Diagnosis and management of epilepsy in adults

According to the International League Against Epilepsy, epilepsy can be diagnosed if any of the following criteria are met: at least two unprovoked seizures occurring on separate days (seizures within 24 hours count as one event); one unprovoked seizure with at least a 60% risk of recurrence over the next ten years on the basis of associated clinical factors (such as a recent stroke or brain tumour); diagnosis of a specific epilepsy syndrome. Convulsive events should be described rather than given a label. The EEG can establish the diagnosis of epilepsy and distinguish between focal and primary generalised epilepsy. An MRI brain scan is usually mandatory

ON THE DEVELOPMENT OF A DATASET PUBLICATION GUIDELINE: DATA REPOSITORIES AND KEYWORD ANALYSIS IN ISPRS DOMAIN

The FAIR principle (find, access, interoperability, reuse) forms a sustainable resource for scientific exchange between researchers. Currently, the implementation of this principle is an important process for future research projects. To support this process in the ISPRS community, the usage of data repositories for dataset publication has the potential to bring closer the achievement of the FAIR principle. Therefore, we (1) analysed available data repositories, (2) identified common keywords in ISPRS publications and (3) developed a tool for searching appropriate repositories. Thus, infrastructures from the field of geosciences, that can already be used, become more accessible

CURRENT STATUS OF THE BENCHMARK DATABASE BEMEDA

Open science is an important attribute for developing new approaches. Especially, the data component plays a significant role. The FAIR principle provides a good orientation towards open data. One part of FAIR is findability. Thus, domain specific dataset search platforms were developed: the Earth Observation Database and our Benchmark Metadata Database (BeMeDa). In addition to the search itself, the datasets found by this platforms can be compared with each other with regard to their interoperability. We compare these two platforms and present an update of our platform BeMeDa. This update includes additional location information about the datasets and a new frontend design with improved usability. We rely on user feedback for further improvements and enhancements

Can N-Methyl-D-Aspartate Receptor Hypofunction in Schizophrenia Be Localized to an Individual Cell Type?

Hypofunction of N-methyl-D-aspartate glutamate receptors (NMDARs), whether caused by endogenous factors like auto-antibodies or mutations, or by pharmacological or genetic manipulations, produces a wide variety of deficits which overlap with—but do not precisely match—the symptom spectrum of schizophrenia. In order to understand how NMDAR hypofunction leads to different components of the syndrome, it is necessary to take into account which neuronal subtypes are particularly affected by it in terms of detrimental functional alterations. We provide a comprehensive overview detailing findings in rodent models with cell type–specific knockout of NMDARs. Regarding inhibitory cortical cells, an emerging model suggests that NMDAR hypofunction in parvalbumin (PV) positive interneurons is a potential risk factor for this disease. PV interneurons display a selective vulnerability resulting from a combination of genetic, cellular, and environmental factors that produce pathological multi-level positive feedback loops. Central to this are two antioxidant mechanisms—NMDAR activity and perineuronal nets—which are themselves impaired by oxidative stress, amplifying disinhibition. However, NMDAR hypofunction in excitatory pyramidal cells also produces a range of schizophrenia-related deficits, in particular maladaptive learning and memory recall. Furthermore, NMDAR blockade in the thalamus disturbs thalamocortical communication, and NMDAR ablation in dopaminergic neurons may provoke over-generalization in associative learning, which could relate to the positive symptom domain. Therefore, NMDAR hypofunction can produce schizophrenia-related effects through an action on various different circuits and cell types

Learning to live with Dale's principle: ANNs with separate excitatory and inhibitory units

The units in artificial neural networks (ANNs) can be thought of as abstractions of biological neurons, and ANNs are increasingly used in neuroscience research. However, there are many important differences between ANN units and real neurons. One of the most notable is the absence of Dale's principle, which ensures that biological neurons are either exclusively excitatory or inhibitory. Dale's principle is typically left out of ANNs because its inclusion impairs learning. This is problematic, because one of the great advantages of ANNs for neuroscience research is their ability to learn complicated, realistic tasks. Here, by taking inspiration from feedforward inhibitory interneurons in the brain we show that we can develop ANNs with separate populations of excitatory and inhibitory units that learn just as well as standard ANNs. We call these networks Dale's ANNs (DANNs). We present two insights that enable DANNs to learn well: (1) DANNs are related to normalization schemes, and can be initialized such that the inhibition centres and standardizes the excitatory activity, (2) updates to inhibitory neuron parameters should be scaled using corrections based on the Fisher Information matrix. These results demonstrate how ANNs that respect Dale's principle can be built without sacrificing learning performance, which is important for future work using ANNs as models of the brain. The results may also have interesting implications for how inhibitory plasticity in the real brain operates

Macrostate Data Clustering

We develop an effective nonhierarchical data clustering method using an analogy to the dynamic coarse graining of a stochastic system. Analyzing the eigensystem of an interitem transition matrix identifies fuzzy clusters corresponding to the metastable macroscopic states (macrostates) of a diffusive system. A "minimum uncertainty criterion" determines the linear transformation from eigenvectors to cluster-defining window functions. Eigenspectrum gap and cluster certainty conditions identify the proper number of clusters. The physically motivated fuzzy representation and associated uncertainty analysis distinguishes macrostate clustering from spectral partitioning methods. Macrostate data clustering solves a variety of test cases that challenge other methods.Comment: keywords: cluster analysis, clustering, pattern recognition, spectral graph theory, dynamic eigenvectors, machine learning, macrostates, classificatio

Myasthenia and related disorders of the neuromuscular junction

Our understanding of transmission at the neuromuscular junction has increased greatly in recent years. We now recognise a wide variety of autoimmune and genetic diseases that affect this specialised synapse, causing muscle weakness and fatigue. These disorders greatly affect quality of life and rarely can be fatal. Myasthenia gravis is the most common disorder and is most commonly caused by autoantibodies targeting postsynaptic acetylcholine receptors. Antibodies to muscle-specific kinase (MuSK) are detected in a variable proportion of the remainder. Treatment is symptomatic and immunomodulatory. Lambert-Eaton myasthenic syndrome is caused by antibodies to presynaptic calcium channels, and approximately 50% of cases are paraneoplastic, most often related to small cell carcinoma of the lung. Botulism is an acquired disorder caused by neurotoxins produced by Clostridium botulinum, impairing acetylcholine release into the synaptic cleft. In addition, several rare congenital myasthenic syndromes have been identified, caused by inherited defects in presynaptic, synaptic basal lamina and postsynaptic proteins necessary for neuromuscular transmission. This review focuses on recent advances in the diagnosis and treatment of these disorders

Data clustering and noise undressing for correlation matrices

We discuss a new approach to data clustering. We find that maximum likelihood leads naturally to an Hamiltonian of Potts variables which depends on the correlation matrix and whose low temperature behavior describes the correlation structure of the data. For random, uncorrelated data sets no correlation structure emerges. On the other hand for data sets with a built-in cluster structure, the method is able to detect and recover efficiently that structure. Finally we apply the method to financial time series, where the low temperature behavior reveals a non trivial clustering.Comment: 8 pages, 5 figures, completely rewritten and enlarged version of cond-mat/0003241. Submitted to Phys. Rev.

Consumers don't play dice, influence of social networks and advertisements

Empirical data of supermarket sales show stylised facts that are similar to stock markets, with a broad (truncated) Levy distribution of weekly sales differences in the baseline sales [R.D. Groot, Physica A 353 (2005) 501]. To investigate the cause of this, the influence of social interactions and advertisements are studied in an agent-based model of consumers in a social network. The influence of network topology was varied by using a small-world network, a random network and a Barabasi-Albert network. The degree to which consumers value the opinion of their peers was also varied. On a small-world and random network we find a phase-transition between an open market and a locked-in market that is similar to condensation in liquids. At the critical point, fluctuations become large and buying behaviour is strongly correlated. However, on the small world network the noise distribution at the critical point is Gaussian, and critical slowing down occurs which is not observed in supermarket sales. On a scale-free network, the model shows a transition between a gas-like phase and a glassy state, but at the transition point the noise amplitude is much larger than what is seen in supermarket sales. To explore the role of advertisements, a model is studied where imprints are placed on the minds of consumers that ripen when a decision for a product is made. The correct distribution of weekly sales returns follows naturally from this model, as well as the noise amplitude, the correlation time and cross-correlation of sales fluctuations. For particular parameter values, simulated sales correlation shows power law decay in time. The model predicts that social interaction helps to prevent aversion, and that products are viewed more positively when their consumption rate is higher.Comment: Accepted for publication in Physica

Novel therapies for epilepsy in the pipeline

Despite the availability of many antiepileptic drugs (AEDs) (old and newly developed) and, as recently suggested, their optimization in the treatment of patients with uncontrolled seizures, more than 30% of patients with epilepsy continue to experience seizures and have drug-resistant epilepsy; the management of these patients represents a real challenge for epileptologists and researchers. Resective surgery with the best rates of seizure control is not an option for all of them; therefore, research and discovery of new methods of treating resistant epilepsy are of extreme importance. In this article, we will discuss some innovative approaches, such as P-glycoprotein (P-gp) inhibitors, gene therapy, stem cell therapy, traditional and novel antiepileptic devices, precision medicine, as well as therapeutic advances in epileptic encephalopathy in children; these treatment modalities open up new horizons for the treatment of patients with drug-resistant epilepsy