pre-task in beta EEG, a significant increase in tree hierarchy was observed in aMCI group (2.41%) than in normal control (−3.89%) (3) Furthermore, the classification analysis of combined measures of functional connectivity, brain topology, and MMSE test showed improved accuracy compared to the single method, for which the connectivity patterns and graph metrics were used as separate inputs. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. Results showed that: (1) across the pre-task condition, in the theta band, the aMCI group had a significantly lower global mean DWPLI than the control group the functional connectivity patterns were different in the left hemisphere between two groups the aMCI group showed significantly higher average clustering coefficient and the remarkably lower global efficiency than the control. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants.

Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). To this end, rsEEG signals were recorded before and after a cognitive task. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). The oscillatory patterns of electroencephalography (EEG), during resting states, are informative and helpful in understanding the functional states of brain network and their contribution to behavioral performances.

3School of Life Sciences, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.

2Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.

1School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.

Nadia Youssef 1, Shasha Xiao 1, Meng Liu 2, Haipeng Lian 1, Renren Li 2, Xi Chen 2, Wei Zhang 1, Xiaoran Zheng 2, Yunxia Li 2* and Yingjie Li 1,3*

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