EEG-based Graph Neural Network Classification of Alzheimer's Disease:An Empirical Evaluation of Functional Connectivity Methods

Alzheimer’s 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

Adaptive Gated Graph Convolutional Network for Explainable Diagnosis of Alzheimer’s Disease using EEG Data

Graph neural network (GNN) models are increasingly being used for the classification of electroencephalography (EEG) data. However, GNN-based diagnosis of neurological disorders, such as Alzheimer's disease (AD), remains a relatively unexplored area of research. Previous studies have relied on functional connectivity methods to infer brain graph structures and used simple GNN architectures for the diagnosis of AD. In this work, we propose a novel adaptive gated graph convolutional network (AGGCN) that can provide explainable predictions. AGGCN adaptively learns graph structures by combining convolution-based node feature enhancement with a correlation-based measure of power spectral density similarity. Furthermore, the gated graph convolution can dynamically weigh the contribution of various spatial scales. The proposed model achieves high accuracy in both eyes-closed and eyes-open conditions, indicating the stability of learned representations. Finally, we demonstrate that the proposed AGGCN model generates consistent explanations of its predictions that might be relevant for further study of AD-related alterations of brain networks.Comment: 16 pages, 16 figure

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

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 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 δ-θ 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.UnknownSupports Open Acces

Characterising Alzheimer's Disease with EEG-based Energy Landscape Analysis

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases, with around 50 million patients worldwide. Accessible and non-invasive methods of diagnosing and characterising AD are therefore urgently required. Electroencephalography (EEG) fulfils these criteria and is often used when studying AD. Several features derived from EEG were shown to predict AD with high accuracy, e.g. signal complexity and synchronisation. However, the dynamics of how the brain transitions between stable states have not been properly studied in the case of AD and EEG data. Energy landscape analysis is a method that can be used to quantify these dynamics. This work presents the first application of this method to both AD and EEG. Energy landscape assigns energy value to each possible state, i.e. pattern of activations across brain regions. The energy is inversely proportional to the probability of occurrence. By studying the features of energy landscapes of 20 AD patients and 20 healthy age-matched counterparts, significant differences were found. The dynamics of AD patients' brain networks were shown to be more constrained - with more local minima, less variation in basin size, and smaller basins. We show that energy landscapes can predict AD with high accuracy, performing significantly better than baseline models.Comment: 11 pages, 7 figure

Dementia classification using a graph neural network on imaging of effective brain connectivity

Alzheimer's disease (AD) and Parkinson's disease (PD) are two of the most common forms of neurodegenerative diseases. The literature suggests that effective brain connectivity (EBC) has the potential to track differences between AD, PD and healthy controls (HC). However, how to effectively use EBC estimations for the research of disease diagnosis remains an open problem. To deal with complex brain networks, graph neural network (GNN) has been increasingly popular in very recent years and the effectiveness of combining EBC and GNN techniques has been unexplored in the field of dementia diagnosis. In this study, a novel directed structure learning GNN (DSL-GNN) was developed and performed on the imaging of EBC estimations and power spectrum density (PSD) features. In comparison to the previous studies on GNN, our proposed approach enhanced the functionality for processing directional information, which builds the basis for more efficiently performing GNN on EBC. Another contribution of this study is the creation of a new framework for applying univariate and multivariate features simultaneously in a classification task. The proposed framework and DSL-GNN are validated in four discrimination tasks and our approach exhibited the best performance, against the existing methods, with the highest accuracy of 94.0% (AD vs. HC), 94.2% (PD vs. HC), 97.4% (AD vs. PD) and 93.0% (AD vs. PD vs. HC). In a word, this research provides a robust analytical framework to deal with complex brain networks containing causal directional information and implies promising potential in the diagnosis of two of the most common neurodegenerative conditions

A wavelet-based correlation analysis framework to study cerebromuscular activity in essential tremor

Deep brain stimulation (DBS) provides dramatic tremor relief in patients with severe essential tremor (ET). Typically, the VIM nucleus is the most effective brain area to target for high-frequency electrical stimulation in these patients. Correlation analysis between electrical local field potential (LFP) recordings from the thalamic DBS leads and electrical muscle activity from the contralateral tremulous limb has become an attractive practical tool to interpret the LFPs and their association with the tremulous clinical manifestations. Although functional connectivity analysis between brain electrical recordings and electromyographic (EMG) signals from the tremor has been of interest to an increasing number of engineering researchers, there is no well-accepted tailored framework to consistently characterise the association between thalamic electrical recordings and the tremorogenic EMG activity. Methods. This paper proposes a novel framework to address this challenge, including an estimation of the interaction strength using wavelet cross-spectrum and phase lag index while demonstrating the statistical significance of the findings. Results. Consistent results were estimated for single and multiple trials of consecutive or partially overlapping epochs of data. The latter approach reveals a substantial increase on the range of statistically significant dynamic low-frequency interrelationships while decreasing the dynamic range of high-frequency interactions. Conclusion. Results from both simulation and real data demonstrate the feasibility and robustness of the proposed framework. Significance. This study offers the proof of principle required to implement this methodology to uncover VIM thalamic LFP-EMG interactions for (i) better understanding of the pathophysiology of tremor; (ii) objective selection of the DBS electrode contacts with the highest strength of association with the tremorogenic EMG, a particularly useful feature for the implementation of novel multicontact directional leads in clinical practice; and (iii) future research on DBS closed-loop devices

Transglutaminase 6 antibodies in gluten neuropathy

BACKGROUND: TG6 antibodies have been shown to be a marker of gluten ataxia but their presence in the context of other neurological manifestations of gluten sensitivity has not been explored. We investigated the presence of TG6 antibodies in gluten neuropathy (GN), defined as as an otherwise idiopathic peripheral neuropathy associated with serological markers of gluten sensitivity (one or more of antigliadin IgG and/or IgA, endomysial and transglutaminase-2 antibodies). METHODS: This was a cross-sectional study conducted at the Sheffield Institute of Gluten Related Diseases, Royal Hallamshire Hospital, Sheffield, UK. Blood samples were collected whilst the patients were on a gluten containing diet. Duodenal biopsies were performed to establish the presence of enteropathy. RESULTS: Twenty-eight patients were recruited (mean age 62.5±13.7 years). Fifteen (53.6%) had sensory ganglionopathy, 12 (42.9%) had symmetrical axonal neuropathy and 1 had mononeuritis multiplex. The prevalence of TG6 antibodies was 14 of 28 (50%) compared to 4% in the healthy population. TG6 antibodies were found in 5/15 (33.3%) patients with sensory ganglionopathy and in 8/12 (66.7%) with symmetrical axonal neuropathy. Twenty-four patients underwent duodenal biopsy 11 (45.8%) of which had enteropathy. The prevalence of TG6 was not significantly different when comparing those with or without enteropathy. CONCLUSIONS: We found a high prevalence of antibodies against TG6 in patients with GN. This suggests that TG6 involvement is not confined to the central nervous system. The role of transglutaminase 6 in peripheral nerve function remains to be determined but TG6 antibodies may be helpful in the diagnosis of GN

Ultra-high-resolution time-frequency analysis of EEG to characterise brain functional connectivity with the application in Alzheimer's disease

Objective. This study aims to explore the potential of high-resolution brain functional connectivity based on electroencephalogram, a non-invasive low-cost technique, to be translated into a long-overdue biomarker and a diagnostic method for Alzheimer's disease (AD). Approach. The paper proposes a novel ultra-high-resolution time-frequency nonlinear cross-spectrum method to construct a promising biomarker of AD pathophysiology. Specifically, using the peak frequency estimated from a revised Hilbert–Huang transformation (RHHT) cross-spectrum as a biomarker, the support vector machine classifier is used to distinguish AD from healthy controls (HCs). Main results. With the combinations of the proposed biomarker and machine learning, we achieved a promising accuracy of 89%. The proposed method performs better than the wavelet cross-spectrum and other functional connectivity measures in the temporal or frequency domain, particularly in the Full, Delta and Alpha bands. Besides, a novel visualisation approach developed from topography is introduced to represent the brain functional connectivity, with which the difference between AD and HCs can be clearly displayed. The interconnections between posterior and other brain regions are obviously affected in AD. Significance. Those findings imply that the proposed RHHT approach could better track dynamic and nonlinear functional connectivity information, paving the way for the development of a novel diagnostic approach

EEG recordings as biomarkers of pain perception: where do we stand and where to go?

Introduction: The universality and complexity of pain, which is highly prevalent, yield its significance to both patients and researchers. Developing a non-invasive tool that can objectively measure pain is of the utmost importance for clinical and research purposes. Traditionally electroencephalography (EEG) has been mostly used in epilepsy; however, over the recent years EEG has become an important non-invasive clinical tool that has helped increase our understanding of brain network complexities and for the identification of areas of dysfunction. This review aimed to investigate the role of EEG recordings as potential biomarkers of pain perception. Methods: A systematic search of the PubMed database led to the identification of 938 papers, of which 919 were excluded as a result of not meeting the eligibility criteria, and one article was identified through screening of the reference lists of the 19 eligible studies. Ultimately, 20 papers were included in this systematic review. Results: Changes of the cortical activation have potential, though the described changes are not always consistent. The most consistent finding is the increase in the delta and gamma power activity. Only a limited number of studies have looked into brain networks encoding pain perception. Conclusion: Although no robust EEG biomarkers of pain perception have been identified yet, EEG has potential and future research should be attempted. Designing strong research protocols, controlling for potential risk of biases, as well as investigating brain networks rather than isolated cortical changes will be crucial in this attempt