Estimation of the hemodynamic response in event-related functional MRI: Bayesian networks as a framework for efficient Bayesian modeling and inference

A convenient way to analyze blood-oxygen-level-dependent functional magnetic resonance imaging data consists of modeling the whole brain as a stationary, linear system characterized by its transfer function: the hemodynamic response function (HRF). HRF estimation, though of the greatest interest, is still under investigation, for the problem is ill-conditioned. In this paper, we recall the most general Bayesian model for HRF estimation and show how it can beneficially be translated in terms of Bayesian graphical models, leading to 1) a clear and efficient representation of all structural and functional relationships entailed by the model, and 2) a straightforward numerical scheme to approximate the joint posterior distribution, allowing for estimation of the HRF, as well as all other model parameters. We finally apply this novel technique on both simulations and real data.     

以血管壁动态信息为参数的血液脉动流模型的研究

在传统脉动流数学模型的基础上,应用计算机多道无创检测技术和血液流体动力学理论,采用多种传感方式,以血管壁的多种动态信息为参数,建立新型血液脉动流模型。可实时、动态、多参数的检测血管壁动态信息,为临床心血管疾病提供又一种新的辅助诊断手段。     

Embedding a cardiac pulsatile model into an integrated model of the cardiovascular regulation for heart failure followup

The analysis of followup data from patients suffering from heart failure is a difficult task, due to the complex and multifactorial nature of this pathology. In this paper, we present a coupled model, integrating a pulsatile heart into a model of the short to long-term regulations of the cardiovascular system. An interface method is proposed to couple these models, which present significantly different time scales. Results from a sensitivity analysis of the original and integrated models are proposed with simulations reproducing the main effects of the short- and long-term responses of an acute decompensated heart failure episode on a patient undergoing cardiac resynchronization therapy.     

Virtual angiography for visualization and validation of computational models of aneurysm hemodynamics

It has recently become possible to simulate aneurysmal blood flow dynamics in a patient-specific manner via the coupling of three-dimensional (3-D) X-ray angiography and cmputational fluid dynamics (CFD). Before such image-based CFD models can be used in a predictive capacity, however, it must be shown that they indeed reproduce the in vivo hemodynamic environment. Motivated by the fact that there are currently no techniques for adequately measuring complex blood velocity fields in vivo, in this paper we describe how cine X-ray angiograms may be simulated for the purpose of indirectly validating patient-sperific CFD models. Mimicking the radiological procedure, a virtual angiogram is constructed by first simulating the time-varying injection of contrast agent into a precomputed, patient-specific CFD model. A time-series of images is then constructed by simulating the attenuation of X-rays through the computed 3-D contrast-agent flow dynamics. Virtual angiographic images and residence time maps, here derived from an image-based CFD model of a giant aneurysm, are shown to be in excellent agreement wiith the corresponding clinical images and residence time maps, but only when the interaction between the quasisteady contrast agent injection and the pulsatile flow are properly accounted for. These virtual angiographic techniques pave the way for validating image-based CFD models against routinely available clinical data, and provide a means of visualizing complex, 3-D blood flow dynamics in a clinically relevant manner. They also clearly show how the contrast agent injection perturbs the noraml blood flow patterns, further highlighting the potential utility of image-based CFD as a window into the true aneurysmal hemodynamics.     

Reconstruction and quantification of the carotid artery bifurcation from 3-D ultrasound images

Three-dimensional (3-D) ultrasound is a relatively new technique, which is well suited to imaging superficial blood vessels, and potentially provides a useful, noninvasive method for generating anatomically realistic 3-D models of the peripheral vasculature. Such models are essential for accurate simulation of blood flow using computational fluid dynamics (CFD), but may also be used to quantify atherosclerotic plaque more comprehensively than routine clinical methods. In this paper, we present a spline-based method for reconstructing the normal and diseased carotid artery bifurcation from images acquired using a freehand 3-D ultrasound system. The vessel wall (intima-media interface) and lumen surfaces are represented by a geometric model defined using smoothing splines. Using this coupled wall-lumen model, we demonstrate how plaque may be analyzed automatically to provide a comprehensive set of quantitative measures of size and shape, including established clinical measures, such as degree of (diameter) stenosis. The geometric accuracy of 3-D ultrasound reconstruction is assessed using pulsatile phantoms of the carotid bifurcation, and we conclude by demonstrating the in vivo application of the algorithms outlined to 3-D ultrasound scans from a series of patient carotid arteries.     

The method of double averaging: an approach for modeling power-factor-correction switching converters

This paper describes the modeling of power-factor-correction converters under average-current-mode control, which are widely used in switch-mode power supply applications. The objective is to identify stability boundaries in terms of major circuit parameters for facilitating design of such converters. The approach employs a double averaging procedure, which first applies the usual averaging over the switching period and subsequently applies generalized averaging over the mains period. The resulting model, after two averaging steps and application of a harmonic balance procedure, is nonlinear and capable of describing the low-frequency nonlinear dynamics of the system. The parameter ranges within which stable operation is guaranteed can be accurately and easily found using this model. Experimental measurements are provided for verification of the analytical results.     

A large-scale, energetic model of cardiovascular homeostasis predicts dynamics of arterial pressure in humans.

The energetic balance of forces in the cardiovascular system is vital to the stability of blood flow to all physiological systems in mammals. Yet, a large-scale, theoretical model, summarizing the energetic balance of major forces in a single, mathematically closed system has not been described. Although a number of computer simulations have been successfully performed with the use of analog models, the analysis of energetic balance of forces in such models is obscured by a big number of interacting elements. Hence, the goal of our study was to develop a theoretical model that represents large-scale, energetic balance in the cardiovascular system, including the energies of arterial pressure wave, blood flow, and the smooth muscle tone of arterial walls. Because the emphasis of our study was on tracking beat-to-beat changes in the balance of forces, we used a simplified representation of the blood pressure wave as a trapezoidal pressure-pulse with a strong-discontinuity leading front. This allowed significant reduction in the number of required parameters. Our approach has been validated using theoretical analysis, and its accuracy has been confirmed experimentally. The model predicted the dynamics of arterial pressure in human subjects undergoing physiological tests and provided insights into the relationships between arterial pressure and pressure wave velocity.     

Mathematical Analysis of Cardiovascular Function

This paper identifies several published models of cardiovascular function, and attempts to analyze them in terms of the physiological processes which are embedded in their structure. It appears that representative models have increased in size and complexity in direct proportion to increases in the power and suitability of available computing machinery. An early focus on pulsatile hemodynamics within a single vascular compartment has evolved into analyses of ventricular filling and ejection, the function of the circulatory system as a closed system, longterm cardiovascular control, and the interaction of the circulatory system with other organ systems.     

Line-Frequency Instability of PFC Power Supplies

Typically, a power-factor-correction (PFC) power supply consists of two stages, one responsible for PFC and the other for voltage regulation. In this paper, we propose to use a constant power sink to represent the voltage regulating stage, resulting in an analytically tractable model that is able to predict the period-doubled oscillations at line frequency that are not detectable by other conventional models. Using a generalized averaging approach, we investigate the low-frequency dynamics and derive closed-form stability conditions that accurately locate the stability boundaries on selected parameter planes. The model can be conveniently used to evaluate the performance of PFC power supplies, such as harmonic distortion and power factor. Experimental results are presented to verify the model.      

The Burg algorithm for segments

In many applications, the duration of an uninterrupted measurement of a time series is limited. However, it is often possible to obtain several separate segments of data. The estimation of an autoregressive model from this type of data is discussed. A straightforward approach is to take the average of models estimated from each segment separately. In this way, the variance of the estimated parameters is reduced. However, averaging does not reduce the bias in the estimate. With the Burg algorithm for segments, both the variance and the bias in the estimated parameters are reduced by fitting a single model to all segments simultaneously. As a result, the model estimated with the Burg algorithm for segments is more accurate than models obtained with averaging. The new weighted Burg algorithm for segments allows combining segments of different amplitudes     

Behavioral Modeling Methods for Switched-Capacitor Σ∆ Modulators

Sigma-delta Modulators (SigmaDeltaMs) are cornerstone elements in oversampled analog-to-digital converters and digital-to-analog converters (DAC). Although transistor-level simulation is the most accurate approach known for these components, this method becomes impractical for complex systems due to its long computational time requirements. Behavioral modeling has become a viable solution to this problem. In this paper, we study styles and issues in the accurate modeling of low-power, high-speed SigmaDeltaMs and introduce two new behavioral models for switched-capacitor (SC) integrators. The first model is based on the SC integrator transient response, including the effects of the amplifier transconductance, output conductance, and the dynamic capacitive loading effect on the settling time. The second model is based on a symbolic node admittance matrix representation of the system. Nonidealities such as jitter, thermal noise, and DAC mismatch are also addressed and included in a dual-band, GSM/WCDMA, second-order, multibit SigmaDeltaM model with individual level averaging. VHDL-AMS and MATLAB Simulink were used as modeling languages. Both models are validated against experimental data, showing competitive results in the signal-to-noise-plus-distortion ratio. A comparative analysis between the proposed and a traditional model is presented, with emphasis on the degrading effects due to the integrator dynamics. Moreover, a general simulation speed analysis of the proposed models is addressed.     

Image Guided Personalization of Reaction-Diffusion Type Tumor Growth Models Using Modified Anisotropic Eikonal Equations

Reaction-diffusion based tumor growth models have been widely used in the literature for modeling the growth of brain gliomas. Lately, recent models have started integrating medical images in their formulation. Including different tissue types, geometry of the brain and the directions of white matter fiber tracts improved the spatial accuracy of reaction-diffusion models. The adaptation of the general model to the specific patient cases on the other hand has not been studied thoroughly yet. In this paper, we address this adaptation. We propose a parameter estimation method for reaction-diffusion tumor growth models using time series of medical images. This method estimates the patient specific parameters of the model using the images of the patient taken at successive time instances. The proposed method formulates the evolution of the tumor delineation visible in the images based on the reaction-diffusion dynamics; therefore, it remains consistent with the information available. We perform thorough analysis of the method using synthetic tumors and show important couplings between parameters of the reaction-diffusion model. We show that several parameters can be uniquely identified in the case of fixing one parameter, namely the proliferation rate of tumor cells. Moreover, regardless of the value the proliferation rate is fixed to, the speed of growth of the tumor can be estimated in terms of the model parameters with accuracy. We also show that using the model-based speed, we can simulate the evolution of the tumor for the specific patient case. Finally, we apply our method to two real cases and show promising preliminary results.      

A 2D non-parabolic six-moments model

In order to describe carrier transport in inversion layers we have developed a two-dimensional non-parabolic macroscopic transport model up to the sixth order. To model the transport parameters with as few simplifying assumptions as possible, we apply an extraction technique from Subband Monte Carlo simulations followed by an interpolation within these Monte Carlo tables through the whole inversion layer. Important effects like surface-roughness scattering as well as quantization are inherently considered in the Subband Monte Carlo data, which are used to model higher-order mobilities as well as the macroscopic relaxation times as a function of the effective field and the carrier temperature. The parameters are compared with the results obtained from models using bulk Monte Carlo data, where neither surface roughness nor quantization are considered. The models are applied to a UTB SOI-MOSFET and their predictions are discussed for different gate lengths.     

Noise-aware interconnect power optimization in domino logic synthesis

Realization of high-performance domino logic depends strongly on energy-efficient and noise-tolerant interconnect design in ultradeep submicrometer processes. We characterize the cycle-averaged power model for interconnects accounting for switching statistics and dynamic behaviors. For the sake of signal integrity, cross-coupling effects are also characterized, which reflect logical correlation between adjacent wires. Based on the new models for interconnect power and capacitive crosstalk, we optimize the coupling power consumed by interconnects with crosstalk constraints. Experimental results show that optimized designs save the power consumption about 14% on average.     

Cross validation for selection of cortical interaction models from scalp EEG or MEG

A cross-validation (CV) method based on state-space framework is introduced for comparing the fidelity of different cortical interaction models to the measured scalp electroencephalogram (EEG) or magnetoencephalography (MEG) data being modeled. A state equation models the cortical interaction dynamics and an observation equation represents the scalp measurement of cortical activity and noise. The measured data are partitioned into training and test sets. The training set is used to estimate model parameters and the model quality is evaluated by computing test data innovations for the estimated model. Two CV metrics normalized mean square error and log-likelihood are estimated by averaging over different training/test partitions of the data. The effectiveness of this method of model selection is illustrated by comparing two linear modeling methods and two nonlinear modeling methods on simulated EEG data derived using both known dynamic systems and measured electrocorticography data from an epilepsy patient.     

一类电路系统的分岔分析及超混沌控制研究

为了揭示电路系统丰富的非线性动力学行为,提高电路系统的稳定性,避免混沌或超混沌电路对元器件的危害,针对一类电路系统模型,应用现代数学中的微分方程理论和非线性动力学的方法,分析了系统发生分岔的条件,并通过数值分析验证了该理论结果。研究发现系统在一定参数条件下存在内衣马克-沙克分岔和倍周期分岔,随着参数的变化系统演化为混沌和超混沌。针对目前超混沌控制方法的研究较少,而且控制的周期轨道多是低周期轨道,提出一种节约能量并能将系统控制到高倍周期和概周期的方法,为研究许多现实离散系统模型提供了一种新的方法,对于研究电路系统提供了一条新的思路,因而具有一定的理论意义和实用价值。     

Measurement and modelling of radiative coupling in oscillatorarrays

Arrays of coupled oscillators can be used for power-combining at microwave and millimeter-wave frequencies, and have been successfully demonstrated with a variety of devices. Such arrays have also recently been mode-locked for pulse generation, and can be configured for phase-shifterless beam scanning. The nonlinear theory of coupled oscillator phase dynamics depends crucially on the parameters describing the coupled between oscillators. Methods for experimental characterization of these parameters are described here, and simple models which reproduce the measurements quite well are developed. The models apply to radiative coupling and the effects of external reflectors which are sometimes used for stabilization. The theory is verified with a two-oscillator system     

Network traffic prediction based on INGARCH model

In this paper, we introduce the integer-valued generalized autoregressive conditional heteroscedasticity (INGARCH) as a network traffic prediction model. As the INGARCH is known as a non-linear analytical model that could capture the characteristics of network traffic such as Poisson packet arrival and long-range dependence property, INGARCH seems to be an adequate model for network traffic prediction. Based on the investigation for the traffic arrival process in various network topologies including IoT and VANET, we could confirm that assuming the Poisson process as packet arrival works for some networks and environments of networks. The prediction model is generated by estimating parameters of the INGARCH process and predicting the Poisson parameters of future-steps ahead process using the conditional maximum likelihood estimation method and prediction procedure, respectively. Its performance is compared with those of three different models; autoregressive integrated moving average, GARCH, and long short-term memory recurrent neural network. Anonymized passive traffic traces provided by the Center for Applied Internet Data Analysis are used in the experiment. Numerical results show that the proposed model predicts better than the three models in terms of measurements used in prediction models. Based on the study, we can conclude the followings: INGARCH can capture the characteristics of network traffic better than other statistic models, it is more tractable than neural networks (NNs) overcoming the black-box nature of NNs, and the performances of some statistical models are comparable or even superior to those of NNs, especially when the data is insufficient to apply deep NNs.