Functional Integration and Segregation in Multiplex Brain Networks for Alzheimer's Disease

Abstract

Growing evidence links impairment of brain functions in Alzheimer's disease (AD) with disruptions of brain functional connectivity. However, whether the AD brain shows similar changes from a dynamic or cross-frequency view remains poorly explored. This paper provides an effective framework to investigate the properties of multiplex brain networks in AD considering inter-frequency and temporal dynamics. Using resting-state EEG signals, two types of multiplex networks were reconstructed separately considering the network interactions between different frequency bands or time points. We further applied multiplex network features to characterize functional integration and segregation of the cross-frequency or time-varying networks. Finally, machine learning methods were employed to evaluate the performance of multiplex-network-based indexes for detection of AD. Results revealed that the brain networks of AD patients are disrupted with reduced segregation particularly in the left occipital area for both cross-frequency and time-varying networks. However, the alteration of integration differs among brain regions and may show an increasing trend in the frontal area of AD brain. By combining the features of integration and segregation in time-varying networks, the best classification performance was achieved with an accuracy of 92.5%. These findings suggest that our multiplex framework can be applied to explore functional integration and segregation of brain networks and characterize the abnormalities of brain function. This may shed new light on the brain network analysis and extend our understanding of brain function in patients with neurological diseases.

Identification of Alzheimer's EEG With a WVG Network-Based Fuzzy Learning Approach

“…In future works it will be combined with multi-layer network theory, further discussing the correlation between different channels with constructing multi-layer network. In past research we confirmed the feasibility of the multi-layer network scheme, and extracted the multiplex clustering coefficient and multiplex participation coefficient (Cai et al, 2020 ). Future work will consider both the implicit characteristics of single channels and the information integration between multiple channels.…”

Integration and Segregation of Dynamic Functional Connectivity States for Mild Cognitive Impairment Revealed by Graph Theory Indicators

“…Currently, more and more scholars adopt the method of detecting dynamic functional connectivity [12,13], which reflects how the functional connectivity of the brain changes over time [14]. Some studies have found that diseases can change the dynamic characteristics of functional connectivity, which can be used as a physiological index for disease research [15], and has important theoretical and practical value for the study of the dynamic characteristics of brain functional networks. Dynamic functional connectivity can more accurately represent the dynamic features of the brain [11], whereas static functional connectivity also helps to understand brain correlations.…”

Dopamine transporter is a master regulator of dopaminergic neural network connectivity

“…Thus, the network attempting to maintain synchrony by temporally aligning may then contribute to dysfunction of other nuclei within the basal ganglia 134,135 . Conversely, the response found within the VTA would suggest robustness resilience to cascades of energy usage or pathogens 100,136 .…”