Detecting multiple sclerosis via breath analysis using an eNose, a pilot study.

In the present study we investigated whether multiple sclerosis (MS) can be detected via exhaled breath analysis using an electronic nose (eNose). The AeonoseTM(an eNose, The eNose Company, Zutphen, the Netherlands) is a diagnostic test device to detect patterns of volatile organic compounds in exhaled breath. We evaluated whether the AeonoseTMcan make a distinction between the breath patterns of patients with MS and healthy control subjects. In this mono-center, prospective, non-invasive study, 124 subjects with a confirmed diagnosis of MS and 129 control subjects each breathed into the AeonoseTMfor 5 min. Exhaled breath data was used to train an artificial neural network (ANN) predictive model. To investigate the influence of medication intake we created a second predictive model with a subgroup of MS patients without medication prescribed for MS. The ANN model based on the entire dataset was able to distinguish MS patients from healthy controls with a sensitivity of 0.75 (95% CI: 0.66-0.82) and specificity of 0.60 (0.51-0.69). The model created with the subgroup of MS patients not using medication and the healthy control subjects had a sensitivity of 0.93 (0.82-0.98) and a specificity of 0.74 (0.65-0.81). The study showed that the AeonoseTMis able to make a distinction between MS patients and healthy control subjects, and could potentially provide a quick screening test to assist in diagnosing MS. Further research is needed to determine whether the AeonoseTMis able to differentiate new MS patients from subjects who will not get the diagnosis.

Event-related mu-rhythm desynchronization during movement observation is impaired in Parkinson's disease.

OBJECTIVE: Patients with Parkinson's disease often experience difficulties in adapting movements and learning alternative movements to compensate for symptoms. Since observation of movement has been demonstrated to lead to the formation of a lasting specific motor memory that resembled that elicited by physical training we hypothesize that mu-rhythm desynchronization in response to movement observation is impaired in Parkinson's disease. METHOD: In a pilot study with nine patients with Parkinson's disease at a Hoehn and Yahr stage of I or II and eleven age-matched controls, we tested this hypothesis by comparing the event related desynchronization (ERD) patterns from the EEG recorded during the observation of hand action and baseline videos. RESULTS: Healthy subjects showed normal bilateral ERD of the mu-rhythm. In patients with Parkinson's disease this distinct ERD pattern was lacking. CONCLUSION: The results of this study suggest that event-related mu-rhythm desynchronization is impaired in Parkinson's disease, even at early stages of the disease. SIGNIFICANCE: Studying event-related mu-rhythm desynchronization dysfunction in Parkinson's disease patients may enhance our understanding of symptoms as impaired motor learning.

Suppressors of interictal discharges in idiopathic childhood occipital epilepsy of Gastaut.

Idiopathic childhood occipital epilepsy of Gastaut (ICOE-G) is a rare form of epilepsy, characterized by visual hallucinations, periods of blindness, motor seizures, and migraine-like symptoms. A characteristic EEG feature is fixation-off sensitivity: epileptiform discharges are suppressed by visual input. Here, we present an 11-year-old girl suffering from ICOE-G, who was studied to identify potential additional suppressors of the epileptiform discharges.

Predicting success of vagus nerve stimulation (VNS) from interictal EEG.

PURPOSE: Vagus nerve stimulation (VNS) has shown to be an effective treatment for drug resistant epilepsy in numerous patients, however, not in all. It is still not possible to predict which patients will profit from VNS. In this pilot study, we explore predictive interictal EEG features for seizure reduction after VNS. METHODS: 19 Patients with medically refractory epilepsy and an implanted VNS system were included. Interictal EEG registrations, recorded before implantation, were retrospectively analysed. A quantative symmetry measure, the pair wise derived brain symmetry index (pdBSI), was tested to predict VNS outcome. Reduction in seizure frequency was used to define the responders. RESULTS: 10 Patients did respond to VNS, of whom 7 patients had a seizure reduction of at least 50% in a follow-up period of 2 years. On average, we find higher pdBSI values for delta, theta, alpha and beta bands for non-responders than for responders. The average pdBSI of the theta and alpha bands could significantly discriminate between responders and non-responders. CONCLUSION: In this study, quantifying EEG symmetry using the pdBSI shows promising results in predicting the reduction of seizure frequency after VNS treatment.