Ernesto Zacur

Quantification of Restitution Dispersion From the Dynamic Changes of the

Action potential duration restitution (APDR) curves present spatial variations due to the electrophysiological heterogeneities present in the heart. Enhanced spatial APDR dispersion in ventricle has been suggested as an arrhythmic risk marker. In this study, we propose a method to noninvasively quantify dispersion of APDR slopes at tissue level by making only use of the surface electrocardiogram (ECG). The proposed estimate accounts for rate normalized differences in the steady-state T-wave peak to T-wave end interval (Tpe). A methodology is developed for its computation, which includes compensation for the Tpe memory lag after heart-rate (HR) changes. The capability of the proposed estimate to reflect APDR dispersion is assessed using a combination of ECG signal processing, and computational modeling and simulation. Specifically, ECG recordings of control subjects undergoing a tilt test trial are used to measure that estimate, while its capability to provide a quantification of APDR dispersion at tissue level is assessed by using a 2-D ventricular tissue simulation. From this simulation, APDR dispersion, denoted as ΔαSIM, is calculated, and pseudo-ECGs are derived. Estimates of APDR dispersion measured from the pseudo-ECGs show to correlate with ΔαSIM, being the mean relative error below 5%. A comparison of the ECG estimates obtained from tilt test recordings and the ΔαSIM values measured in silico simulations at tissue level show that differences between them are below 20%, which is within physiological variability limits. Our results provide evidence that the proposed estimate is a noninvasive measurement of APDR dispersion in ventricle. Additional results from this study confirm that Tpe adapts to HR changes much faster than the QT interval.

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BACKGROUND The size of particular sub-regions within the ventromedial prefrontal cortex (vmPFC) has been associated with fear extinction in humans. Exposure therapy is a form of extinction learning widely used in the treatment of obsessive-compulsive disorder (OCD). Here we investigated the relationship between morphometric measurements of different sub-regions of the vmPFC and exposure therapy outcome in OCD. METHOD A total of 74 OCD patients and 86 healthy controls underwent magnetic resonance imaging (MRI). Cortical thickness and volumetric measurements were obtained for the rostral anterior cingulate cortex (rACC), the medial orbital frontal cortex and the subcallosal cortex. After MRI acquisition, patients were enrolled in an exposure therapy protocol, and we assessed the relationship between MRI-derived measurements and treatment outcome. Baseline between-group differences for such measurements were also assessed. RESULTS Compared with healthy controls, OCD patients showed a thinner left rACC (p = 0.008). Also, left rACC thickness was inversely associated with exposure therapy outcome (r - 0.32, p = 0.008), and this region was significantly thinner in OCD patients who responded to exposure therapy than in those who did not (p = 0.006). Analyses based on regional volumetry did not yield any significant results. CONCLUSIONS OCD patients showed cortical thickness reductions in the left rACC, and these alterations were related to exposure therapy outcome. The precise characterization of neuroimaging predictors of treatment response derived from the study of the brain areas involved in fear extinction may optimize exposure therapy planning in OCD and other anxiety disorders.

-Wave Peak to End, Measured at the Surface ECG

Aims Acute ischemia is a major cause of sudden arrhythmic death, further promoted by potassium current blockers. Macro-reentry around the ischemic region and early afterdepolarizations (EADs) caused by electrotonic current have been suggested as potential mechanisms in animal and isolated cell studies. However, ventricular and human-specific arrhythmia mechanisms and their modulation by repolarization reserve remain unclear. The goal of this paper is to unravel multiscale mechanisms underlying the modulation of arrhythmic risk by potassium current (IKr) block in human ventricles with acute regional ischemia. Methods and results A human ventricular biophysically-detailed model, with acute regional ischemia is constructed by integrating experimental knowledge on the electrophysiological ionic alterations caused by coronary occlusion. Arrhythmic risk is evaluated by determining the vulnerable window (VW) for reentry following ectopy at the ischemic border zone. Macro-reentry around the ischemic region is the main reentrant mechanism in the ischemic human ventricle with increased repolarization reserve due to the ATP-sensitive potassium current (IK(ATP)) activation. Prolongation of refractoriness by 4% caused by 30% IKr reduction counteracts the establishment of macro-reentry and reduces the VW for reentry (by 23.5%). However, a further decrease in repolarization reserve (50% IKr reduction) is less anti-arrhythmic despite further prolongation of refractoriness. This is due to the establishment of transmural reentry enabled by electrotonically-triggered EADs in the ischemic border zone. EADs are produced by L-type calcium current (ICaL) reactivation due to prolonged low amplitude electrotonic current injected during the repolarization phase. Conclusions Electrotonically-triggered EADs are identified as a potential mechanism facilitating intramural reentry in a regionally-ischemic human ventricles model with reduced repolarization reserve.

Brain regions related to fear extinction in obsessive-compulsive disorder and its relation to exposure therapy outcome: a morphometric study

We introduce a facial deformation system that allows artists to define and customize a facial rig and later apply the same rig to different face models. The method uses a set of landmarks that define specific facial features and deforms the rig anthropometrically. We find the correspondence of the main attributes of a source rig, transfer them to different three‐demensional (3D) face models and automatically generate a sophisticated facial rig. The method is general and can be used with any type of rig configuration. We show how the landmarks, combined with other deformation methods, can adapt different influence objects (NURBS surfaces, polygon surfaces, lattice) and skeletons from a source rig to individual face models, allowing high quality geometric or physically‐based animations. We describe how it is possible to deform the source facial rig, apply the same deformation parameters to different face models and obtain unique expressions. We enable reusing of existing animation scripts and show how shapes nicely mix one with the other in different face models. We describe how our method can easily be integrated in an animation pipeline. We end with the results of tests done with major film and game companies to show the strength of our proposal.

Early afterdepolarizations promote transmural reentry in ischemic human ventricles with reduced repolarization reserve.

Many brain morphometry studies have been performed in order to characterize the brain atrophy pattern of Alzheimers disease (AD). The earliest studies focused on the volume of particular brain structures, such as hippocampus and entorhinal cortex. Even though volumetry is a powerful, robust and intuitive technique that has yielded a wealth of findings, more complex shape descriptors have been used to perform statistical shape analysis of particular brain structures. However, in shape analysis studies of brain structures the information of the relative pose between neighbor structures is typically disregarded. This work presents a framework to analyse pose information including the following approaches: similarity transformations with either pseudo-Riemannian or left-invariant Riemannian metric, and centered transformations with a bi-invariant Riemannian metric. As an illustration, an analysis of covariance (ANCOVA) and a discrimination analysis were performed on Alzheimers Disease Neuroimaging Initiative (ADNI) data.

Transferring the Rig and Animations from a Character to Different Face Models

Tensor-based morphometry (TBM) is an analysis technique where anatomical information is characterized by means of the spatial transformations mapping a customized template with the observed images. Therefore, accurate inter-subject non-rigid registration is an essential prerequisite for both template estimation and image warping. Subsequent statistical analysis on the spatial transformations is performed to highlight voxel-wise differences. Most of previous TBM studies did not explore the influence of the registration parameters, such as the parameters defining the deformation and the regularization models. In this work performance evaluation of TBM using stationary velocity field (SVF) diffeomorphic registration was performed in a subset of subjects from Alzheimers Disease Neuroimaging Initiative (ADNI) study. A wide range of values of the registration parameters that define the transformation smoothness and the balance between image matching and regularization were explored in the evaluation. The proposed methodology provided brain atrophy maps with very detailed anatomical resolution and with a high significance level compared with results recently published on the same data set using a non-linear elastic registration method.

Statistical analysis of relative pose information of subcortical nuclei: Application on ADNI data

In computational anatomy variability among medical images is encoded by a large deformation diffeomorphic mapping matching each instance with a template. The set of diffeomorphisms is usually endowed with a Riemannian manifold structure and parameterized by non-stationary velocity vector fields. An alternative parameterization based on stationary vector fields has been proposed, where paths of diffeomorphisms are the one-parameter subgroups, identified with the group exponential map. A log-Euclidean framework was proposed to compute statistics on finite dimensional Lie groups and later extended to diffeomorphisms. A fast algorithm based on the scaling and squaring (SS) method for the matrix exponential was applied to compute the exponential of diffeomorphisms. In this work we evaluate the performance of different approaches to compute the exponential in terms of accuracy and computational time. These approaches include forward Euler method, Taylor expansion, iterative composition, SS method, and a combination of interpolation and SS. In our results the SS method obtained the best performance trade-off, as it is accurate, fast and robust, but it has an intrinsic lower bound in accuracy. This lower bound can be partially overcome by oversampling the grid, at the expense of increased memory and time requirements. The Taylor expansion provided a fast alternative when spatial frequencies are small, and particularly for low ambient dimensions, but its convergence is not guaranteed in general.

Tensor-based morphometry with stationary velocity field diffeomorphic registration: Application to ADNI

Tensor-based morphometry (TBM) studies encode the anatomical information in spatial deformations which are locally characterized by Jacobian matrices. Current methods perform voxel-wise statistical analysis on some features, such as the Jacobian determinant or the Cauchy–Green deformation tensor, which are not complete descriptors of the local deformation. This article introduces a right-invariant Riemannian distance on the GL+(n) group of Jacobian matrices making use of the complete geometrical information of the local deformation. A numerical recipe for the computation of the proposed distance is given. Additionally, experiments are performed on both a synthetic deformation study and a cross-sectional brain MRI study.

Algorithms for computing the group exponential of diffeomorphisms: Performance evaluation

Spatial transformations are mappings between locations of a

Multivariate Tensor-Based Morphometry with a Right-Invariant Riemannian Distance on GL+(n)

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