In patients with drug-resistant focal epilepsy requiring surgery, hippocampal sclerosis was the most common histopathological diagnosis among adults, and focal cortical dysplasia was the most common diagnosis among children. Tumors were the second most common lesion in both groups. (Funded by the European Union and others.).

A pilot prospective follow-up study of the role of the branched chain amino acids as additional therapy to the ketogenic diet was carried out in 17 children, aged between 2 and 7 years, with refractory epilepsy. All of these patients were on the ketogenic diet; none of them was seizure free, while only 13 had more or less benefited from the diet. The addition of branched chain amino acids induced a 100% seizure reduction in 3 patients, while a 50% to 90% reduction was noticed in 5. Moreover, in all of the patients, no reduction in ketosis was recorded despite the change in the fat-to-protein ratio from 4:1 to 2.5:1. Although our data are preliminary, we suggest that branched chain amino acids may increase the effectiveness of the ketogenic diet and the diet could be more easily tolerated by the patients because of the change in the ratio of fat to protein.

No abstract

Hypothalamic hamartomas (HH) are typically associated with gelastic seizures but also implicated in the genesis of other seizure types. In order to identify networks involved in seizure generation, we performed EEG-fMRI in two adult patients with HH, the first with predominantly gelastic seizures and the second with complex partial and no typical gelastic seizures. The ictal and interictal analysis of the patient with gelastic seizures revealed the involvement of the HH, the cingulate gyrus, the precuneus and the prefrontal cortex. The interictal analysis of the patient with complex partial seizures, showed changes in blood oxygen-level dependent signal over the temporal lobes, the base of the frontal lobe, the cingulate, the precuneus and the prefrontal cortex, but not the HH. The differences in the neural networks implicated may account for differences in clinical manifestation of seizures owing to HH.

Epileptiform discharges in interictal electroencephalography (EEG) form the mainstay of epilepsy diagnosis and localization of seizure onset. Visual analysis is rater-dependent and time consuming, especially for long-term recordings, while computerized methods can provide efficiency in reviewing long EEG recordings. This paper presents a machine learning approach for automated detection of epileptiform discharges (spikes). The proposed method first detects spike patterns by calculating similarity to a coarse shape model of a spike waveform and then refines the results by identifying subtle differences between actual spikes and false detections. Pattern classification is performed using support vector machines in a low dimensional space on which the original waveforms are embedded by locality preserving projections. The automatic detection results are compared to experts’ manual annotations (101 spikes) on a whole-night sleep EEG recording. The high sensitivity (97 %) and the low false positive rate (0.1 min−1), calculated by intra-patient cross-validation, highlight the potential of the method for automated interictal EEG assessment.