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.

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.

Kernel-based Nonlinear Manifold Learning for EEG-based Functional Connectivity Analysis and Channel Selection with Application to Alzheimer's Disease.

Dynamical, causal, and cross-frequency coupling analysis using the electroencephalogram (EEG) has gained significant attention for diagnosing and characterizing neurological disorders. Selecting important EEG channels is crucial for reducing computational complexity in implementing these methods and improving classification accuracy. In neuroscience, measures of (dis) similarity between EEG channels are often used as functional connectivity (FC) features, and important channels are selected via feature selection.

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.

Bispectrum-based Cross-frequency Functional Connectivity: Classification of Alzheimer's disease.

Alzheimer's disease (AD) is a neurodegenerative disease known to affect brain functional connectivity (FC). 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. We address this limitation by quantifying cross-frequency FC in addition to the traditional within-band approach.

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).

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

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).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).

Spatial-temporal graph convolutional network for Alzheimer classification based on brain functional connectivity imaging of electroencephalogram.

Functional connectivity of the human brain, representing statistical dependence of information flow between cortical regions, significantly contributes to the study of the intrinsic brain network and its functional mechanism. To fully explore its potential in the early diagnosis of Alzheimer's disease (AD) using electroencephalogram (EEG) recordings, this article introduces a novel dynamical spatial-temporal graph convolutional neural network (ST-GCN) for better classification performance. Different from existing studies that are based on either topological brain function characteristics or temporal features of EEG, the proposed ST-GCN considers both the adjacency matrix of functional connectivity from multiple EEG channels and corresponding dynamics of signal EEG channel simultaneously.

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.

Bickerstaff brainstem encephalitis: clinical, neurophysiological, laboratory and postmortem findings of a case presenting as encephalomyelitis.

Our patient was admitted to hospital with a 1-week history of an upper respiratory tract infection and a rapidly progressive encephalopathy dominated by brainstem features and widespread areflexia. Her antiganglioside antibodies and electroencephalography were consistent with Bickerstaff brainstem encephalitis (BBE), and her postmortem examination revealed a predominantly florid brainstem encephalitis and myelitis. Her sputum and throat swabs isolated and respectively, the former being the most probable trigger of BBE.

Brain functional and effective connectivity based on electroencephalography recordings: A review.

Functional connectivity and effective connectivity of the human brain, representing statistical dependence and directed information flow between cortical regions, significantly contribute to the study of the intrinsic brain network and its functional mechanism. Many recent studies on electroencephalography (EEG) have been focusing on modeling and estimating brain connectivity due to increasing evidence that it can help better understand various brain neurological conditions. However, there is a lack of a comprehensive updated review on studies of EEG-based brain connectivity, particularly on visualization options and associated machine learning applications, aiming to translate those techniques into useful clinical tools.

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.

Glycine receptor antibodies and coeliac disease-related neurological dysfunction.

Gluten sensitivity can manifest with a spectrum of neurological dysfunction including ataxia, encephalopathy and neuropathy with or without associated coeliac disease (CD). Gluten sensitivity can also present with central nervous system (CNS) hyperexcitability and cortical myoclonus which is often accompanied with refractory CD. CNS hyperexcitability can also be associated with Glutamic Acid Decarboxylase (GAD) antibodies or much less commonly with Glycine Receptor Antibodies (GlyR-Abs) but the direct pathogenic roles of these antibodies remain debatable.

Stiff Person Syndrome and Gluten Sensitivity.

Stiff person syndrome (SPS) is a rare autoimmune disease characterised by axial stiffness and episodic painful spasms. It is associated with additional autoimmune diseases and cerebellar ataxia. Most patients with SPS have high levels of glutamic acid decarboxylase (GAD) antibodies.

A Revised Hilbert-Huang Transformation to Track Non-Stationary Association of Electroencephalography Signals.

The time-varying cross-spectrum method has been used to effectively study transient and dynamic brain functional connectivity between non-stationary electroencephalography (EEG) signals. Wavelet-based cross-spectrum is one of the most widely implemented methods, but it is limited by the spectral leakage caused by the finite length of the basic function that impacts the time and frequency resolutions. This paper proposes a new time-frequency brain functional connectivity analysis framework to track the non-stationary association of two EEG signals based on a Revised Hilbert-Huang Transform (RHHT).

Autologous haematopoietic stem cell transplantation for refractory stiff-person syndrome: the UK experience.

Unlabelled: Stiff Person Syndrome (SPS) is a rare immune-mediated disabling neurological disorder characterised by muscle spasms and high GAD antibodies. There are only a few case reports of autologous haematopoietic stem cell transplantation (auto-HSCT) as a treatment for SPS.

Objective: To describe the UK experience of treating refractory SPS with auto-HSCT.

Gluten neuropathy: electrophysiological progression and HLA associations.

Objective: Gluten neuropathy (GN) is the term used to describe peripheral neuropathy that occurs in patients with gluten sensitivity (GS) or coeliac disease (CD) in the absence of other risk factors. We aimed to describe the neurophysiological progression rate of GN across time and look into the potential role of genetic susceptibility in its development.

Methods: This is a cohort study of 45 patients with GN with a mean follow-up period of 8 ± 5 years.

Diagnostic Criteria for Primary Autoimmune Cerebellar Ataxia-Guidelines from an International Task Force on Immune-Mediated Cerebellar Ataxias.

Aside from well-characterized immune-mediated ataxias with a clear trigger and/or association with specific neuronal antibodies, a large number of idiopathic ataxias are suspected to be immune mediated but remain undiagnosed due to lack of diagnostic biomarkers. Primary autoimmune cerebellar ataxia (PACA) is the term used to describe this later group. An International Task Force comprising experts in the field of immune ataxias was commissioned by the Society for Research on the Cerebellum and Ataxias (SRCA) in order to devise diagnostic criteria aiming to improve the diagnosis of PACA.

Repetitive Magnetic Stimulation for the Management of Peripheral Neuropathic Pain: A Systematic Review.

Introduction: Repetitive magnetic stimulation (rMS) is a safe and well-tolerated intervention. Transcranial magnetic stimulation (TMS) is used for the treatment of depression and for the treatment and prevention of migraine. Over the last few years, several reports and randomised controlled studies of the use of rMS for the treatment of pain have been published.

Pain as a determinant of health-related quality of life in patients with carpal tunnel syndrome; a case-controlled study.

: Carpal tunnel syndrome (CTS) is an entrapment neuropathy accounting for up to 90% of nerve compression syndromes. It causes both positive and negative symptoms in the hands. These symptoms, especially pain, can be debilitating, which can in turn have a negative effect on patients' quality of life (QoL).