Complex network analysis of resting state EEG in amnestic mild cognitive impairment patients with type 2 diabetes

Abstract

Purpose: 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).Method: 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.

Biomarkers for cognitive decline in patients with diabetes mellitus: evidence from clinical studies

“…These dysfunctions were positively correlated with HbA1c and DM duration, indicating the key role of hyperglycemia in the process of cognitive decline. Zeng et al demonstrated consistent results in 28 diabetic patients and found dysfunctional network was related to poorer MoCA scores [ 99 ]. Thus, the network organization and functional connectivity in some brain regions could be affected in patients with diabetes, which can be used for the evaluation and diagnosis of cognitive decline.…”

EEG spectral power abnormalities and their relationship with cognitive dysfunction in patients with Alzheimer's disease and type 2 diabetes

“…The relationship between these oscillatory changes and cognitive dysfunction remains unclear, though some studies have reported correlations with individual tests of cognitive functions (Babiloni et al, 2007;Moretti et al, 2009;van der Hiele et al, 2007). While fewer studies have examined oscillatory changes in T2DM, there is some evidence of a similar shift in power from higher to lower frequencies (Bian et al, 2014;Cooray et al, 2011;Cui et al, 2014;Wen et al, 2016;Zeng et al, 2015).…”

Disrupted Brain Network in Children with Autism Spectrum Disorder

“…Such an organization of brain network plays a crucial role in keeping a good balance between global integration (short path) and local specialization (high clustering). Several studies have demonstrated that better cognitive capabilities are associated with higher clustering and shorter paths, while deviant brain topologies may lead to neurological diseases 18 , 19 . In addition, there is evidence of large scale network reorganization during normal development of brain 20 .…”