Tracking of Blood Vessels Motion from 4D-flow MRI Data.

This paper presents a novel approach to track objects from 4D-flow MRI data. A salient feature of the proposed method is that it fully exploits the geometrical and dynamical nature of the information provided by this imaging modality. The underlying idea consists in formulating the tracking problem as a data assimilation problem, in which both position and velocity observations are extracted from the 4D-flow MRI data series. Optimal state estimation is then performed in a sequential fashion via Kalman filtering. The capabilities of the method are extensively assessed in a numerical study involving synthetic and clinical data.

Reconstructing haemodynamics quantities of interest from Doppler ultrasound imaging.

The present contribution deals with the estimation of haemodynamics Quantities of Interest by exploiting Ultrasound Doppler measurements. A fast method is proposed, based on the Parameterized Background Data-Weak (PBDW) method. Several methodological contributions are described: a sub-manifold partitioning is introduced to improve the reduced-order approximation, two different ways to estimate the pressure drop are compared, and an error estimation is derived. A fully synthetic test-case on a realistic common carotid geometry is presented, showing that the proposed approach is promising in view of realistic applications.

Characterization of Spatio-Temporal Cardiac Action Potential Variability at Baseline and Under ß-Adrenergic Stimulation by Combined Unscented Kalman Filter and Double Greedy Dimension Reduction.

OBJECTIVE: Elevated spatio-temporal variability of human ventricular repolarization has been related to increased risk for ventricular arrhythmias and sudden cardiac death, particularly under ß-adrenergic stimulation ( ß-AS). This work presents a methodology for theoretical characterization of temporal and spatial repolarization variability at baseline conditions and in response to ß-AS. For any measured voltage trace, the proposed methodology estimates the parameters and state variables of an underlying human ventricular action potential (AP) model by combining Double Greedy Dimension Reduction (DGDR) with automatic selection of biomarkers and the Unscented Kalman Filter (UKF). Such theoretical characterization can facilitate subsequent characterization of underlying variability mechanisms. MATERIAL AND METHODS: Given an AP trace, initial estimates for the ionic conductances in a stochastic version of the baseline human ventricular O'Hara et al. model were obtained by DGDR. Those estimates served to initialize and update model parameter estimates by the UKF method based on formulation of an associated nonlinear state-space representation and joint estimation of model parameters and state variables. Similarly, ß-AS-induced phosphorylation levels of cellular substrates were estimated by the DGDR-UKF methodology. Performance was tested by building an experimentally-calibrated population of virtual cells, from which synthetic AP traces were generated for baseline and ß-AS conditions. RESULTS: The combined DGDR-UKF methodology led to 25% reduction in the error associated with estimation of ionic current conductances at baseline conditions and phosphorylation levels under ß-AS with respect to individual DGDR and UKF methods. This improvement was not at the expense of higher computational load, which was diminished by 90% with respect to the individual UKF method. Both temporal and spatial AP variability of repolarization were accurately characterized by the DGDR-UKF methodology. CONCLUSIONS: A combined DGDR-UKF methodology is proposed for parameter and state variable estimation of human ventricular cell models from available AP traces at baseline and under ß-AS. This methodology improves the estimation performance and reduces the convergence time with respect to individual DGDR and UKF methods and renders a suitable approach for computational characterization of spatio-temporal repolarization variability to be used for ascertainment of variability mechanisms and its relation to arrhythmogenesis.

A greedy classifier optimization strategy to assess ion channel blocking activity and pro-arrhythmia in hiPSC-cardiomyocytes.

Novel studies conducting cardiac safety assessment using human-induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs) are promising but might be limited by their specificity and predictivity. It is often challenging to correctly classify ion channel blockers or to sufficiently predict the risk for Torsade de Pointes (TdP). In this study, we developed a method combining in vitro and in silico experiments to improve machine learning approaches in delivering fast and reliable prediction of drug-induced ion-channel blockade and proarrhythmic behaviour. The algorithm is based on the construction of a dictionary and a greedy optimization, leading to the definition of optimal classifiers. Finally, we present a numerical tool that can accurately predict compound-induced pro-arrhythmic risk and involvement of sodium, calcium and potassium channels, based on hiPSC-CM field potential data.

INSPIRE: A European training network to foster research and training in cardiovascular safety pharmacology.

Safety pharmacology is an essential part of drug development aiming to identify, evaluate and investigate undesirable pharmacodynamic properties of a drug primarily prior to clinical trials. In particular, cardiovascular adverse drug reactions (ADR) have halted many drug development programs. Safety pharmacology has successfully implemented a screening strategy to detect cardiovascular liabilities, but there is room for further refinement. In this setting, we present the INSPIRE project, a European Training Network in safety pharmacology for Early Stage Researchers (ESRs), funded by the European Commission's H2020-MSCA-ITN programme. INSPIRE has recruited 15 ESR fellows that will conduct an individual PhD-research project for a period of 36 months. INSPIRE aims to be complementary to ongoing research initiatives. With this as a goal, an inventory of collaborative research initiatives in safety pharmacology was created and the ESR projects have been designed to be complementary to this roadmap. Overall, INSPIRE aims to improve cardiovascular safety evaluation, either by investigating technological innovations or by adding mechanistic insight in emerging safety concerns, as observed in the field of cardio-oncology. Finally, in addition to its hands-on research pillar, INSPIRE will organize a number of summer schools and workshops that will be open to the wider community as well. In summary, INSPIRE aims to foster both research and training in safety pharmacology and hopes to inspire the future generation of safety scientists.

Reducing Hypermuscularization of the Transitional Segment Between Arterioles and Capillaries Protects Against Spontaneous Intracerebral Hemorrhage.

BACKGROUND: Spontaneous deep intracerebral hemorrhage (ICH) is a devastating subtype of stroke without specific treatments. It has been thought that smooth muscle cell (SMC) degeneration at the site of arteriolar wall rupture may be sufficient to cause hemorrhage. However, deep ICHs are rare in some aggressive small vessel diseases that are characterized by significant arteriolar SMC degeneration. Here we hypothesized that a second cellular defect may be required for the occurrence of ICH. METHODS: We studied a genetic model of spontaneous deep ICH using Col4a1+/G498V and Col4a1+/G1064D mouse lines that are mutated for the α1 chain of collagen type IV. We analyzed cerebroretinal microvessels, performed genetic rescue experiments, vascular reactivity analysis, and computational modeling. We examined postmortem brain tissues from patients with sporadic deep ICH. RESULTS: We identified in the normal cerebroretinal vasculature a novel segment between arterioles and capillaries, herein called the transitional segment (TS), which is covered by mural cells distinct from SMCs and pericytes. In Col4a1 mutant mice, this TS was hypermuscularized, with a hyperplasia of mural cells expressing more contractile proteins, whereas the upstream arteriole exhibited a loss of SMCs. TSs mechanistically showed a transient increase in proliferation of mural cells during postnatal maturation. Mutant brain microvessels, unlike mutant arteries, displayed a significant upregulation of SM genes and Notch3 target genes, and genetic reduction of Notch3 in Col4a1+/G498V mice protected against ICH. Retina analysis showed that hypermuscularization of the TS was attenuated, but arteriolar SMC loss was unchanged in Col4a1+/G498V, Notch3+/- mice. Moreover, hypermuscularization of the retinal TS increased its contractility and tone and raised the intravascular pressure in the upstream feeding arteriole. We similarly found hypermuscularization of the TS and focal arteriolar SMC loss in brain tissues from patients with sporadic deep ICH. CONCLUSIONS: Our results suggest that hypermuscularization of the TS, through increased Notch3 activity, is involved in the occurrence of ICH in Col4a1 mutant mice, by raising the intravascular pressure in the upstream feeding arteriole and promoting its rupture at the site of SMC loss. Our human data indicate that these 2 mutually reinforcing vascular defects may represent a general mechanism of deep ICH.

How to choose biomarkers in view of parameter estimation.

In numerous applications in biophysics, physiology and medicine, the system of interest is studied by monitoring quantities, called biomarkers, extracted from measurements. These biomarkers convey some information about relevant hidden quantities, which can be seen as parameters of an underlying model. In this paper we propose a strategy to automatically design biomarkers to estimate a given parameter. Such biomarkers are chosen as the solution of a sparse optimization problem given a user-supplied dictionary of candidate features. The method is in particular illustrated with two realistic applications, one in electrophysiology and the other in hemodynamics. In both cases, our algorithm provides composite biomarkers which improve the parameter estimation problem.

Modelling variability in cardiac electrophysiology: a moment-matching approach.

The variability observed in action potential (AP) cardiomyocyte measurements is the consequence of many different sources of randomness. Often ignored, this variability may be studied to gain insight into the cell ionic properties. In this paper, we focus on the study of ionic channel conductances and describe a methodology to estimate their probability density function (PDF) from AP recordings. The method relies on the matching of observable statistical moments and on the maximum entropy principle. We present four case studies using synthetic and sets of experimental AP measurements from human and canine cardiomyocytes. In each case, the proposed methodology is applied to infer the PDF of key conductances from the exhibited variability. The estimated PDFs are discussed and, when possible, compared to the true distributions. We conclude that it is possible to extract relevant information from the variability in AP measurements and discuss the limitations and possible implications of the proposed approach.

Composite Biomarkers Derived from Micro-Electrode Array Measurements and Computer Simulations Improve the Classification of Drug-Induced Channel Block.

The Micro-Electrode Array (MEA) device enables high-throughput electrophysiology measurements that are less labor-intensive than patch-clamp based techniques. Combined with human-induced pluripotent stem cells cardiomyocytes (hiPSC-CM), it represents a new and promising paradigm for automated and accurate in vitro drug safety evaluation. In this article, the following question is addressed: which features of the MEA signals should be measured to better classify the effects of drugs? A framework for the classification of drugs using MEA measurements is proposed. The classification is based on the ion channels blockades induced by the drugs. It relies on an in silico electrophysiology model of the MEA, a feature selection algorithm and automatic classification tools. An in silico model of the MEA is developed and is used to generate synthetic measurements. An algorithm that extracts MEA measurements features designed to perform well in a classification context is described. These features are called composite biomarkers. A state-of-the-art machine learning program is used to carry out the classification of drugs using experimental MEA measurements. The experiments are carried out using five different drugs: mexiletine, flecainide, diltiazem, moxifloxacin, and dofetilide. We show that the composite biomarkers outperform the classical ones in different classification scenarios. We show that using both synthetic and experimental MEA measurements improves the robustness of the composite biomarkers and that the classification scores are increased.

Nonparametric k-nearest-neighbor entropy estimator.

A nonparametric k-nearest-neighbor-based entropy estimator is proposed. It improves on the classical Kozachenko-Leonenko estimator by considering nonuniform probability densities in the region of k-nearest neighbors around each sample point. It aims to improve the classical estimators in three situations: first, when the dimensionality of the random variable is large; second, when near-functional relationships leading to high correlation between components of the random variable are present; and third, when the marginal variances of random variable components vary significantly with respect to each other. Heuristics on the error of the proposed and classical estimators are presented. Finally, the proposed estimator is tested for a variety of distributions in successively increasing dimensions and in the presence of a near-functional relationship. Its performance is compared with a classical estimator, and a significant improvement is demonstrated.

An information-theoretic approach to assess practical identifiability of parametric dynamical systems.

A new approach for assessing parameter identifiability of dynamical systems in a Bayesian setting is presented. The concept of Shannon entropy is employed to measure the inherent uncertainty in the parameters. The expected reduction in this uncertainty is seen as the amount of information one expects to gain about the parameters due to the availability of noisy measurements of the dynamical system. Such expected information gain is interpreted in terms of the variance of a hypothetical measurement device that can measure the parameters directly, and is related to practical identifiability of the parameters. If the individual parameters are unidentifiable, correlation between parameter combinations is assessed through conditional mutual information to determine which sets of parameters can be identified together. The information theoretic quantities of entropy and information are evaluated numerically through a combination of Monte Carlo and k-nearest neighbour methods in a non-parametric fashion. Unlike many methods to evaluate identifiability proposed in the literature, the proposed approach takes the measurement-noise into account and is not restricted to any particular noise-structure. Whilst computationally intensive for large dynamical systems, it is easily parallelisable and is non-intrusive as it does not necessitate re-writing of the numerical solvers of the dynamical system. The application of such an approach is presented for a variety of dynamical systems--ranging from systems governed by ordinary differential equations to partial differential equations--and, where possible, validated against results previously published in the literature.

Advection modes by optimal mass transfer.

Classical model reduction techniques approximate the solution of a physical model by a limited number of global modes. These modes are usually determined by variants of principal component analysis. Global modes can lead to reduced models that perform well in terms of stability and accuracy. However, when the physics of the model is mainly characterized by advection, the nonlocal representation of the solution by global modes essentially reduces to a Fourier expansion. In this paper we describe a method to determine a low-order representation of advection. This method is based on the solution of Monge-Kantorovich mass transfer problems. Examples of application to point vortex scattering, Korteweg-de Vries equation, and hurricane Dean advection are discussed.