Biology subjects

Blackburn, D. J.

Publications and source records attributed to Blackburn, D. J..

3 recordsLinked to original sources

bioRxiv2022

EEG Network Classification of Alzheimer's disease: Comparison of Functional Connectivity Methods

Alzheimers disease (AD) is the leading form of dementia worldwide. AD disrupts neuronal pathways and thus is commonly viewed as a network disorder. Many studies demonstrate the power of functional connectivity (FC) graph-based biomarkers for automated diagnosis of AD using electroencephalography (EEG). However, various FC measures are commonly utilised, as each aims to quantify a unique aspect of brain coupling. Graph neural networks (GNN) provide a powerful framework for learning on graphs. While a growing number of studies use GNN to classify EEG brain graphs, it is unclear which method should be utilised to estimate the brain graph. We use eight FC measures to estimate FC brain graphs from sensor-level EEG signals. GNN models are trained in order to compare the performance of the selected FC measures. Additionally, three baseline models based on literature are trained for comparison. We show that GNN models perform significantly better than the other baseline models. Moreover, using FC measures to estimate brain graphs improves the performance of GNN compared to models trained using a fixed graph based on the spatial distance between the EEG sensors. However, no FC measure performs consistently better than the other measures. The best GNN reaches 0.984 area under sensitivity-specificity curve (AUC) and 92% accuracy, whereas the best baseline model, a convolutional neural network, has 0.924 AUC and 84.7% accuracy.

neuroscience↗

bioRxiv2021

A Kernel-based Nonlinear Manifold Learning for EEG Channel Selection with Application to Alzheimer's Disease

For the characterisation and diagnosis of neurological disorders, dynamical, causal and crossfrequency coupling analysis using the EEG has gained considerable attention. Due to high computational costs in implementing some of these methods, the selection of important EEG channels is crucial. The channel selection method should be able to accommodate non-linear and spatiotemporal interactions among EEG channels. In neuroscience, different measures of (dis)similarity are used to quantify functional connectivity between EEG channels. Brain regions functionally connected under one measure do not necessarily imply the same with another measure, as they could even be disconnected. Therefore, developing a generic measure of (dis)similarity is important in channel selection. In this paper, learning of spatial and temporal structures within the data is achieved by using kernel-based nonlinear manifold learning, where the positive semi-definite kernel is a generalisation of various (dis)similarity measures. We introduce a novel EEG channel selection method to determine which channel interrelationships are more important for the in-depth neural dynamical analysis, such as understanding the effect of neurodegeneration, e.g. Alzheimers disease (AD), on global and local brain dynamics. The proposed channel selection methodology uses kernel-based nonlinear manifold learning via Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM). The Isomap-GPLVM method is employed to learn the spatial and temporal local similarities and global dissimilarities present within the EEG data structures. The resulting kernel (dis)similarity matrix is used as a measure of synchrony, i.e. linear and nonlinear functional connectivity, between EEG channels. Based on this information, linear Support Vector Machine (SVM) classification with Monte-Carlo cross-validation is then used to determine the most important spatio-temporal channel inter-relationships that can well distinguish a group of patients from a cohort of age-matched healthy controls (HC). In this work, the analysis of EEG data from HC and patients with mild to moderate AD is presented as a case study. Considering all pairwise EEG channel combinations, our analysis shows that functional connectivity between bipolar channels within temporal, parietal and occipital regions can distinguish well between mild to moderate AD and HC groups. Furthermore, while only considering connectivity with respect to each EEG channel. Our results indicate that connectivity of EEG channels along the fronto-parietal with other channels are important in diagnosing mild to moderate AD.

neuroscience↗

bioRxiv2021

Bispectrum-based Cross-frequency Functional Connectivity: A Study of Alzheimer's Disease

Alzheimers 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 reconstruction of 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. Cross-bispectrum detects cross-frequency differences, mainly increased FC in AD cases in{delta} -{theta} coupling. Overall, increased strength of low-frequency coupling and decreased level of high-frequency coupling is observed in AD cases in comparison to healthy controls (HC). We demonstrate that a graph-theoretic analysis of cross-frequency brain networks is crucial to obtain a more detailed insight into their structure and function. Vulnerability analysis reveals that the integration and segregation properties of networks are enabled by different frequency couplings in AD networks compared to HCs. Finally, we use the reconstructed networks for classification. The extra cross-frequency coupling information can improve the classification performance significantly, suggesting an important role of cross-frequency FC. The results highlight the importance of studying nonlinearity and including cross-frequency FC in characterising AD.

neuroscience↗