Termination of seizure clusters is related to the duration of focal seizures.

OBJECTIVE: Clustered seizures are characterized by shorter than usual interseizure intervals and pose increased morbidity risk. This study examines the characteristics of seizures that cluster, with special attention to the final seizure in a cluster. METHODS: This is a retrospective analysis of long-term inpatient monitoring data from the EPILEPSIAE project. Patients underwent presurgical evaluation from 2002 to 2009. Seizure clusters were defined by the occurrence of at least two consecutive seizures with interseizure intervals of <4 h. Other definitions of seizure clustering were examined in a sensitivity analysis. Seizures were classified into three contextually defined groups: isolated seizures (not meeting clustering criteria), terminal seizure (last seizure in a cluster), and intracluster seizures (any other seizures within a cluster). Seizure characteristics were compared among the three groups in terms of duration, type (focal seizures remaining restricted to one hemisphere vs. evolving bilaterally), seizure origin, and localization concordance among pairs of consecutive seizures. RESULTS: Among 92 subjects, 77 (83%) had at least one seizure cluster. The intracluster seizures were significantly shorter than the last seizure in a cluster (p = 0.011), whereas the last seizure in a cluster resembled the isolated seizures in terms of duration. Although focal only (unilateral), seizures were shorter than seizures that evolved bilaterally and there was no correlation between the seizure type and the seizure position in relation to a cluster (p = 0.762). Frontal and temporal lobe seizures were more likely to cluster compared with other localizations (p = 0.009). Seizure pairs that are part of a cluster were more likely to have a concordant origin than were isolated seizures. Results were similar for the 2 h definition of clustering, but not for the 8 h definition of clustering. SIGNIFICANCE: We demonstrated that intracluster seizures are short relative to isolated seizures and terminal seizures. Frontal and temporal lobe seizures are more likely to cluster.

Dacrystic seizures: demographic, semiologic, and etiologic insights from a multicenter study in long-term video-EEG monitoring units.

PURPOSE: To provide an estimate of the frequency of dacrystic seizures in video-electroencephalography (EEG) long-term monitoring units of tertiary referral epilepsy centers and to describe the clinical presentation of dacrystic seizures in relationship to the underlying etiology. METHODS: We screened clinical records and video-EEG reports for the diagnosis of dacrystic seizures of all patients admitted for video-EEG long-term monitoring at five epilepsy referral centers in the United States and Germany. Patients with a potential diagnosis of dacrystic seizures were identified, and their clinical charts and video-EEG recordings were reviewed. We included only patients with: (1) stereotyped lacrimation, sobbing, grimacing, yelling, or sad facial expression; (2) long-term video-EEG recordings (at least 12 h); and (3) at least one brain magnetic resonance imaging (MRI) study. KEY FINDINGS: Nine patients (four female) with dacrystic seizures were identified. Dacrystic seizures were identified in 0.06-0.53% of the patients admitted for long-term video-EEG monitoring depending on the specific center. Considering our study population as a whole, the frequency was 0.13%. The presence of dacrystic seizures without other accompanying clinical features was found in only one patient. Gelastic seizures accompanied dacrystic seizures in five cases, and a hypothalamic hamartoma was found in all of these five patients. The underlying etiology in the four patients with dacrystic seizures without gelastic seizures was left mesial temporal sclerosis (three patients) and a frontal glioblastoma (one patient). All patients had a difficult-to-control epilepsy as demonstrated by the following: (1) at least three different antiepileptic drugs were tried in each patient, (2) epilepsy was well controlled with antiepileptic drugs in only two patients, (3) six patients were considered for epilepsy surgery and three of them underwent a surgical/radiosurgical or radioablative procedure. Regarding outcome, antiepileptic drugs alone achieved seizure freedom in two patients and did not change seizure frequency in another patient. Radiosurgery led to moderately good seizure control in one patient and did not improve seizure control in another patient. Three patients were or are being considered for epilepsy surgery on last follow-up. One patient remains seizure free 3 years after epilepsy surgery. SIGNIFICANCE: Dacrystic seizures are a rare but clinically relevant finding during video-EEG monitoring. Our data show that when the patient has dacrystic and gelastic seizures, the cause is a hypothalamic hamartoma. In contrast, when dacrystic seizures are not accompanied by gelastic seizures the underlying lesion is most commonly located in the temporal cortex.

Are prodromes preictal events? A prospective PDA-based study.

Up to 29% of patients with epilepsy report "prodromal" sensations more than 30 minutes prior to seizures. We developed and implemented an objective methodology to prospectively assess the sensitivity and specificity of these subjective experiences using personal digital assistants (PDAs). The key property, in contrast to paper-based diaries, is the internal recording of the patient's entering time of prodromes and seizures. Of 500 patients with epilepsy interviewed, 31 claimed to sense prodromal symptoms at least 30 minutes before seizure onset. Eleven of them agreed to participate in a 4-week study to objectively measure their prospective prediction performance. In 9 patients returning data, the majority of prodrome entries were not followed by seizures or were identified only retrospectively. Statistical analysis revealed that no patient could outperform a nonspecific random predictor when predicting seizures based on the occurrence of prodromes, and that the group performance matched precisely the expected result for a by-chance prediction. These results question the predictive value of "prodromes" and the specificity of their occurrence in the preictal period.

Safety of hybrid electrodes for single-neuron recordings in humans.

BACKGROUND: Intracranial in vivo recordings of individual neurons in humans are increasingly performed for a better understanding of the mechanisms of epileptogenesis and of the neurobiological basis of cognition. So far, information about the safety of stereotactic implantations and of magnetic resonance imaging (MRI) with hybrid depth electrodes is scarce. OBJECTIVE: The aim of this study was to assess neurosurgical safety of implantations, recordings, and imaging using hybrid electrodes in humans. METHODS: Perioperative and long-term safety of implantation of a total of 88 hybrid depth electrodes with integrated microwires was assessed retrospectively in 25 consecutive epilepsy patients who underwent implantation of electrodes from 2007 to 2011 based on electronically stored charts. Safety aspects of MRI are reported from both in vitro and in vivo investigations. Precision of electrode implantation is evaluated based on intraoperative computed tomography and pre- and postoperative MRI. RESULTS: There was no clinically relevant morbidity associated with the use of hybrid electrodes in any of the patients. Precision of recordings from the targets aimed at was similar to that of standard depth electrodes. In vitro studies demonstrated the absence of relevant heating of hybrid electrodes with newly designed connectors with MRI at 1.5 T, corresponding to well-tolerated clinical MRI in patients. CONCLUSION: Given the technical approach described here, precise targeting and safe use are possible with hybrid electrodes containing microwires for in vivo recording of human neuronal units.

Mathematical modeling of biochemical systems with PottersWheel.

The program PottersWheel has been developed to provide an intuitive and yet powerful framework for data-based modeling of dynamical systems like biochemical reaction networks. Its key functionality is multi-experiment fitting, where several experimental data sets from different laboratory conditions are fitted simultaneously in order to improve the estimation of unknown model parameters, to check the validity of a given model, and to discriminate competing model hypotheses. New experiments can be designed interactively. Models are either created text-based or using a visual model designer. Dynamically generated and compiled C files provide fast simulation and fitting procedures. Each function can either be accessed using a graphical user interface or via command line, allowing for batch processing within custom Matlab scripts. PottersWheel is designed as a Matlab toolbox, comprises 250,000 lines of Matlab and C code, and is freely available for academic usage at www.potterswheel.de .

In silico labeling reveals the time-dependent label half-life and transit-time in dynamical systems.

BACKGROUND: Mathematical models of dynamical systems facilitate the computation of characteristic properties that are not accessible experimentally. In cell biology, two main properties of interest are (1) the time-period a protein is accessible to other molecules in a certain state - its half-life - and (2) the time it spends when passing through a subsystem - its transit-time. We discuss two approaches to quantify the half-life, present the novel method of in silico labeling, and introduce the label half-life and label transit-time. The developed method has been motivated by laboratory tracer experiments. To investigate the kinetic properties and behavior of a substance of interest, we computationally label this species in order to track it throughout its life cycle. The corresponding mathematical model is extended by an additional set of reactions for the labeled species, avoiding any double-counting within closed circuits, correcting for the influences of upstream fluxes, and taking into account combinatorial multiplicity for complexes or reactions with several reactants or products. A profile likelihood approach is used to estimate confidence intervals on the label half-life and transit-time. RESULTS: Application to the JAK-STAT signaling pathway in Epo-stimulated BaF3-EpoR cells enabled the calculation of the time-dependent label half-life and transit-time of STAT species. The results were robust against parameter uncertainties. CONCLUSIONS: Our approach renders possible the estimation of species and label half-lives and transit-times. It is applicable to large non-linear systems and an implementation is provided within the PottersWheel modeling framework (http://www.potterswheel.de).

How cortical neurons help us see: visual recognition in the human brain.

Through a series of complex transformations, the pixel-like input to the retina is converted into rich visual perceptions that constitute an integral part of visual recognition. Multiple visual problems arise due to damage or developmental abnormalities in the cortex of the brain. Here, we provide an overview of how visual information is processed along the ventral visual cortex in the human brain. We discuss how neurophysiological recordings in macaque monkeys and in humans can help us understand the computations performed by visual cortex.

Adaptive classification for brain computer interfaces.

In this paper we evaluate the performance of a new adaptive classifier for the use within a Brain Computer-Interface (BCI). The classifier can either be adaptive in a completely unsupervised manner or using unsupervised adaptation in conjunction with a neuronal evaluation signal to improve adaptation. The first variant, termed Adaptive Linear Discriminant Analysis (ALDA), updates mean values as well as covariances of the class distributions continuously in time. In simulated as well as experimental data ALDA substantially outperforms the non-adaptive LDA. The second variant, termed Adaptive Linear Discriminant Analysis with Error Correction (ALDEC), extends the unsupervised algorithm with an additional independent neuronal evaluation signal. Such a signal could be an error related potential which indicates when the decoder did not classify correctly. When the mean values of the class distributions circle around each other or even cross their way, ALDEC can yield a substantially better adaptation than ALDA depending on the reliability of the error signal. Given the non-stationarity of EEG signals during BCI control our approach might strongly improve the precision and the time needed to gain accurate control in future BCI applications.