Reconstruction of functional brain network in Alzheimer's disease via cross-frequency phase synchronization

Cai, Lihui; Wei, Xile; Wang, Jiang; Yu, Haitao; Deng, Bin; Wang, Ruofan

doi:10.1016/j.neucom.2018.07.019

Cited by 36 publications

(42 citation statements)

References 45 publications

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“…Cai et al [17] report comparable differences using PSI between AD and HC, but in contrast to our results, they report mainly decreased NS in AD. This might be be- cause CS and CBS are influenced by amplitude, while PSI is a pure phase coupling measure.…”

Section: B Coupling-wise Node Strengthcontrasting

confidence: 99%

“…Moreover, significant differences were found in global connectivity in all δ, θ and β CFC (except for δγ,θ-γ and β-α) and in β-β. Differences in these bands (both WFC and CFC) were reported by Cai et al [17] using PSI, but the reported differences are opposite to our findings. Moreover, we found significant differences in γ WFC and γ-δ and γ-β CFC.…”

Section: A Connectivity Matrices and Global Connectivitycontrasting

confidence: 99%

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Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer’s Disease

Klepl

et al. 2021

Preprint

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Alzheimer's disease (AD) is a neurodegenerative disorder known to affect functional connectivity (FC) across many brain regions. Linear FC measures have been applied to study the differences in AD by splitting neurophysiological signals such as electroencephalography (EEG) recordings into discrete frequency bands and analysing them in isolation from each other. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach. Cross-bispectrum, a higher-order spectral analysis approach, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. This work reports the first use of cross-bispectrum to reconstruct a cross-frequency FC network where each frequency band is treated as a layer in a multilayer network with both inter- and intra-layer edges. An increase of within-band FC in AD is observed in low-frequency bands using both methods. Bispectrum also detects multiple cross-frequency differences, mainly increased FC in AD in delta-theta coupling. An increased importance of low-frequency coupling and decreased importance of high-frequency coupling is observed in AD. Integration properties of AD networks are more vulnerable than HC, while the segregation property is maintained in AD. Moreover, the segregation property of γ is less vulnerable in AD, suggesting the shift of importance from high-frequency activity towards low-frequency components. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD. Moreover, the results demonstrate the advantages and limitations of using bispectrum to reconstruct FC networks.

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Section: B Coupling-wise Node Strengthcontrasting

confidence: 99%

Section: A Connectivity Matrices and Global Connectivitycontrasting

confidence: 99%

Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer’s Disease

Klepl

et al. 2021

Preprint

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“…(2) Reduced Complexity. By comparing AD patients to healthy controls, a reduction in the complexity of brain electrical activity has been found [14,[94][95][96].…”

Section: Conditions Of Eeg Recording and Symptoms Of Admentioning

confidence: 99%

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

Ouchani

Gharibzadeh

Jamshidi

et al. 2021

BioMed Research International

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This study will concentrate on recent research on EEG signals for Alzheimer’s diagnosis, identifying and comparing key steps of EEG-based Alzheimer’s disease (AD) detection, such as EEG signal acquisition, preprocessing function extraction, and classification methods. Furthermore, highlighting general approaches, variations, and agreement in the use of EEG identified shortcomings and guidelines for multiple experimental stages ranging from demographic characteristics to outcomes monitoring for future research. Two main targets have been defined based on the article’s purpose: (1) discriminative (or detection), i.e., look for differences in EEG-based features across groups, such as MCI, moderate Alzheimer’s disease, extreme Alzheimer’s disease, other forms of dementia, and stable normal elderly controls; and (2) progression determination, i.e., look for correlations between EEG-based features and clinical markers linked to MCI-to-AD conversion and Alzheimer’s disease intensity progression. Limitations mentioned in the reviewed papers were also gathered and explored in this study, with the goal of gaining a better understanding of the problems that need to be addressed in order to advance the use of EEG in Alzheimer’s disease science.

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“…On the other hand, it offers a general language to describe the global properties of brain networks (e.g., modularity). For these reasons, graph theory can provide a promising framework to characterize the pathological processes in AD brain (Tijms et al, 2013;Cai et al, 2018). Recent functional network studies have reported that AD is associated with decreased nodal centrality in higher-order association areas and selective impairment of hippocampus and posterior hub areas (Crossley et al, 2014;Yu et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

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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.

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Reconstruction of functional brain network in Alzheimer's disease via cross-frequency phase synchronization

Cited by 36 publications

References 45 publications

Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer’s Disease

Cross-Frequency Multilayer Network Analysis with Bispectrum-based Functional Connectivity: A Study of Alzheimer’s Disease

A Review of Methods of Diagnosis and Complexity Analysis of Alzheimer’s Disease Using EEG Signals

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

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