Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

Nadia Youssef; Shasha Xiao; Meng Liu; Haipeng Lian; Renren Li; Xi Chen; Wei Zhang; Xiaoran Zheng; Yunxia Li; Yingjie Li

doi:10.3389/fncom.2021.698386

Functional Brain Networks in Mild Cognitive Impairment Based on Resting Electroencephalography Signals

Front Comput Neurosci. 2021 Oct 20:15:698386. doi: 10.3389/fncom.2021.698386. eCollection 2021.

Authors

Nadia Youssef¹, Shasha Xiao¹, Meng Liu², Haipeng Lian¹, Renren Li², Xi Chen², Wei Zhang¹, Xiaoran Zheng², Yunxia Li², Yingjie Li^{1

3}

Affiliations

¹ School of Communication and Information Engineering, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.
² Department of Neurology, Tongji Hospital, School of Medicine, Tongji University, Shanghai, China.
³ School of Life Sciences, Shanghai Institute for Advanced Communication and Data Science, Shanghai University, Shanghai, China.

Abstract

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. The aim of this study is to characterize the functional brain network alterations in patients with amnestic mild cognitive impairment (aMCI). To this end, rsEEG signals were recorded before and after a cognitive task. Functional connectivity metrics were calculated using debiased weighted phase lag index (DWPLI). Topological features of the functional connectivity network were analyzed using both the classical graph approach and minimum spanning tree (MST) algorithm. Subsequently, the network and connectivity values together with Mini-Mental State Examination cognitive test were used as features to classify the participants. 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. (2) Analysis of graph measures under post-task resting state, unveiled that for the percentage change of post-task vs. 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. The classification accuracy obtained for the case of post-task resting state was 87.2%, while the one achieved under pre-task resting state was found to be 77.7%. Therefore, the functional network alterations in aMCI patients were more prominent during the post-task resting state. This study suggests that the disintegration observed in MCI functional network during the resting states, preceding and following a task, might be possible biomarkers of cognitive dysfunction in aMCI patients.

Keywords: connectivity; graph theory; machine learning; mild cognitive impairment; minimum spanning tree; resting EEG.