Hypoglycemia-Induced Decrease of EEG Coherence in Patients with Type 1 Diabetes
Bibliographic record
Abstract
BACKGROUND: Hypoglycemic events in patients with type 1 diabetes (T1D) are associated with measurable electroencephalography (EEG) changes. Previous studies have, however, evaluated these changes on a single EEG channel level, whereas multivariate analysis of several EEG channels has been scarcely investigated. The aim of the present work is to use a coherence approach to quantitatively assess how hypoglycemia affects mutual connectivity of different brain areas. MATERIALS AND METHODS: EEG multichannel data were obtained from 19 patients with T1D (58% males; mean age, 55 ± 2.4 years; diabetes duration, 28.5 ± 2.6 years; glycated hemoglobin, 8.0 ± 0.2%) who underwent a hyperinsulinemic-hypoglycemic clamp study. The information partial directed coherence (iPDC) function was computed through multivariate autoregressive models during eu- and hypoglycemia in the theta and alpha bands. RESULTS: In passing from eu- to hypoglycemia, absolute values of the iPDC function tend to decrease in both bands in all combinations of the considered channels. In particular, the scalar indicator [Formula: see text], which summarizes iPDC information, significantly decreased (P < 0.01) in 17 of 19 subjects: from T5-A1A2 to C3-A1A2 from O1-A1A2 to C4-A1A2 and from O2-A1A2 to Cz-A1A2 in the theta band and from O1-A1A2 to T4-A1A2 and from O1-A1A2 to C4-A1A2 in the alpha band. CONCLUSIONS: The coherence decrease measured by iPDC in passing from eu- to hypoglycemia is likely related to the progressive loss of cognitive function and altered cerebral activity in hypoglycemia. This result encourages further quantitative investigation of EEG changes in hypoglycemia and of how EEG acquisition and real-time processing can support hypoglycemia alert systems.
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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 | 0.000 | 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) | 0.001 | 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".