Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes - Zeng, Ke; Wang, Yinghua; Ouyang, Gaoxiang; Bian, Zhijie; Wang, Lei; Li, Xiaoli (Frontiers in Computational Neuroscience, 2015)
Purpose: Diabetes is a great risk factor for dementia and mild cognitive impairment. This study inves-tigates whether complex network-derived features in resting state EEG (rsEEG) could be applied as a biomarker to distinguish amnestic mild cognitive impairment (aMCI) from normal cognitive function in subjects with type 2 diabetes. Method: In this study, EEG was recorded in 28 patients with type 2 diabetes (16 aMCI patients and 12 controls) during a no-task eyes-closed resting state. Pair-wise synchronization of rsEEG signals were assessed in six frequency bands (delta, theta, lower alpha, upper alpha, beta, and gamma) using phase lag index (PLI) and grouped into long distance (intra- and inter-hemispheric) and short distance inter-actions. PLI-weighted connectivity networks were also constructed, and characterized by mean clus-tering coefficient and path length. The correlation of these features and Montreal Cognitive Assess-ment (MoCA) scores was assessed. Results: Main findings of this study were as follows: (1) In comparison with controls, patients with aMCI had a significant decrease of global mean PLI in lower alpha, upper alpha, and beta bands. Lower functional connection at short and long intra-hemispheric distance mainly appeared on the left hemisphere. (2) In the lower alpha band, clustering coefficient was significantly lower in aMCI group, and the path length significantly increased. (3) Cognitive status measured by MoCA had a significant positive correlation with cluster coefficient and negative correlation with path length in lower alpha band. Conclusions: The brain network of aMCI patients displayed a disconnection syndrome and a loss of small-world architecture. The correlation between cognitive states and network characteristics sug-gested that the more in deterioration of the diabetes patients’ cognitive state, the less optimal the net-work organization become. Hence, the complex network-derived biomarkers based on EEG could be employed to track cognitive function of diabetic patients and provide a new diagnosis tool for aMCI.