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.
Highlights
As the most common cause of dementia, Alzheimer’s disease (AD) is a disabling neurodegenerative disorder characterized by progressive impairment of learning, memory, and other cognitive functions
Notice that the sequences of clustering coefficient in different layers are uncorrected or weakly anti-corrected, while multiplex clustering coefficient (MCC) may show no correlation or weak correlation with clustering coefficient in frequency-specific networks. This result confirms that MCC may provide important information different from those obtained within frequency bands
In the cross-frequency networks, an average accuracy of 70.63% was achieved by the MCC (AUC = 0.78), while for the multiplex participation coefficient (MPC), the classification performance was much better with an accuracy of 77.5% and an area under the curve (AUC) of 0.86
Summary
As the most common cause of dementia, Alzheimer’s disease (AD) is a disabling neurodegenerative disorder characterized by progressive impairment of learning, memory, and other cognitive functions. Some researchers reported that AD patients showed higher functional connectivity over centro-parietal regions in the theta band and over occipito-parietal regions in the beta and gamma band (Stam et al, 2006), whereas remarkable reduction of beta connectivity in right frontal region can be found (Chan et al, 2013). These studies confirmed that the functional networks provide frequency-specific information about the brain function disruptions in AD.
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