Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes
Bibliographic record
Abstract
Mild cognitive impairment (MCI) is one of the important comorbidities of type 2 diabetes mellitus (T2DM). It is critical to find appropriate methods for early diagnosis and objective assessment of mild cognitive impairment patients with type 2 diabetes (T2DM-MCI). Our study aimed to investigate potential early alterations in phase lag index (PLI) and determine whether it can distinguish between T2DM-MCI and normal controls with T2DM (T2DM-NC). EEG was recorded in 30 T2DM-MCI patients and 30 T2DM-NC patients. The phase lag index was computed and used in a logistic regression model to discriminate between groups. The correlation between the phase lag index and Montreal Cognitive Assessment (MoCA) score was assessed. The α-band phase lag index was significantly decreased in the T2DM-MCI group compared with the T2DM-NC group and showed a moderate degree of classification accuracy. The MoCA score was positively correlated with the α-band phase lag index (r = 0.4812, moderate association, p = 0.015). This work shows that the functional connectivity analysis of EEG may offer an effective way to track the cortical dysfunction linked to the cognitive deterioration of T2DM patients, and the α-band phase lag index may have a role in guiding the diagnosis of T2DM-MCI.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".