Stochastic dynamic causal modeling of working memory connections in cocaine dependence

Although reduced working memory brain activation has been reported in several brain regions of cocaine‐dependent subjects compared with controls, very little is known about whether there is altered connectivity of working memory pathways in cocaine dependence. This study addresses this issue by using functional magnetic resonance imaging‐based stochastic dynamic causal modeling (DCM) analysis to study the effective connectivity of 19 cocaine‐dependent subjects and 14 healthy controls while performing a working memory task. Stochastic DCM is an advanced method that has recently been implemented in SPM8 that can obtain improved estimates, relative to deterministic DCM, of hidden neuronal causes before convolution with the hemodynamic response. Thus, stochastic DCM may be less influenced by the confounding effects of variations in blood oxygen level‐dependent response caused by disease or drugs. Based on the significant regional activation common to both groups and consistent with previous working memory activation studies, seven regions of interest were chosen as nodes for DCM analyses. Bayesian family level inference, Bayesian model selection analyses, and Bayesian model averaging (BMA) were conducted. BMA showed that the cocaine‐dependent subjects had large differences compared with the control subjects in the strengths of prefrontal–striatal modulatory (B matrix) DCM parameters. These findings are consistent with altered cortical–striatal networks that may be related to reduced dopamine function in cocaine dependence. As far as we are aware, this is the first between‐group DCM study using stochastic methodology. Hum Brain Mapp 35:760–778, 2014. © 2012 Wiley Periodicals, Inc.     

Bayesian age–period–cohort models with versatile interactions and long‐term predictions: mortality and population in Finland 1878–2050

Age–period–cohort (APC) models are widely used for studying time trends of disease incidence or mortality. Model identifiability has become less of a problem with Bayesian APC models. These models are usually based on random walk (RW1, RW2) smoothing priors. For long and complex time series and for long predicted periods, these models as such may not be adequate. We present two extensions for the APC models. First, we introduce flexible interactions between the age, period and cohort effects based on a two‐dimensional conditional autoregressive smoothing prior on the age/period plane. Our second extension uses autoregressive integrated (ARI) models to provide reasonable long‐term predictions. To illustrate the utility of our model framework, we provide stochastic predictions for the Finnish male and female population, in 2010–2050. For that, we first study and forecast all‐cause male and female mortality in Finland, 1878–2050, showing that using an interaction term is needed for fitting and interpreting the observed data. We then provide population predictions using a cohort component model, which also requires predictions for fertility and migration. As our main conclusion, ARI models have better properties for predictions than the simple RW models do, but mixing these prediction models with RW1 or RW2 smoothing priors for observed periods leads to a model that is not fully consistent. Further research with our model framework will concentrate on using a more consistent model for smoothing and prediction, such as autoregressive integrated moving average models with state‐space methods or Gaussian process priors. Copyright © 2013 John Wiley & Sons, Ltd.     

L2‐L∞ filtering for a class of stochastic nonlinear systems with aperiodic sampling

This work investigates the problem of L₂‐L_∞ filtering for a class of stochastic nonlinear systems with nonuniform sampling. The sampled‐data filter developed in this paper is an impulsive differential system whose states change abruptly at every sampling instant. The resulting filtering error system is modeled as a stochastic nonlinear impulsive differential system. The goal is to propose a method for designing a target filter that ensures the stochastic asymptotic stability of the filtering error system and guarantees a prescribed L₂‐L_∞ performance. Based on a time‐varying Lyapunov functional, by virtue of a convex combination technique, a design method to achieve such a filter is formulated in the form of solving a set of linear matrix inequalities. The effectiveness of the proposed filtering strategy is shown via a numerical example of a stochastic Chua's circuit system.     

Fluctuation broadening in carbon nanotube resonators

We simulated the behavior of suspended carbon nanotube resonators over a broad range of temperatures to explore the physics of semiflexible polymers in underdamped environments. We find that thermal fluctuations induce strong coupling between resonance modes. This effect leads to spectral fluctuations that readily account for the experimentally observed quality factors Q ∼ 100 at 300 K. Using a mean-field approach to describe fluctuations, we analytically calculate Q and frequency shifts in tensioned and buckled carbon nanotubes and find excellent agreement with simulations.     

Task-dependent organization of brain regions active during rest

The human brain demonstrates complex yet systematic patterns of neural activity at rest. We examined whether functional connectivity among those brain regions typically active during rest depends on ongoing and recent task demands and individual differences. We probed the temporal coordination among these regions during periods of language comprehension and during the rest periods that followed comprehension. Our findings show that the topography of this “rest network” varies with exogenous processing demands. The network encompassed more highly interconnected regions during rest than during listening, but also when listening to unsurprising vs. surprising information. Furthermore, connectivity patterns during rest varied as a function of recent listening experience. Individual variability in connectivity strength was associated with cognitive function: more attentive comprehenders demonstrated weaker connectivity during language comprehension, and a greater differentiation between connectivity during comprehension and rest. The regions we examined have generally been thought to form an invariant physiological and functional network whose activity reflects spontaneous cognitive processes. Our findings suggest that their function extends beyond the mediation of unconstrained thought, and that they play an important role in higher-level cognitive function.     

Estimation of measles vaccine coverage needed to prevent transmission in schools

Background: The aim of the present study was to produce the first estimation in Japan of the basic reproduction number (R₀) and the minimum level of vaccine coverage needed to prevent measles outbreaks (P_c).
Methods: A questionnaire survey was conducted during two measles outbreaks among 12–15-year-old middle school students in one prefecture in spring, from the end of February to the beginning of May 2002, and a stochastic mathematical model was constructed to calculate vaccine effectiveness (VE) and the basic reproduction number (R₀). P_c was calculated from R₀ and VE.
Results: In outbreak 1 (school A), 62 (94%) of 66 patients responded to the questionnaire. Of a total of 601 students, 534 (88.9%) responded. Of these, 82.6% (441/534) had previously received measles vaccine. In outbreak 2 (school B), 20 (99%) of 21 patients responded. Of a total of 375 students, 373 (99.5%) responded. Of these, 317 (85.0%) received measles immunization. Mathematical analysis was as follows: in outbreak 1 R₀ was 7.40 (95% confidence interval [CI]: 7.36–7.44) and VE was 76.55% (95%CI: 53.24–87.54). In outbreak 2, R₀ was 18.89 (95%CI: 18.88–18.90) and VE was 98.54% (95%CI: 94.89–99.73). Consequently, P_c was 112.97% (95%CI: 92.29–145.52) in outbreak 1 and 96.11% (95%CI: 93.81–98.53) in outbreak 2.
Conclusion: Because of the lower VE in outbreak 1, measles virus transmission could not have been stopped even if all students received a single dose of vaccine. In outbreak 2, with higher VE, the outbreak could have been prevented by increasing the proportion of students who had been vaccinated.     

Models of hepatic elimination: Comparison of stochastic models to describe residence time distributions and to predict the influence of drug distribution,enzyme heterogeneity,and systemic recycling on hepatic elimination

The residence time distribution of noneliminated solutes in the liver can be represented by a variety of stochastic models. The dispersion model (closed and mixed boundary conditions), gamma distribution, log normal distribution and normal distribution models were used to describe output concentration-time profiles after bolus injections into the liver of labeled erythrocytes and albumin. The dispersion model and log normal distribution model provide the best representation of the data and give similar estimates of relative dispersion and availability for varying hepatocellular enzyme activity. The availability of solutes eliminated from the liver by first-order kinetics is determined by the residence time distribution of the solute in the liver and not on events occurring in the liver when a uniform enzyme distribution is assumed. Both enzyme heterogeneity (axial or transverse) and hepatocyte permeability may affect solute availability. A more complex model accounting for enzyme distribution and the micromixing of solute within the liver is required for solutes undergoing saturable kinetics.     

A stochastic version of corticosteriod pharmacogenomic model

The purpose of this study was to develop a stochastic version of corticosteriod fifth generation pharmacogenomic model. The Gillespie algorithm was used to generate the independent time courses of the receptor messenger RNA (mRNA). Initial parameters for the stochastic simulation were adapted from the study by Jin et al. The result obtained from the proposed stochastic model showed an overall agreement with the deterministic fifth generation model. This study suggested that because the stochastic model takes into account the "noise" nature of gene regulation, it would have potential application in pharmacogenomic modeling.