Levetiracetam improves symptoms of multiple chemical sensitivity : Case report

Multiple chemical sensitivity (MCS) is a disorder of unknown etiology with no effective treatment. Many clinicians accept that a diagnosis of somatic symptoms disorder (SSD) is an appropriate diagnostic category for MCS. We found that administration of levetiracetam improved recurrent symptoms of MCS in a patient. A 23-year-old female presented with recurrent multiple symptoms of musculoskeletal, airway or mucous membrane, heart/chest-related, gastrointestinal, cognitive, affective, neuromuscular, head-related, and skinrelated induced by exposure to diesel or gas engine exhaust, tobacco smoke, insecticide, gasoline, paint or paint thinner, cleaning products, fragrances, tar or asphalt, nail polish or hairspray, and new furnishings. Gastrointestinal, cognitive, and skin-related symptoms were precipitated by some food additives. She suffered partial seizures from the age of 17 years, and was diagnosed with right parietal lobe epilepsy. Administration of levetiracetam (250 mg/day) eliminated her MCS symptoms. Levetiracetam reduces the release of presynaptic neurotransmitter including glutamate by binding to presynaptic vesicle protein. A recent study established the presence of glutamatergic overactivation in somatization disorder, a form of SSD. Our case may indicate that a subset of patients with SSD have glutamatergic overactivation, which levetiracetam can normalize

Parental satisfaction and seizure outcome after corpus callosotomy in patients with infantile or early childhood onset epilepsy

AbstractPurposeTo elucidate the benefit of corpus callosotmy in terms of parental satisfaction and seizure outcome.MethodThis study included 16 consecutive patients with infantile or early childhood onset epilepsy who underwent total corpus callosotomy for alleviation of seizures. Questionnaires were sent anonymously to the parents asking about relative changes in seizures and about parental satisfaction for the post-operative outcome.ResultsThe improvements in frequency, intensity, and duration of seizures were correlated with the level of satisfaction (Spearman's rank-order correlation coefficient, ρ=0.87, 0.93, and 0.75, respectively). The highest level of satisfaction was only seen in patients who achieved freedom from all seizures or drop attacks.ConclusionComplete seizure freedom and freedom from drop attacks are important goals of corpus callosotomy for parental satisfaction. These factors should be considered in assessing post-operative outcome after corpus callosotomy

Ongoing EEG artifact correction using blind source separation

Objective: Analysis of the electroencephalogram (EEG) for epileptic spike and seizure detection or brain-computer interfaces can be severely hampered by the presence of artifacts. The aim of this study is to describe and evaluate a fast automatic algorithm for ongoing correction of artifacts in continuous EEG recordings, which can be applied offline and online. Methods: The automatic algorithm for ongoing correction of artifacts is based on fast blind source separation. It uses a sliding window technique with overlapping epochs and features in the spatial, temporal and frequency domain to detect and correct ocular, cardiac, muscle and powerline artifacts. Results: The approach was validated in an independent evaluation study on publicly available continuous EEG data with 2035 marked artifacts. Validation confirmed that 88% of the artifacts could be removed successfully (ocular: 81%, cardiac: 84%, muscle: 98%, powerline: 100%). It outperformed state-of-the-art algorithms both in terms of artifact reduction rates and computation time. Conclusions: Fast ongoing artifact correction successfully removed a good proportion of artifacts, while preserving most of the EEG signals. Significance: The presented algorithm may be useful for ongoing correction of artifacts, e.g., in online systems for epileptic spike and seizure detection or brain-computer interfaces.Comment: 16 pages, 4 figures, 3 table

Development of an epileptic seizure prediction algorithm using R–R intervals with self-attentive autoencoder

Epilepsy is a neurological disorder that may affect the autonomic nervous system (ANS) from 15 to 20 min before seizure onset, and disturbances of ANS affect R–R intervals (RRI) on an electrocardiogram (ECG). This study aims to develop a machine learning algorithm for predicting focal epileptic seizures by monitoring R–R interval (RRI) data in real time. The developed algorithm adopts a self-attentive autoencoder (SA-AE), which is a neural network for time-series data. The results of applying the developed seizure prediction algorithm to clinical data demonstrated that it functioned well in most patients; however, false positives (FPs) occurred in specific participants. In a future work, we will investigate the causes of FPs and optimize the developing seizure prediction algorithm to further improve performance using newly added clinical data

Magnetoencephalography localizing spike sources of atypical benign partial epilepsy

Rationale: Atypical benign partial epilepsy (ABPE) is characterized by centro-temporal electroencephalography (EEG) spikes, continuous spike and waves during sleep (CSWS), and multiple seizure types including epileptic negative myoclonus (ENM), but not tonic seizures. This study evaluated the localization of magnetoencephalography (MEG) spike sources (MEGSSs) to investigate the clinical features and mechanism underlying ABPE. Methods: We retrospectively analyzed seizure profiles, scalp video EEG (VEEG) and MEG in ABPE patients. Results: Eighteen ABPE patients were identified (nine girls and nine boys). Seizure onset ranged from 1.3 to 8.8 years (median, 2.9 years). Initial seizures consisted of focal motor seizures (15 patients) and absences/atypical absences (3). Seventeen patients had multiple seizure types including drop attacks (16), focal motor seizures (16), ENM (14), absences/atypical absences (11) and focal myoclonic seizures (10). VEEG showed centro-temporal spikes and CSWS in all patients. Magnetic resonance imaging (MRI) was reported as normal in all patients. MEGSSs were localized over the following regions: both Rolandic and sylvian (8), peri-sylvian (5), peri-Rolandic (4), parieto-occipital (1), bilateral (10) and unilateral (8). All patients were on more than two antiepileptic medications. ENM and absences/atypical absences were controlled in 14 patients treated with adjunctive ethosuximide. Conclusion: MEG localized the source of centro-temporal spikes and CSWS in the Rolandic-sylvian regions. Centro-temporal spikes, Rolandic-sylvian spike sources and focal motor seizures are evidence that ABPE presents with Rolandic-sylvian onset seizures. ABPE is therefore a unique, age-related and localization-related epilepsy with a Rolandic-sylvian epileptic focus plus possible thalamo-cortical epileptic networks in the developing brain of children