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

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.

Task aftereffect reorganization of resting state functional brain networks in healthy aging and mild cognitive impairment

“…We also found no significant pre-to-post differences in these two measures over the whole cohort or between groups. This endorses the idea that MST parameters are more sensitive to detect profound changes in brain networks for MCI patients than conventional measures ( López et al, 2017 ; Požar et al, 2020 ; Youssef et al, 2021 ).…”

“…If we compare trees A and B in Figure 1 , then A is more integrated than B and both the leaf number and tree hierarchy are higher in A than in B. In contrast, if we compare trees B and C in Figure 1 , then B is more integrated than C, but the leaf number is the same in both trees while the tree hierarchy is smaller in B than in C. Hence, we cannot immediately conclude that increased MST hierarchy reported in Youssef et al, (2021) indicates a more integrated topology. However, in the same study one can find that the percentage of change in mean beta leaf number was not preserved from the pre- to post-task timepoint but was slightly decreased in the control group and slightly increased in the aMCI group.…”

“…Such modified topology increases global efficiency and is thus beneficial for information processing in the network, yet is less optimal as the central vertex has a greater probability to be burdened by a relatively large information flow and may require costly infrastructure ( Meunier et al, 2010 ). A recent EEG study has reported that patients with amnestic MCI (aMCI) showed a significantly higher increment in MST hierarchy from the pre- to post-task timepoint in the beta frequency band compared to healthy controls as reflected by the percentage change from the pre- to post-task timepoint ( Youssef et al, 2021 ). Put simply, the tree hierarchy is the ratio between the leaf number and maximum betweenness centrality and measures the balance between integration and overload prevention.…”

“…This finding indicates that the return to the baseline resting-state EEG is time-consuming and may index the efficacy of cognitive processing and/or cognitive health. Subsequently, only one other study has examined the interplay between brain network organization in MCI identified with resting-state EEG data before and after a cognitive task ( Youssef et al, 2021 ). That study revealed a global reorganization from the pre- to post-task resting-state of the patients’ brain networks when compared to healthy controls.…”

Changes in Electrical Brain Activity and Cognitive Functions Following Mild to Moderate COVID-19: A one-Year Prospective Study After Acute Infection

“…Several studies have demonstrated the utility of these features in the analysis of EEG signals. For instance, they have been used in brain-computer interface (BCI) control and EEG-controlled robotic navigation, 54 as well as in the assessment of mild cognitive impairment, 44,55 attention deficit hyperactivity disorder, 56 neurodegenerative disorders such as Alzheimer's disease, 26 and extraction of information from different biomedical signals. [57][58][59] We also observed a decline in cognitive function 6-12 months after acute COVID-19 infection, as demonstrated by TMT-A.…”