Human narcolepsy with cataplexy is a neurological disorder, which develops due to a deficiency in hypocretin producing neurons in the hypothalamus. There is a strong association with human leucocyte antigens HLA-DR2 and HLA-DQB1*0602. The disease typically starts in adolescence. Recent developments in narcolepsy research support the hypothesis of narcolepsy being an immune-mediated disease. Narcolepsy is associated with polymorphisms of the genes encoding T cell receptor alpha chain, tumour necrosis factor alpha and tumour necrosis factor receptor II. Moreover the rate of streptococcal infection is increased at onset of narcolepsy. The hallmarks of anti-self reactions in the tissue--namely upregulation of major histocompatibility antigens and lymphocyte infiltrates--are missing in the hypothalamus. These findings are questionable because they were obtained by analyses performed many years after onset of disease. In some patients with narcolepsy autoantibodies to Tribbles homolog 2, which is expressed by hypocretin neurons, have been detected recently. Immune-mediated destruction of hypocretin producing neurons may be mediated by microglia/macrophages that become activated either by autoantigen specific CD4(+) T cells or superantigen stimulated CD8(+) T cells, or independent of T cells by activation of DQB1*0602 signalling. Activation of microglia and macrophages may lead to the release of neurotoxic molecules such as quinolinic acid, which has been shown to cause selective destruction of hypocretin neurons in the hypothalamus.
Epileptic seizures are due to the pathological collective activity of large cellular assemblies. A better understanding of this collective activity is integral to the development of novel diagnostic and therapeutic procedures. In contrast to reductionist analyses, which focus solely on small-scale characteristics of ictogenesis, here we follow a systems-level approach, which combines both small-scale and larger-scale analyses. Peri-ictal dynamics of epileptic networks are assessed by studying correlation within and between different spatial scales of intracranial electroencephalographic recordings (iEEG) of a heterogeneous group of patients suffering from pharmaco-resistant epilepsy. Epileptiform activity as recorded by a single iEEG electrode is determined objectively by the signal derivative and then subjected to a multivariate analysis of correlation between all iEEG channels. We find that during seizure, synchrony increases on the smallest and largest spatial scales probed by iEEG. In addition, a dynamic reorganization of spatial correlation is observed on intermediate scales, which persists after seizure termination. It is proposed that this reorganization may indicate a balancing mechanism that decreases high local correlation. Our findings are consistent with the hypothesis that during epileptic seizures hypercorrelated and therefore functionally segregated brain areas are re-integrated into more collective brain dynamics. In addition, except for a special sub-group, a highly significant association is found between the location of ictal iEEG activity and the location of areas of relative decrease of localised EEG correlation. The latter could serve as a clinically important quantitative marker of the seizure onset zone (SOZ).
Quantitative EEG (qEEG) has modified our understanding of epileptic seizures, shifting our view from the traditionally accepted hyper-synchrony paradigm toward more complex models based on re-organization of functional networks. However, qEEG measurements are so far rarely considered during the clinical decision-making process. To better understand the dynamics of intracranial EEG signals, we examine a functional network derived from the quantification of information flow between intracranial EEG signals. Using transfer entropy, we analyzed 198 seizures from 27 patients undergoing pre-surgical evaluation for pharmaco-resistant epilepsy. During each seizure we considered for each network the in-, out- and total "hubs", defined respectively as the time and the EEG channels with the maximal incoming, outgoing or total (bidirectional) information flow. In the majority of cases we found that the hubs occur around the middle of seizures, and interestingly not at the beginning or end, where the most dramatic EEG signal changes are found by visual inspection. For the patients who then underwent surgery, good postoperative clinical outcome was on average associated with a higher percentage of out- or total-hubs located in the resected area (for out-hubs p = 0.01, for total-hubs p = 0.04). The location of in-hubs showed no clear predictive value. We conclude that the study of functional networks based on qEEG measurements may help to identify brain areas that are critical for seizure generation and are thus potential targets for focused therapeutic interventions.
SUMMARYPurpose: Epileptic seizures typically reveal a high degree of stereotypy, that is, for an individual patient they are characterized by an ordered and predictable sequence of symptoms and signs with typically little variability. Stereotypy implies that ictal neuronal dynamics might have deterministic characteristics, presumably most pronounced in the ictogenic parts of the brain, which may provide diagnostically and therapeutically important information. Therefore the goal of our study was to search for indications of determinism in periictal intracranial electroencephalography (EEG) studies recorded from patients with pharmacoresistent epilepsy. Methods: We assessed the number of forbidden ordinal patterns of 110 periictal multichannel intracranial EEG studies of 16 patients. Ordinal patterns are derived from the rank order of short sequences of consecutive EEG values. Ordinal patterns are well suited for analyzing realworld time series, for they have low sensitivity for many forms of noise and are applicable to nonstationary data. Although Gaussian random dynamics generate all possible ordinal patterns for a given sequence length, deterministic dynamics typically manifest with less random and more regular signals that miss a certain number of all the possible ordinal patterns. These missing ordinal patterns are referred to as ''forbidden ordinal patterns.'' In this study, the number of forbidden ordinal patterns n fp of an EEG signal was interpreted as an indication of determinism, when it was larger than the number of forbidden patterns occurring in amplitude adjusted Fourier transform surrogates. We computed n fp for each EEG signal in a time-resolved way by using a moving-window approach. Then we specifically investigated n mean fp denoting the average number of forbidden patterns across all EEG signals, and n max fp , which represents the number of forbidden patterns occurring in the EEG signal with the largest n fp during the seizure-onset period. Key Findings: The average number of forbidden patterns of all EEG signals, n mean fp , typically first increased and then decreased during the seizures. However, these changes were not statistically significant relative to the preseizure time period. In contrast, n max fp typically increased significantly during the first third of the seizure period and then gradually decreased toward and beyond seizure termination. In those patients who became seizure free following surgery, a larger percentage of the EEG signals containing the maximal number of forbidden patterns during the seizure-onset period tended to be recorded from within the visually identified seizure-onset zones. Significance: Our findings demonstrate a spatiotemporally limited shift of neuronal dynamics toward a more deterministic dynamic regimen, specifically pronounced during the seizure-onset period. Assessing the number of forbidden ordinal patterns of intracranial EEG provides quantitative and observer-independent information. We propose that it is at least partially complementary to class...
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