Ptolemaios G. Sarrigiannis

Phenytoin for neuroprotection in patients with acute optic neuritis: a randomised, placebo-controlled, phase 2 trial

BACKGROUND Acute demyelinating optic neuritis, a common feature of multiple sclerosis, can damage vision through neurodegeneration in the optic nerve and in its fibres in the retina. Inhibition of voltage-gated sodium channels is neuroprotective in preclinical models. In this study we aimed to establish whether sodium-channel inhibition with phenytoin is neuroprotective in patient with acute optic neuritis. METHODS We did a randomised, placebo-controlled, double-blind phase 2 trial at two UK academic hospitals in London and Sheffield. Patients with acute optic neuritis aged 18-60 years, presenting within 2 weeks of onset, with visual acuity of 6/9 or worse, were randomly assigned (1:1) by minimisation via a web-based service to oral phenytoin (maintenance dose 4 mg/kg per day if randomised before or on July 16, 2013, and 6 mg/kg per day if randomised on or after July 17, 2013) or placebo for 3 months, stratified by time from onset, centre, previous multiple sclerosis diagnosis, use of disease-modifying treatment, and use of corticosteroids for acute optic neuritis. Participants and treating and assessing physicians were masked to group assignment. The primary outcome was retinal nerve fibre layer (RNFL) thickness in the affected eye at 6 months, adjusted for fellow-eye RNFL thickness at baseline, analysed in a modified intention-to-treat population of all randomised participants who were followed up at 6 months. Safety was analysed in the entire population, including those who were lost to follow-up. The trial is registered with ClinicalTrials.gov, number NCT 01451593. FINDINGS We recruited 86 participants between Feb 3, 2012, and May 22, 2014 (42 assigned to phenytoin and 44 to placebo). 29 were assigned to phenytoin 4 mg/kg and 13 to phenytoin 6 mg/kg. Five participants were lost to follow-up, so the primary analysis included 81 participants (39 assigned to phenytoin and 42 to placebo). Mean 6-month RNFL thickness in the affected eye at 6 months was 81.46 μm (SD 16.27) in the phenytoin group (a mean decrease of 16.69 μm [SD 13.73] from baseline) versus 74.29 μm (15.14) in the placebo group (a mean decrease of 23.79 μm [13.97] since baseline; adjusted 6-month difference of 7.15 μm [95% CI 1.08-13.22]; p=0.021), corresponding to a 30% reduction in the extent of RNFL loss with phenytoin compared with placebo. Treatment was well tolerated, with five (12%) of 42 patients having a serious adverse event in the phenytoin group (only one, severe rash, was attributable to phenytoin) compared with two (5%) of 44 in the placebo group. INTERPRETATION These findings support the concept of neuroprotection with phenytoin in patients with acute optic neuritis at concentrations at which it blocks voltage-gated sodium channels selectively. Further investigation in larger clinical trials in optic neuritis and in relapsing multiple sclerosis is warranted. FUNDING US National Multiple Sclerosis Society, Multiple Sclerosis Society of Great Britain and Northern Ireland, Novartis, UK National Institute for Health Research (NIHR), and NIHR UCLH/UCL Biomedical Research Centre.

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.

Time-varying model identification for time–frequency feature extraction from EEG data

A novel modelling scheme that can be used to estimate and track time-varying properties of nonstationary signals is investigated. This scheme is based on a class of time-varying AutoRegressive with an eXogenous input (TVARX) models where the associated time-varying parameters are represented by multi-wavelet basis functions. The orthogonal least square (OLS) algorithm is then applied to refine the model parameter estimates of the TVARX model. The main features of the multi-wavelet approach is that it enables smooth trends to be tracked but also to capture sharp changes in the time-varying process parameters. Simulation studies and applications to real EEG data show that the proposed algorithm can provide important transient information on the inherent dynamics of nonstationary processes.

A new NARX-based Granger linear and nonlinear casual influence detection method with applications to EEG data.

A new NARX-based Granger linear and nonlinear casual influence detection method is presented in this paper to address the potential for linear and nonlinear models in data with applications to human EEG data analysis. Considering two signals initially, the paper introduces four indexes to measure the linearity and nonlinearity of a single signal, and one signal influencing the second signal. This method is then extended to the time-varying and multivariate cases. An adaptation of an Orthogonal Least Squares routine is employed to select the significant terms in the models. A numerical example is provided to illustrate the effectiveness of the new algorithms together with the application to real EEG data collected from 4 patients.

Identification of nonlinear time-varying systems using an online sliding-window and common model structure selection CMSS approach with applications to EEG

The identification of nonlinear time-varying systems using linear-in-the-parameter models is investigated. An efficient common model structure selection (CMSS) algorithm is proposed to select a common model structure, with application to EEG data modelling. The time-varying parameters for the identified common-structured model are then estimated using a sliding-window recursive least squares (SWRLS) approach. The new method can effectively detect and adaptively track and rapidly capture the transient variation of nonstationary signals, and can also produce robust models with better generalisation properties. Two examples are presented to demonstrate the effectiveness and applicability of the new approach including an application to EEG data.

Myoclonus ataxia and refractory coeliac disease.

BackgroundCortical myoclonus with ataxia has only rarely been reported in association with Coeliac Disease (CD). Such reports also suggested that it is unresponsive to gluten-free diet. We present detailed electro-clinical characteristics of a new syndrome of progressive cortical hyperexcitability with ataxia and refractory CD. At our gluten/neurology clinic we have assessed and regularly follow up over 600 patients with neurological manifestations due to gluten sensitivity. We have identified 9 patients with this syndrome.ResultsAll 9 patients (6 male, 3 female) experienced asymmetrical irregular myoclonus involving one or more limbs and sometimes face. This was often stimulus sensitive and became more widespread over time. Three patients had a history of Jacksonian march and five had at least one secondarily generalised seizure. Electrophysiology showed evidence of cortical myoclonus. Three had a phenotype of epilepsia partialis continua at onset. There was clinical, imaging and/or pathological evidence of cerebellar involvement in all cases. All patients adhered to a strict gluten-free diet with elimination of gluten-related antibodies in most. However, there was still evidence of enteropathy in all, suggestive of refractory celiac disease. Two died from enteropathy-associated lymphoma and one from status epilepticus. Five patients were treated with mycophenolate and one in addition with rituximab and IV immunoglobulins. Their ataxia and enteropathy improved but myoclonus remained the most disabling feature of their illness.ConclusionsThis syndrome may well be the commonest neurological manifestation of refractory CD. The clinical involvement, apart from ataxia, covers the whole clinical spectrum of cortical myoclonus.

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.

A Parametric Method to Measure Time-Varying Linear and Nonlinear Causality With Applications to EEG Data

A linear and nonlinear causality detection method called the error-reduction-ratio causality (ERRC) test is introduced in this paper to investigate if linear or nonlinear models should be considered in the study of human electroencephalograph (EEG) data. In comparison to the traditional Granger methods, one significant advantage of the ERRC approach is that it can effectively detect the time-varying linear and nonlinear causalities between two signals without fitting a complete nonlinear model. Two numerical simulation examples are employed to compare the performance of the new method with other widely used methods in the presence of noise and in tracking time-varying causality. Finally, an application to measure the linear and nonlinear relationships between two EEG signals from different cortical sites for patients with childhood absence epilepsy is discussed.

A nonlinear causality measure in the frequency domain: nonlinear partial directed coherence with applications to EEG.

BACKGROUND Frequency domain Granger causality measures have been proposed and widely applied in analyzing rhythmic neurophysiological and biomedical signals. Almost all these measures are based on linear time domain regression models, and therefore can only detect linear causal effects in the frequency domain. NEW METHOD A frequency domain causality measure, the partial directed coherence, is explicitly linked with the frequency response function concept of linear systems. By modeling the nonlinear relationships between time series using nonlinear models and employing corresponding frequency-domain analysis techniques (i.e., generalized frequency response functions), a new nonlinear partial directed coherence method is derived. RESULTS The advantages of the new method are illustrated via a numerical example of a nonlinear physical system and an application to electroencephalogram signals from a patient with childhood absence epilepsy. COMPARISON WITH EXISTING METHODS The new method detects both linear and nonlinear casual effects between bivariate signals in the frequency domain, while the existing measures can only detect linear effects. CONCLUSIONS The proposed new method has important advantages over the classical linear measures, because detecting nonlinear dependencies has become more and more important in characterizing functional couplings in neuronal and biological systems.

Spectral Analysis for Nonstationary and Nonlinear Systems: A Discrete-Time-Model-Based Approach

A new frequency-domain analysis framework for nonlinear time-varying systems is introduced based on parametric time-varying nonlinear autoregressive with exogenous input models. It is shown how the time-varying effects can be mapped to the generalized frequency response functions (FRFs) to track nonlinear features in frequency, such as intermodulation and energy transfer effects. A new mapping to the nonlinear output FRF is also introduced. A simulated example and the application to intracranial electroencephalogram data are used to illustrate the theoretical results.