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Background: Cerebellar ataxias are the result of diverse disease processes that can be genetic or acquired. Establishing a diagnosis requires a methodical approach with expert clinical evaluation and investigations. We describe the causes of ataxia in 1500 patients with cerebellar ataxia.Methods: All patients were referred to the Sheffield Ataxia Centre, UK, and underwent extensive investigations, including, where appropriate genetic testing using next-generation sequencing (NGS). Patients were followed up on a 6-monthly basis for reassessment and further investigations if indicated.Results: A total of 1500 patients were assessed over 20 years. Twenty per cent had a family history, the remaining having sporadic ataxia. The commonest cause of sporadic ataxia was gluten ataxia (25%). A genetic cause was identified in 156 (13%) of sporadic cases with other causes being alcohol excess (12%) and cerebellar variant of multiple system atrophy (11%). Using NGS, positive results were obtained in 32% of 146 patients tested. The commonest ataxia identified was EA2. A genetic diagnosis was achieved in 57% of all familial ataxias. The commonest genetic ataxias were Friedreich's ataxia (22%), SCA6 (14%), EA2 (13%), SPG7 (10%) and mitochondrial disease (10%). The diagnostic yield following attendance at the Sheffield Ataxia Centre was 63%.Conclusions: Immune-mediated ataxias are common. Advances in genetic testing have significantly improved the diagnostic yield of patients suspected of having a genetic ataxia. Making a diagnosis of the cause of ataxia is essential due to potential therapeutic interventions for immune and some genetic ataxias. [ABSTRACT FROM AUTHOR]

• Recognise epileptic patients using interictal seizure-free EEG data. • Achieved 97 % classification accuracy for EG vs HC using functional connectivity. • Achieved 73 % classification accuracy for EG vs NEAD using functional connectivity. • The proposed multivariant method outperforms the univariant method. Most seizures in adults with epilepsy occur rather infrequently and as a result, the interictal EEG plays a crucial role in the diagnosis and classification of epilepsy. However, empirical interpretation, of a first EEG in adult patients, has a very low sensitivity ranging between 29–55 %. Useful EEG information remains buried within the signals in seizure-free EEG epochs, far beyond the observational capabilities of any specialised physician in this field. Unlike most of the existing works focusing on either seizure data or single-variate method, we introduce a multi-variate method to characterise sensor level brain functional connectivity from interictal EEG data to identify patients with generalised epilepsy. A total of 9 connectivity features based on 5 different measures in time, frequency and time-frequency domains have been tested. The solution has been validated by the K-Nearest Neighbour algorithm, classifying an epilepsy group (EG) vs healthy controls (HC) and subsequently with another cohort of patients characterised by non-epileptic attacks (NEAD), a psychogenic type of disorder. A high classification accuracy (97 %) was achieved for EG vs HC while revealing significant spatio-temporal deficits in the frontocentral areas in the beta frequency band. For EG vs NEAD, the classification accuracy was only about 73 %, which might be a reflection of the well-described coexistence of NEAD with epileptic attacks. Our work demonstrates that seizure-free interictal EEG data can be used to accurately classify patients with generalised epilepsy from HC and that more systematic work is required in this direction aiming to produce a clinically useful diagnostic method. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. [ABSTRACT FROM AUTHOR]

There is increasing evidence to suggest that essential tremor has a central origin. Different structures appear to be part of the central tremorogenic network, including the motor cortex, the thalamus and the cerebellum. Some studies using electroencephalogram (EEG) and magnetoencephalography (MEG) show linear association in the tremor frequency between the motor cortex and the contralateral tremor electromyography (EMG). Additionally, high thalamomuscular coherence is found with the use of thalamic local field potential (LFP) recordings and tremulous EMG in patients undergoing surgery for deep brain stimulation (DBS). Despite a well-established reciprocal anatomical connection between the thalamus and cortex, the functional association between the two structures during "tremor-on" periods remains elusive. Thalamic (Vim) LFPs, ipsilateral scalp EEG from the sensorimotor cortex and contralateral tremor arm EMG recordings were obtained from two patients with essential tremor who had undergone successful surgery for DBS. Coherence analysis shows a strong linear association between thalamic LFPs and contralateral tremor EMG, but the relationship between the EEG and the thalamus is much less clear. These measurements were then analyzed by constructing a novel parametric nonlinear autoregressive with exogenous input (NARX) model. This new approach uncovered two distinct and not overlapping frequency "channels" of communication between Vim thalamus and the ipsilateral motor cortex, defining robustly "tremor-on" versus "tremor-off" states. The associated estimated nonlinear time lags also showed non-overlapping values between the two states, with longer corticothalamic lags (exceeding 50ms) in the tremor active state, suggesting involvement of an indirect multisynaptic loop. The results reveal the importance of the nonlinear interactions between cortical and subcortical areas in the central motor network of essential tremor. This work is important because it demonstrates for the first time that in essential tremor the functional interrelationships between the cortex and thalamus should not be sought exclusively within individual frequencies but more importantly between cross-frequency nonlinear interactions. Should our results be successfully reproduced on a bigger cohort of patients with essential tremor, our approach could be used to create an on-demand closed-loop DBS device, able to automatically activate when the tremor is on., (Copyright © 2016 IBRO. Published by Elsevier Ltd. All rights reserved.)