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.

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. This article reviews EEG-based functional and effective connectivity studies undertaken over the last few years, in terms of estimation, visualization, and applications associated with machine learning classifiers. Methods are explored and discussed from various dimensions, such as either linear or nonlinear, parametric or nonparametric, time-based, and frequency-based or time-frequency-based. Then it is followed by a novel review of brain connectivity visualization methods, grouped by Heat Map, data statistics, and Head Map, aiming to explore the variation of connectivity across different brain regions. Finally, the current challenges of related research and a roadmap for future related research are presented.

Sudomotor dysfunction in patients with gluten neuropathy.

BACKGROUND AND AIM: Gluten neuropathy (GN) is a common neurological manifestation of gluten sensitivity (GS), characterized by serological evidence of GS, while other risk factors for developing neuropathy are absent. The degree of small fiber dysfunction in GN has not been studied in depth to date. Small fiber involvement may lead to pain, thermal perception abnormalities, and sweat gland dysfunction. Sudomotor innervation refers to the cholinergic innervation of the sympathetic nervous system through small fibers in the sweat glands. The aim of our study was to assess the sudomotor function of GN patients. METHODS: Patients with GN were recruited. Clinical and neurophysiological data were obtained. HLA-DQ genotyping was performed. The skin electrochemical conductance (ESC) was measured with SUDOSCANTM. RESULTS: Thirty-two patients (25 males, mean age 69.5±10.2 years) were recruited. Thirteen patients (40.6%) had abnormal sudomotor function of the hands. Sixteen patients (50%) had abnormal sudomotor function of the feet. Twenty-one patients (65.6%) had abnormal sudomotor function of either the hands or feet. Sudomotor dysfunction did not correlate with the type of neuropathy (length-dependent neuropathy or sensory ganglionopathy), gluten-free diet adherence, severity of neuropathy, and duration of disease or HLA-DQ genotype. No differences in the ESC were found between patients with painful and patients with painless GN. CONCLUSION: Sudomotor dysfunction affects two-thirds of patients with GN. The lack of correlation between pain and sudomotor dysfunction suggests different patterns of small fiber involvement in patients with GN.

Chronic idiopathic axonal polyneuropathy: Electrophysiological progression and human leukocyte antigen associations.

BACKGROUND: We aimed to describe the electrophysiological progression rate of chronic idiopathic axonal polyneuropathy (CIAP) and look into the potential role of human leukocyte antigen (HLA) genetic susceptibility in its development. METHODS: We recruited 57 patients with CIAP (mean age at diagnosis 67, mean follow-up 7 years). The assessments included clinical and electrophysiological data and HLA-DQ genotyping. RESULTS: The DQA1*05 allele was found more frequently in patients than in healthy controls (odds ratio, 1.96, P = .011). In patients with length-dependent CIAP, a linear effect of time on the electrophysiological findings was found in the superficial radial (3.2% mean annual decrement, P < .001), sural (4.7% mean annual decrement, P = .002) and tibial nerve (6.1% mean annual decrement, P = .007) amplitudes, independently from age or gender. CONCLUSIONS: Patients with length-dependent CIAP, show a linear progression over time. Interesting associations of HLA-DQA1*05 allele with length-dependent CIAP and non-DQ2/DQ8 with idiopathic sensory ganglionopathy were found. These merit further investigation in larger cohorts and may suggest a role of the immune system in the pathogenesis of CIAP.

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. The proposed diagnostic criteria for PACA are based on clinical (mode of onset, pattern of cerebellar involvement, presence of other autoimmune diseases), imaging findings (MRI and if available MR spectroscopy showing preferential, but not exclusive involvement of vermis) and laboratory investigations (CSF pleocytosis and/or CSF-restricted IgG oligoclonal bands) parameters. The aim is to enable clinicians to consider PACA when encountering a patient with progressive ataxia and no other diagnosis given that such consideration might have important therapeutic implications.

Safety, Tolerability, and Nocebo Phenomena During Transcranial Magnetic Stimulation: A Systematic Review and Meta-Analysis of Placebo-Controlled Clinical Trials.

BACKGROUND: The methodology used for the application of repetitive transcranial magnetic stimulation (TMS) is such that it may induce a placebo effect. Respectively, adverse events (AEs) can occur when using a placebo, a phenomenon called nocebo. The primary aim of our meta-analysis is to establish the nocebo phenomena during TMS. Safety and tolerability of TMS were also studied. METHODS: After a systematic Medline search for TMS randomized controlled trials (RCTs), we assessed the number of patients reporting at least one AE and the number of discontinuations because of AE in active and sham TMS groups. RESULTS: Data were extracted from 93 RCTs. The overall pooled estimate of active TMS and placebo treated patients who discontinued treatment because of AEs was 2.5% (95% CI 1.9%-3.2%) and 2.7% (95% CI 2.0%-3.5%), respectively. The pooled estimate of active TMS and placebo treated patients experiencing at least one AE was 29.3% (95% CI 19.0%-22.6%) and 13.6% (95% CI 11.6%-15.8%), respectively, suggesting that the odds of experiencing an AE is 2.60 times higher (95% CI 1.75-3.86) in the active treatment group compared to placebo (p < 0.00001). The most common AE was headache, followed by dizziness. Secondary meta-analyses in depression and psychotic disorders showed that the odds of experiencing an AE is 3.98 times higher (95% CI 2.14-7.40) and 2.93 times higher (95% CI 1.41-6.07), respectively, in the active treatment groups compared to placebo. CONCLUSIONS: TMS is a safe and well-tolerated intervention. Nocebo phenomena do occur during TMS treatment and should be acknowledged during clinical trial design and daily clinical practice.

Neurologic Deficits in Patients With Newly Diagnosed Celiac Disease Are Frequent and Linked With Autoimmunity to Transglutaminase 6.

BACKGROUND & AIMS: Celiac disease is an autoimmune disorder induced by ingestion of gluten that affects 1% of the population and is characterized by gastrointestinal symptoms, weight loss, and anemia. We evaluated the presence of neurologic deficits and investigated whether the presence of antibodies to Transglutaminase 6 (TG6) increases the risk of neurologic defects in patients with a new diagnosis of celiac disease. METHODS: We performed a prospective cohort study at a secondary-care gastroenterology center of 100 consecutive patients who received a new diagnosis of celiac disease based on gastroscopy and duodenal biopsy. We collected data on neurologic history, and patients were evaluated in a clinical examination along with magnetic resonance imaging of the brain, magnetic resonance (MR) spectroscopy of the cerebellum, and measurements of antibodies against TG6 in serum samples. The first 52 patients recruited underwent repeat MR spectroscopy at 1 year after a gluten-free diet (GFD). The primary aim was to establish if detection of antibodies against TG6 can be used to identify patients with celiac disease and neurologic dysfunction. RESULTS: Gait instability was reported in 24% of the patients, persisting sensory symptoms in 12%, and frequent headaches in 42%. Gait ataxia was found in 29% of patients, nystagmus in 11%, and distal sensory loss in 10%. Sixty percent of patients had abnormal results from magnetic resonance imaging, 47% had abnormal results from MR spectroscopy of the cerebellum, and 25% had brain white matter lesions beyond that expected for their age group. Antibodies against TG6 were detected in serum samples from 40% of patients-these patients had significant atrophy of subcortical brain regions compared with patients without TG6 autoantibodies. In patients with abnormal results from MR spectroscopy of the cerebellum, those on the GFD had improvements detected in the repeat MR spectroscopy 1 year later. CONCLUSIONS: In a prospective cohort study of patients with a new diagnosis of celiac disease at a gastroenterology clinic, neurologic deficits were common and 40% had circulating antibodies against TG6. We observed a significant reduction in volume of specific brain regions in patients with TG6 autoantibodies, providing evidence for a link between autoimmunity to TG6 and brain atrophy in patients with celiac disease. There is a need for early diagnosis, increased awareness of the neurologic manifestations among clinicians, and reinforcement of adherence to a strict GFD by patients to avoid permanent neurologic disability.

Electrophysiological determinants of the clinical severity of axonal peripheral neuropathy.

INTRODUCTION: Electrophysiological diagnosis of axonal peripheral neuropathy (PN) is based on the attenuated amplitudes of nerve conduction studies (NCS), or a reduced sural/radial amplitude ratio (SRAR). We aimed to identify the electrophysiological determinants of the clinical severity of PN. METHODS: Patients with chronic axonal PN underwent detailed NCS. The clinical severity of PN was determined based upon the overall neuropathy limitations scale (ONLS). RESULTS: Ninety-five patients (71.6% males, mean age 71.9 ± 9.0 years) were recruited. Significant correlations were observed between the radial sensory nerve action potential (SNAP) and the ONLS total score (Spearman's rho -0.382, p < 0.001); and between the tibial compound muscle action potential and the ONLS leg score (Spearman's rho -0.283, p = 0.005). No correlations between the SRAR and the ONLS scores were found. DISCUSSION: The radial SNAP is the strongest electrophysiological determinant of PN severity and might be useful for monitoring disease progression or response to treatment. Muscle Nerve 59:491-493, 2019.

Neurological Dysfunction in Coeliac Disease and Non-Coeliac Gluten Sensitivity.

OBJECTIVES: Non-coeliac gluten sensitivity (NCGS) refers to patients with primarily gastrointestinal symptoms without enteropathy that symptomatically benefit from gluten-free diet (GFD). Little is known about its pathophysiology, propensity to neurological manifestations, and if these differ from patients with coeliac disease (CD). We investigated the clinical and immunological characteristics of patients presenting with neurological manifestations with CD and those with NCGS. METHODS: We compared clinical, neurophysiological, and imaging data of patients with CD and NCGS presenting with neurological dysfunction assessed and followed up regularly over a period of 20 years. RESULTS: Out of 700 patients, 562 were included. Exclusion criteria included no bowel biopsy to confirm CD, no HLA type available, and failure to adhere to GFD. All patients presented with neurological dysfunction and had circulating anti-gliadin antibodies. Out of 562 patients, 228 (41%) had evidence of enteropathy (Group 1, CD) and 334 (59%) did not (Group 2, NCGS). The most common neurological manifestations were cerebellar ataxia, peripheral neuropathy, and encephalopathy. There was a greater proportion of patients with encephalopathy in Group 1 and with a greater proportion of neuropathy in Group 2. The severity of ataxia did not differ between the two groups. Patients in Group 1 had more severe neuropathy. All patients from both groups responded to gluten-free diet. Anti-tissue transglutaminase (TG2) antibodies were found in 91% of patients in Group 1 and in 29% of patients in Group 2. Comparison between those patients in Group 2 with HLA-DQ2/DQ8 and those without as well as those with positive TG2 compared with those with negative TG2 antibodies identified no differences within these subgroups. Serological positivity for TG6 antibodies was similar in the two groups (67 and 60%). CONCLUSIONS: The neurological manifestations of CD and NCGS are similar and equally responsive to a GFD suggestive of common pathophysiological mechanisms.

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.

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. Developing a generic measure of (dis) similarity is important for FC analysis and channel selection. In this study, learning of (dis) similarity information within the EEG is achieved using kernel-based nonlinear manifold learning. The focus is on FC changes and, thereby, EEG channel selection. Isomap and Gaussian Process Latent Variable Model (Isomap-GPLVM) are employed for this purpose. The resulting kernel (dis) similarity matrix is used as a novel measure of linear and nonlinear FC between EEG channels. The analysis of EEG from healthy controls (HC) and patients with mild to moderate Alzheimer's disease (AD) are presented as a case study. Classification results are compared with other commonly used FC measures. Our analysis shows significant differences in FC between bipolar channels of the occipital region and other regions (i.e. parietal, centro-parietal, and fronto-central) between AD and HC groups. Furthermore, our results indicate that FC changes between channels along the fronto-parietal region and the rest of the EEG are important in diagnosing AD. Our results and its relation to functional networks are consistent with those obtained from previous studies using fMRI, resting-state fMRI and EEG.

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.

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.

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 Haemophilus influenzae and Fusobacterium, respectively, the former being the most probable trigger of BBE. Our patient's death, despite the otherwise good prognosis of the disorder, may reflect the severity of the pathological changes at postmortem or the association of comorbid disorders such as sepsis-associated encephalopathy. Her poor outcome may also be an indication to treat rapidly progressive cases of BBE with more than one immune modulating drug.

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.

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. Cross-bispectrum, a higher-order spectral analysis, is used to measure the nonlinear FC and is compared with the cross-spectrum, which only measures the linear FC within bands. Each frequency coupling is then used to construct an FC network, which is in turn vectorised and used to train a classifier. We show that fusing features from networks improves classification accuracy. Although both within-frequency and cross-frequency networks can be used to predict AD with high accuracy, our results show that bispectrum-based FC outperforms cross-spectrum suggesting an important role of cross-frequency FC. Clinical relevance-This establishes diagnostic relevance of cross-frequency coupling in Alzheimer's disease.

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. 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 age-matched healthy counterparts (HC), significant differences are found. The dynamics of AD patients' EEG are 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. Moreover, these findings are replicated in a separate dataset including 9 AD and 10 HC above 70 years old.

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.

Stretch syncope: reflex vasodepressor faints easily mistaken for epilepsy.

The pathophysiology of stretch syncope is demonstrated through the clinical, electrophysiological, and hemodynamic findings in three patients. Fifty-seven attacks were captured by video/EEG monitoring. Simultaneous EEG, transcranial (middle cerebral artery) doppler, and continuous arterial pressure measurements were obtained for at least one typical attack of each patient. They all experienced a compulsion to precipitate their attacks. Episodes started with a stereotyped phase of stretching associated with neck torsion and breath holding, followed by a variable degree of loss of consciousness and asymmetric, recurrent facial and upper limb jerks in the more prolonged episodes. Significant sinus tachycardia coincided with the phase of stretching and was followed within 9-16 seconds by rhythmic generalized slow wave abnormalities on the EEG in attacks with impairment of consciousness. Transcranial doppler studies showed a dramatic drop in cerebral perfusion in the middle cerebral arteries during the episodes. The combination of the stereotyped semiology of the attacks, the pseudofocal myoclonic jerking, and the rhythmic generalized slow wave EEG abnormalities with the tachycardia make differential diagnosis from epilepsy challenging.