE: 26.25 vs. 27.76, P = 0.004; MoCA: 25.38 vs. 26.63, P = 0.007). Additionally, the CI group had higher 24-hour mean systolic blood pressure (SBP, 140.68 vs. 136.36 mmHg, P = 0.038) and lower daytime SBP coefficient of variation (9.46% vs. 10.63%, P = 0.002). Receiver operating characteristic analysis revealed that F8-P8 PLI had an area under the curve of 0.608, indicating moderate discriminatory ability for identifying cognitive dysfunction. CONCLUSION: Decreased phase synchronization in the EEG alpha-band correlated with cognitive dysfunction in CSVD patients, indicating that impaired neural connectivity may serve as a potential electrophysiological biomarker.
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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".