Interacting Multiple Try Algorithms with Different Proposal Distributions

We propose a new class of interacting Markov chain Monte Carlo (MCMC) algorithms designed for increasing the efficiency of a modified multiple-try Metropolis (MTM) algorithm. The extension with respect to the existing MCMC literature is twofold. The sampler proposed extends the basic MTM algorithm by allowing different proposal distributions in the multiple-try generation step. We exploit the structure of the MTM algorithm with different proposal distributions to naturally introduce an interacting MTM mechanism (IMTM) that expands the class of population Monte Carlo methods. We show the validity of the algorithm and discuss the choice of the selection weights and of the different proposals. We provide numerical studies which show that the new algorithm can perform better than the basic MTM algorithm and that the interaction mechanism allows the IMTM to efficiently explore the state space

On the flexibility of the design of Multiple Try Metropolis schemes

The Multiple Try Metropolis (MTM) method is a generalization of the classical Metropolis-Hastings algorithm in which the next state of the chain is chosen among a set of samples, according to normalized weights. In the literature, several extensions have been proposed. In this work, we show and remark upon the flexibility of the design of MTM-type methods, fulfilling the detailed balance condition. We discuss several possibilities and show different numerical results

Testing blood and CSF in people with epilepsy: a practical guide.

Laboratory investigations, whilst not essential to the diagnosis of seizures or of epilepsy, can be fundamental to determining the cause and guiding management. Over 50% of first seizures have an acute symptomatic cause, including a range of metabolic, toxic or infectious cause. The same triggers can precipitate status epilepticus, either de novo or as part of a deterioration in control in individuals with established epilepsy. Some, such as hypoglycaemia or severe hyponatraemia, can be fatal without prompt identification and treatment. Failure to identify seizures associated with recreational drug or alcohol misuse can lead to inappropriate AED treatment, as well as a missed opportunity for more appropriate intervention. In individuals with established epilepsy on treatment, some laboratory monitoring is desirable at least occasionally, in particular, in relation to bone health, as well as in situations where changes in AED clearance or metabolism are likely (extremes of age, pregnancy, comorbid disorders of renal or hepatic function). For any clinician managing people with epilepsy, awareness of the commoner derangements associated with individual AEDs is essential to guide practice. In this article, we review indications for tests on blood, urine and/or cerebrospinal fluid in patients presenting with new-onset seizures and status epilepticus and in people with established epilepsy presenting acutely or as part of planned monitoring. Important, but rare, neurometabolic and genetic disorders associated with epilepsy are also mentioned

Sampling constrained probability distributions using Spherical Augmentation

Statistical models with constrained probability distributions are abundant in machine learning. Some examples include regression models with norm constraints (e.g., Lasso), probit, many copula models, and latent Dirichlet allocation (LDA). Bayesian inference involving probability distributions confined to constrained domains could be quite challenging for commonly used sampling algorithms. In this paper, we propose a novel augmentation technique that handles a wide range of constraints by mapping the constrained domain to a sphere in the augmented space. By moving freely on the surface of this sphere, sampling algorithms handle constraints implicitly and generate proposals that remain within boundaries when mapped back to the original space. Our proposed method, called {Spherical Augmentation}, provides a mathematically natural and computationally efficient framework for sampling from constrained probability distributions. We show the advantages of our method over state-of-the-art sampling algorithms, such as exact Hamiltonian Monte Carlo, using several examples including truncated Gaussian distributions, Bayesian Lasso, Bayesian bridge regression, reconstruction of quantized stationary Gaussian process, and LDA for topic modeling.Comment: 41 pages, 13 figure

New SMARCA2 mutation in a patient with Nicolaides–Baraitser syndrome and myoclonic astatic epilepsy

We report a de novo SMARCA2 missense mutation discovered on exome sequencing in a patient with myoclonic astatic epilepsy, leading to reassessment and identification of Nicolaides–Baraitser syndrome. This de novo SMARCA2 missense mutation c.3721C>G, p.Gln1241Glu is the only reported mutation on exon 26 outside the ATPase domain of SMARCA2 to be associated with Nicolaides–Baraitser syndrome and adds to chromatin remodeling as a pathway for epileptogenesis. © 2016 The Authors. American Journal of Medical Genetics Part A published by Wiley Periodicals, Inc

The training and organization of Paediatric Neurology in Europe: Special report of the European Paediatric Neurology Society & Committee of National Advisors

Background: Paediatric Neurology (PN) is a discipline focused on diagnosis, comprehensive management and research into dis

An Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration

While statisticians are well-accustomed to performing exploratory analysis in the modeling stage of an analysis, the notion of conducting preliminary general-purpose exploratory analysis in the Monte Carlo stage (or more generally, the model-fitting stage) of an analysis is an area which we feel deserves much further attention. Towards this aim, this paper proposes a general-purpose algorithm for automatic density exploration. The proposed exploration algorithm combines and expands upon components from various adaptive Markov chain Monte Carlo methods, with the Wang-Landau algorithm at its heart. Additionally, the algorithm is run on interacting parallel chains -- a feature which both decreases computational cost as well as stabilizes the algorithm, improving its ability to explore the density. Performance is studied in several applications. Through a Bayesian variable selection example, the authors demonstrate the convergence gains obtained with interacting chains. The ability of the algorithm's adaptive proposal to induce mode-jumping is illustrated through a trimodal density and a Bayesian mixture modeling application. Lastly, through a 2D Ising model, the authors demonstrate the ability of the algorithm to overcome the high correlations encountered in spatial models.Comment: 33 pages, 20 figures (the supplementary materials are included as appendices

Current practices in long-term video-EEG monitoring services: A survey among partners of the E-PILEPSY pilot network of reference for refractory epilepsy and epilepsy surgery.

The European Union-funded E-PILEPSY network aims to improve awareness of, and accessibility to, epilepsy surgery across Europe. In this study we assessed current clinical practices in epilepsy monitoring units (EMUs) in the participating centers. A 60-item web-based survey was distributed to 25 centers (27 EMUs) of the E-PILEPSY network across 22 European countries. The questionnaire was designed to evaluate the characteristics of EMUs, including organizational aspects, admission, and observation of patients, procedures performed, safety issues, cost, and reimbursement. Complete responses were received from all (100%) EMUs surveyed. Continuous observation of patients was performed in 22 (81%) EMUs during regular working hours, and in 17 EMUs (63%) outside of regular working hours. Fifteen (56%) EMUs requested a signed informed consent before admission. All EMUs performed tapering/withdrawal of antiepileptic drugs, 14 (52%) prior to admission to an EMU. Specific protocols on antiepileptic drugs (AED) tapering were available in four (15%) EMUs. Standardized Operating Procedures (SOP) for the treatment of seizure clusters and status epilepticus were available in 16 (59%). Safety measures implemented by EMUs were: alarm seizure buttons in 21 (78%), restricted patient's ambulation in 19 (70%), guard rails in 16 (59%), and specially designated bathrooms in 7 (26%). Average costs for one inpatient day in EMU ranged between 100 and 2200 Euros. This study shows a considerable diversity in the organization and practice patterns across European epilepsy monitoring units. The collected data may contribute to the development and implementation of evidence-based recommended practices in LTM services across Europe

Causal hierarchy within the thalamo-cortical network in spike and wave discharges

Background: Generalised spike wave (GSW) discharges are the electroencephalographic (EEG) hallmark of absence seizures, clinically characterised by a transitory interruption of ongoing activities and impaired consciousness, occurring during states of reduced awareness. Several theories have been proposed to explain the pathophysiology of GSW discharges and the role of thalamus and cortex as generators. In this work we extend the existing theories by hypothesizing a role for the precuneus, a brain region neglected in previous works on GSW generation but already known to be linked to consciousness and awareness. We analysed fMRI data using dynamic causal modelling (DCM) to investigate the effective connectivity between precuneus, thalamus and prefrontal cortex in patients with GSW discharges. Methodology and Principal Findings: We analysed fMRI data from seven patients affected by Idiopathic Generalized Epilepsy (IGE) with frequent GSW discharges and significant GSW-correlated haemodynamic signal changes in the thalamus, the prefrontal cortex and the precuneus. Using DCM we assessed their effective connectivity, i.e. which region drives another region. Three dynamic causal models were constructed: GSW was modelled as autonomous input to the thalamus (model A), ventromedial prefrontal cortex (model B), and precuneus (model C). Bayesian model comparison revealed Model C (GSW as autonomous input to precuneus), to be the best in 5 patients while model A prevailed in two cases. At the group level model C dominated and at the population-level the p value of model C was ∼1. Conclusion: Our results provide strong evidence that activity in the precuneus gates GSW discharges in the thalamo-(fronto) cortical network. This study is the first demonstration of a causal link between haemodynamic changes in the precuneus - an index of awareness - and the occurrence of pathological discharges in epilepsy. © 2009 Vaudano et al

Uniting statistical and individual-based approaches for animal movement modelling

<div><p>The dynamic nature of their internal states and the environment directly shape animals' spatial behaviours and give rise to emergent properties at broader scales in natural systems. However, integrating these dynamic features into habitat selection studies remains challenging, due to practically impossible field work to access internal states and the inability of current statistical models to produce dynamic outputs. To address these issues, we developed a robust method, which combines statistical and individual-based modelling. Using a statistical technique for forward modelling of the IBM has the advantage of being faster for parameterization than a pure inverse modelling technique and allows for robust selection of parameters. Using GPS locations from caribou monitored in Québec, caribou movements were modelled based on generative mechanisms accounting for dynamic variables at a low level of emergence. These variables were accessed by replicating real individuals' movements in parallel sub-models, and movement parameters were then empirically parameterized using Step Selection Functions. The final IBM model was validated using both k-fold cross-validation and emergent patterns validation and was tested for two different scenarios, with varying hardwood encroachment. Our results highlighted a functional response in habitat selection, which suggests that our method was able to capture the complexity of the natural system, and adequately provided projections on future possible states of the system in response to different management plans. This is especially relevant for testing the long-term impact of scenarios corresponding to environmental configurations that have yet to be observed in real systems.</p></div