Phase Lag Index of Resting-State EEG for Identification of Mild Cognitive Impairment Patients with Type 2 Diabetes.
Yuxing KuangZiyi WuRui XiaXingjie LiJun LiuYalan DaiDan WangShangjie ChenPublished in: Brain sciences (2022)
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.
Keyphrases
- mild cognitive impairment
- functional connectivity
- resting state
- cognitive decline
- end stage renal disease
- ejection fraction
- newly diagnosed
- glycemic control
- chronic kidney disease
- prognostic factors
- machine learning
- oxidative stress
- working memory
- type diabetes
- patient reported outcomes
- metabolic syndrome
- adipose tissue
- skeletal muscle
- weight loss
- patient reported
- insulin resistance