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

Abstract

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.

Cross-bispectrum connectivity of intracranial EEG: A novel approach to seizure onset zone localization

“…A cross-bispectrum (CBS) analysis of two temporal signals yields a 2D mapping of the level of higher-order CFC between every frequency combination present in the signals [11], [17]. For each seizure, directed cross-bispectrum plots between all available iEEG channel pairs were computed using the Higher Order Spectral Analysis Matlab Toolbox from 4-sec segments following electrical seizure onset [18].…”

“…Interestingly, bivariate CFC is asymmetric and thus permits evaluating coupling direction. Connectivity based on directed cross-bispectrum of scalp EEG was recently proposed as a novel biomarker of Alzheimer's disease [17] and showed promise for screening patients with Alzheimer's disease. We hypothesized that connectivity based on cross-bispectrum of iEEG will allow to identify the generators of seizure activity resulting in a better delineation of the SOZ.…”

Graph Neural Network-Based EEG Classification: A Survey

“…and biases in feature selection, along with time-consuming engineering and selection processes, limit scalability and generalisation [7], [12]. Automated feature extraction methods are needed to overcome these limitations, improve efficiency, reduce bias, and enhance classifier adaptability to different EEG datasets.…”

“…The brain exhibits a complex network structure, with neurons forming connections and communicating with each other [15]. Analysing EEG data as a graph enables the study of network properties, including functional connectivity, providing insights into brain function and dysfunction [12], [16], [17]. Graph-based analysis facilitates the examination of network features, node importance, community structure, and information flow, offering insights into brain organisation and dynamics.…”

“…Graph-based analysis facilitates the examination of network features, node importance, community structure, and information flow, offering insights into brain organisation and dynamics. Such graphtheory-based features were shown to be powerful predictive features for EEG classification [12], [17]- [22]. However, these features have the same limitations as manually defined features based on traditional EEG analysis methods introduced above.…”