title: Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes.
abstract: OBJECTIVE
  Diabetes is a great risk factor for dementia and mild cognitive impairment (MCI). This study investigates 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 (T2D).
  
  
  METHODS
  In this study, EEG was recorded in 28 patients with T2D (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 interactions. PLI-weighted connectivity networks were also constructed, and characterized by mean clustering coefficient and path length. The correlation of these features and Montreal Cognitive Assessment (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 suggested that the more in deterioration of the diabetes patients' cognitive state, the less optimal the network 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.
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Zeng
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China.
authors.forename: Yinghua
authors.initials: Y
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China.
authors.forename: Gaoxiang
authors.initials: G
authors.lastname: Ouyang
authors.affiliation: Department of Vascular Neurosurgery, The Second Artillery General Hospital of PLA Beijing, China.
authors.forename: Zhijie
authors.initials: Z
authors.lastname: Bian
authors.affiliation: Department of Neurology, The Second Artillery General Hospital of PLA Beijing, China.
authors.forename: Lei
authors.initials: L
authors.lastname: Wang
authors.affiliation: State Key Laboratory of Cognitive Neuroscience and Learning, IDG/McGovern Institute for Brain Research, Beijing Normal University Beijing, China ; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University Beijing, China.
authors.forename: Xiaoli
authors.initials: X
authors.lastname: Li
keywords: amnestic mild cognitive impairment (aMCI); diabetes; graph analysis; phase lag index (PLI); resting state electroencephalography (rsEEG)