Elisenda Eixarch

Bio: Elisenda Eixarch is an academic researcher from University of Barcelona. The author has contributed to research in topics: Gestational age & Medicine. The author has an hindex of 32, co-authored 152 publications receiving 4297 citations. Previous affiliations of Elisenda Eixarch include Imperial College London & Hospital Sant Joan de Déu Barcelona.

Fetal neurosonography at 31–35 weeks reveals altered cortical development in pre‐eclampsia with and without small‐for‐gestational‐age fetus

Abstract: To explore the pattern of fetal cortical development in pregnancies complicated by pre‐eclampsia (PE), with and without a small‐for‐gestational‐age (SGA) fetus, compared to uncomplicated pregnancies.

Structural Brain Network Reorganization and Social Cognition Related to Adverse Perinatal Condition from Infancy to Early Adolescence (vol 10, 560, 2016)

Abstract: Keywords: connectome ; intrauterine growth retardation ; birth weight ; executive function ; neurodevelopment ; preterm infants Reference EPFL-ARTICLE-230802doi:10.3389/fnins.2017.00257View record in Web of Science Record created on 2017-09-05, modified on 2017-09-05

Fetal MRI Synthesis via Balanced Auto-Encoder Based Generative Adversarial Networks

Abstract: Machine learning approaches for image analysis require large amounts of training imaging data. As an alternative, the use of realistic synthetic data reduces the high cost associated to medical image acquisition, as well as avoiding confidentiality and privacy issues, and consequently allows the creation of public data repositories for scientific purposes. Within the context of fetal imaging, we adopt an auto-encoder based Generative Adversarial Network for synthetic fetal MRI generation. The proposed architecture features a balanced power of the discriminator against the generator during training, provides an approximate convergence measure, and enables fast and robust training to generate high-quality fetal MRI in axial, sagittal and coronal planes. We demonstrate the feasibility of the proposed approach quantitatively and qualitatively by segmenting relevant fetal structures to assess the anatomical fidelity of the simulation, and performing a clinical verisimilitude study distinguishing the simulated data from the real images. The results obtained so far are promising, which makes further investigation on this new topic worthwhile.

BrainNet Viewer: a network visualization tool for human brain connectomics.

Abstract: The human brain is a complex system whose topological organization can be represented using connectomics. Recent studies have shown that human connectomes can be constructed using various neuroimaging technologies and further characterized using sophisticated analytic strategies, such as graph theory. These methods reveal the intriguing topological architectures of human brain networks in healthy populations and explore the changes throughout normal development and aging and under various pathological conditions. However, given the huge complexity of this methodology, toolboxes for graph-based network visualization are still lacking. Here, using MATLAB with a graphical user interface (GUI), we developed a graph-theoretical network visualization toolbox, called BrainNet Viewer, to illustrate human connectomes as ball-and-stick models. Within this toolbox, several combinations of defined files with connectome information can be loaded to display different combinations of brain surface, nodes and edges. In addition, display properties, such as the color and size of network elements or the layout of the figure, can be adjusted within a comprehensive but easy-to-use settings panel. Moreover, BrainNet Viewer draws the brain surface, nodes and edges in sequence and displays brain networks in multiple views, as required by the user. The figure can be manipulated with certain interaction functions to display more detailed information. Furthermore, the figures can be exported as commonly used image file formats or demonstration video for further use. BrainNet Viewer helps researchers to visualize brain networks in an easy, flexible and quick manner, and this software is freely available on the NITRC website (www.nitrc.org/projects/bnv/).

A systematic review and meta-analysis to revise the Fenton growth chart for preterm infants

Abstract: The aim of this study was to revise the 2003 Fenton Preterm Growth Chart, specifically to: a) harmonize the preterm growth chart with the new World Health Organization (WHO) Growth Standard, b) smooth the data between the preterm and WHO estimates, informed by the Preterm Multicentre Growth (PreM Growth) study while maintaining data integrity from 22 to 36 and at 50 weeks, and to c) re-scale the chart x-axis to actual age (rather than completed weeks) to support growth monitoring. Systematic review, meta-analysis, and growth chart development. We systematically searched published and unpublished literature to find population-based preterm size at birth measurement (weight, length, and/or head circumference) references, from developed countries with: Corrected gestational ages through infant assessment and/or statistical correction; Data percentiles as low as 24 weeks gestational age or lower; Sample with greater than 500 infants less than 30 weeks. Growth curves for males and females were produced using cubic splines to 50 weeks post menstrual age. LMS parameters (skew, median, and standard deviation) were calculated. Six large population-based surveys of size at preterm birth representing 3,986,456 births (34,639 births < 30 weeks) from countries Germany, United States, Italy, Australia, Scotland, and Canada were combined in meta-analyses. Smooth growth chart curves were developed, while ensuring close agreement with the data between 24 and 36 weeks and at 50 weeks. The revised sex-specific actual-age growth charts are based on the recommended growth goal for preterm infants, the fetus, followed by the term infant. These preterm growth charts, with the disjunction between these datasets smoothing informed by the international PreM Growth study, may support an improved transition of preterm infant growth monitoring to the WHO growth charts.

Consensus definition of fetal growth restriction: a Delphi procedure

Abstract: Objective To determine, by expert consensus, a definition for early and late fetal growth restriction (FGR) through a Delphi procedure. Method A Delphi survey was conducted among an international panel of experts on FGR. Panel members were provided with 18 literature-based parameters for defining FGR and were asked to rate the importance of these parameters for the diagnosis of both early and late FGR on a 5-point Likert scale. Parameters were described as solitary parameters (parameters that are sufficient to diagnose FGR, even if all other parameters are normal) and contributory parameters (parameters that require other abnormal parameter(s) to be present for the diagnosis of FGR). Consensus was sought to determine the cut-off values for accepted parameters. Results A total of 106 experts were approached, of whom 56 agreed to participate and entered the first round, and 45 (80%) completed all four rounds. For early FGR ( 95th centile in either the UA or uterine artery) were agreed upon. For late FGR (≥ 32 weeks), two solitary parameters (AC or EFW two quartiles on growth charts and cerebroplacental ratio 95th centile) were defined. Conclusion Consensus-based definitions for early and late FGR, as well as cut-off values for parameters involved, were agreed upon by a panel of experts. Copyright © 2016 ISUOG. Published by John Wiley & Sons Ltd.