Three-dimensional MRI assessment of regional wall stress after acute myocardial infarction predicts postdischarge cardiac events

PURPOSE: To determine the prognostic significance of systolic wall stress (SWS) after reperfused acute myocardial infarction (AMI) using MRI. MATERIALS AND METHODS: A total of 105 patients underwent MRI 7.8 +/- 4.2 days after AMI reperfusion. SWS was calculated by using a three-dimensional (3D) MRI approach to left ventricular (LV) wall thickness and to the radius of curvature. Between hospital discharge and the end of follow-up, an average of 4.1 +/- 1.7 years after AMI, 19 patients experienced a major cardiac event, including cardiac death, nonfatal reinfarction or heart failure (18.3%). RESULTS: The results were mainly driven by heart failure outcome. In univariate analysis the following factors were predictive of postdischarge major adverse cardiac events: 1) at the time of AMI: higher heart rate, previous calcium antagonist treatment, in-hospital congestive heart failure, proximal left anterior descending artery (LAD) occlusion, a lower ejection fraction, higher maximal ST segment elevation before reperfusion, and ST segment reduction lower than 50% after reperfusion; 2) MRI parameters: higher LV end-systolic volume, lower ejection fraction, higher global SWS, higher SWS in the infarcted area (SWS MI) and higher SWS in the remote myocardium (SWS remote). In the final multivariate model, only SWS MI (odds ratio [OR]: 1.62; 95% confidence interval [CI]: 1.01-2.60; P = 0.046) and SWS remote (OR: 2.17; 95% CI: 1.02-4.65; P = 0.046) were independent predictors. CONCLUSION: Regional SWS assessed by means of MRI a few days after AMI appears to be strong predictor of postdischarge cardiac events, identifying a subset of at risk patients who could qualify for more aggressive management

Numerical Solution of Differential Equations by the Parker-Sochacki Method

A tutorial is presented which demonstrates the theory and usage of the Parker-Sochacki method of numerically solving systems of differential equations. Solutions are demonstrated for the case of projectile motion in air, and for the classical Newtonian N-body problem with mutual gravitational attraction.Comment: Added in July 2010: This tutorial has been posted since 1998 on a university web site, but has now been cited and praised in one or more refereed journals. I am therefore submitting it to the Cornell arXiv so that it may be read in response to its citations. See "Spiking neural network simulation: numerical integration with the Parker-Sochacki method:" J. Comput Neurosci, Robert D. Stewart & Wyeth Bair and http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2717378

A Markovian event-based framework for stochastic spiking neural networks

In spiking neural networks, the information is conveyed by the spike times, that depend on the intrinsic dynamics of each neuron, the input they receive and on the connections between neurons. In this article we study the Markovian nature of the sequence of spike times in stochastic neural networks, and in particular the ability to deduce from a spike train the next spike time, and therefore produce a description of the network activity only based on the spike times regardless of the membrane potential process. To study this question in a rigorous manner, we introduce and study an event-based description of networks of noisy integrate-and-fire neurons, i.e. that is based on the computation of the spike times. We show that the firing times of the neurons in the networks constitute a Markov chain, whose transition probability is related to the probability distribution of the interspike interval of the neurons in the network. In the cases where the Markovian model can be developed, the transition probability is explicitly derived in such classical cases of neural networks as the linear integrate-and-fire neuron models with excitatory and inhibitory interactions, for different types of synapses, possibly featuring noisy synaptic integration, transmission delays and absolute and relative refractory period. This covers most of the cases that have been investigated in the event-based description of spiking deterministic neural networks

Atrial fibrillation management strategies in routine clinical practice: insights from the International RealiseAF Survey

Atrial fibrillation (AF) can be managed with rhythm- or rate-control strategies. There are few data from routine clinical practice on the frequency with which each strategy is used and their correlates in terms of patients' clinical characteristics, AF control, and symptom burden.RealiseAF was an international, cross-sectional, observational survey of 11,198 patients with AF. The aim of this analysis was to describe patient profiles and symptoms according to the AF management strategy used. A multivariate logistic regression identified factors associated with AF management strategy at the end of the visit.Among 10,497 eligible patients, 53.7% used a rate-control strategy, compared with 34.5% who used a rhythm-control strategy. In 11.8% of patients, no clear strategy was stated. The proportion of patients with AF-related symptoms (EHRA Class > = II) was 78.1% (n = 4396/5630) for those using a rate-control strategy vs. 67.8% for those using a rhythm-control strategy (p = II.In the RealiseAF routine clinical practice survey, rate control was more commonly used than rhythm control, and a change in strategy was uncommon, even in symptomatic patients. In almost 12% of patients, no clear strategy was stated. Physician awareness regarding optimal management strategies for AF may be improved

A small temperature rise may contribute towards the apparent induction by microwaves of heatshock gene expression in the nematode Caenorhabditis elegans

We have previously reported that low-intensity microwave exposure (0.75-1.0 GHz CW at 0.5 W; SAR 4-40 mW kg-1) can induce an apparently non-thermal heat-shock response in Caenorhabditis elegans worms carrying hsp16-1::reporter genes. Using matched copper TEM cells for both sham and exposed groups, we can detect only modest reporter induction in the latter (15-20% after 2.5 h at 26°C, rising to ~50% after 20 h). Traceable calibration of our copper TEM cell by the National Physical Laboratory (NPL) reveals significant power loss within the cell (8.5% at 1.0 GHz), accompanied by slight heating of exposed samples (~0.3°C at 1.0 W). Thus exposed samples are in fact slightly warmer (by ≤0.2°C at 0.5 W) than sham controls. Following NPL recommendations, our TEM cell design was modified with the aim of reducing both power loss and consequent heating. In the modified silver-plated cell, power loss is only 1.5% at 1.0 GHz, and sample warming is reduced to ~ 0.15°C at 1.0 W (i.e. ≤ 0.1°C at 0.5 W). Under sham:sham conditions, there is no difference in reporter expression between the modified silverplated TEM cell and an unmodified copper cell. However, worms exposed to microwaves (1.0 GHz and 0.5 W) in the silver-plated cell also show no detectable induction of reporter expression relative to sham controls in the copper cell. Thus the 20% “microwave induction” observed using two copper cells may be caused by a small temperature difference between sham and exposed conditions. In worms incubated for 2.5 h at 26.0, 26.2 and 27.0°C (with no microwave field), there is a consistent and significant increase in reporter expression between 26.0 and 26.2°C (by ~20% in each of 6 independent runs), but paradoxically expression levels at 27.0°C are similar to those seen at 26.0°C. This surprising result is in line with other evidence pointing towards complex regulation of hsp16-1 gene expression across the sub-heat-shock range of 25-27.5°C in C. elegans. We conclude that our original interpretation of a non-thermal effect of microwaves cannot be sustained; at least part of the explanation appears to be thermal

Simulation of networks of spiking neurons: A review of tools and strategies

We review different aspects of the simulation of spiking neural networks. We start by reviewing the different types of simulation strategies and algorithms that are currently implemented. We next review the precision of those simulation strategies, in particular in cases where plasticity depends on the exact timing of the spikes. We overview different simulators and simulation environments presently available (restricted to those freely available, open source and documented). For each simulation tool, its advantages and pitfalls are reviewed, with an aim to allow the reader to identify which simulator is appropriate for a given task. Finally, we provide a series of benchmark simulations of different types of networks of spiking neurons, including Hodgkin-Huxley type, integrate-and-fire models, interacting with current-based or conductance-based synapses, using clock-driven or event-driven integration strategies. The same set of models are implemented on the different simulators, and the codes are made available. The ultimate goal of this review is to provide a resource to facilitate identifying the appropriate integration strategy and simulation tool to use for a given modeling problem related to spiking neural networks.Comment: 49 pages, 24 figures, 1 table; review article, Journal of Computational Neuroscience, in press (2007

A Comprehensive Workflow for General-Purpose Neural Modeling with Highly Configurable Neuromorphic Hardware Systems

In this paper we present a methodological framework that meets novel requirements emerging from upcoming types of accelerated and highly configurable neuromorphic hardware systems. We describe in detail a device with 45 million programmable and dynamic synapses that is currently under development, and we sketch the conceptual challenges that arise from taking this platform into operation. More specifically, we aim at the establishment of this neuromorphic system as a flexible and neuroscientifically valuable modeling tool that can be used by non-hardware-experts. We consider various functional aspects to be crucial for this purpose, and we introduce a consistent workflow with detailed descriptions of all involved modules that implement the suggested steps: The integration of the hardware interface into the simulator-independent model description language PyNN; a fully automated translation between the PyNN domain and appropriate hardware configurations; an executable specification of the future neuromorphic system that can be seamlessly integrated into this biology-to-hardware mapping process as a test bench for all software layers and possible hardware design modifications; an evaluation scheme that deploys models from a dedicated benchmark library, compares the results generated by virtual or prototype hardware devices with reference software simulations and analyzes the differences. The integration of these components into one hardware-software workflow provides an ecosystem for ongoing preparative studies that support the hardware design process and represents the basis for the maturity of the model-to-hardware mapping software. The functionality and flexibility of the latter is proven with a variety of experimental results

Integrate and Fire Neural Networks, Piecewise Contractive Maps and Limit Cycles

We study the global dynamics of integrate and fire neural networks composed of an arbitrary number of identical neurons interacting by inhibition and excitation. We prove that if the interactions are strong enough, then the support of the stable asymptotic dynamics consists of limit cycles. We also find sufficient conditions for the synchronization of networks containing excitatory neurons. The proofs are based on the analysis of the equivalent dynamics of a piecewise continuous Poincar\'e map associated to the system. We show that for strong interactions the Poincar\'e map is piecewise contractive. Using this contraction property, we prove that there exist a countable number of limit cycles attracting all the orbits dropping into the stable subset of the phase space. This result applies not only to the Poincar\'e map under study, but also to a wide class of general n-dimensional piecewise contractive maps.Comment: 46 pages. In this version we added many comments suggested by the referees all along the paper, we changed the introduction and the section containing the conclusions. The final version will appear in Journal of Mathematical Biology of SPRINGER and will be available at http://www.springerlink.com/content/0303-681