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

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

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

Increased oxidative stress as a risk factor in chronic idiopathic axonal polyneuropathy

Chronic idiopathic axonal polyneuropathy (CIAP) is a disorder with insidious onset and slow progression, where no etiology is identified despite appropriate investigations. We aimed to investigate the role of oxidative stress as a risk factor for the pathogenesis of CIAP. Sera of patients with CIAP were tested for protein carbonyl (PC) and 8-hydroxydeoxyguanosine (8H). As a control group, we recruited patients with gluten neuropathy. Twenty-one patients with CIAP and 21 controls were recruited. The two groups did not differ significantly regarding demographics or clinical characteristics (i.e., neuropathy type or disease severity). After adjusting for gender, having CIAP was positively correlated with both the 8H titer (standardized beta coefficient 0.349, p = 0.013) and the PC titer (standardized beta coefficient 0.469, p = 0.001). Oxidative stress appears to be increased in CIAP and might have a role in the pathogenesis of the disease

Quality of Life in Patients with Gluten Neuropathy: A Case-Controlled Study

Background: Gluten neuropathy (GN) is defined as an otherwise idiopathic peripheral neuropathy in the presence of serological evidence of gluten sensitivity (positive native gliadin antibodies and/or transglutaminase or endomysium antibodies). We aimed to compare the quality of life (QoL) of GN patients with that of control subjects and to investigate the effects of a gluten-free diet (GFD) on the QoL. Methods: All consecutive patients with GN attending a specialist neuropathy clinic were invited to participate. The Overall Neuropathy Limitations Scale (ONLS) was used to assess the severity of the neuropathy. The 36-Item Short Form Survey (SF-36) questionnaire was used to measure participants’ QoL. A strict GFD was defined as effectively being able to eliminate all circulating gluten sensitivity-related antibodies. Results: Fifty-three patients with GN and 53 age- and gender-matched controls were recruited. Compared to controls, GN patients showed significantly worse scores in the physical functioning, role limitations due to physical health, energy/fatigue, and general health subdomains of the SF-36. After adjusting for age, gender, and disease severity, being on a strict GFD correlated with better SF-36 scores in the pain domain of the SF-36 (beta 0.317, p = 0.019) and in the overall health change domain of the SF-36 (beta 0.306, p = 0.017). Conclusion: In GN patients, physical dysfunctioning is the major determinant of poor QoL compared to controls. Routine checking of the elimination of gluten sensitivity-related antibodies that results from a strict GFD should be encouraged, as such elimination ameliorates the overall pain and health scores, indicating a better QoL

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. Different from the traditional graph convolutional neural networks, the proposed ST-GCN makes full use of the constrained spatial topology of functional connectivity and the discriminative dynamic temporal information represented by the 1D convolution. We conducted extensive experiments on the clinical EEG data set of AD patients and Healthy Controls. The results demonstrate that the proposed method achieves better classification performance (92.3%) than the state-of-the-art methods. This approach can not only help diagnose AD but also better understand the effect of normal ageing on brain network characteristics before we can accurately diagnose the condition based on resting-state EEG

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). The framework can estimate the cross-spectrum of decomposed components of EEG, followed by a surrogate significance test. The results of two simulation examples demonstrate that, within a certain statistical confidence level, the proposed framework outperforms the wavelet-based method in terms of accuracy and time-frequency resolution. A case study on classifying epileptic patients and healthy controls using interictal seizure-free EEG data is also presented. The result suggests that the proposed method has the potential to better differentiate these two groups benefiting from the enhanced measure of dynamic time-frequency association.Not heldAccepted versio