7,051 research outputs found
DIRAC framework evaluation for the -LAT and CTA experiments
DIRAC (Distributed Infrastructure with Remote Agent Control) is a general
framework for the management of tasks over distributed heterogeneous computing
environments. It has been originally developed to support the production
activities of the LHCb (Large Hadron Collider Beauty) experiment and today is
extensively used by several particle physics and biology communities. Current
( Large Area Telescope -- LAT) and planned (Cherenkov Telescope Array --
CTA) new generation astrophysical/cosmological experiments, with very large
processing and storage needs, are currently investigating the usability of
DIRAC in this context. Each of these use cases has some peculiarities:
-LAT will interface DIRAC to its own workflow system to allow the access
to the grid resources, while CTA is using DIRAC as workflow management system
for Monte Carlo production and analysis on the grid. We describe the prototype
effort that we lead toward deploying a DIRAC solution for some aspects of
-LAT and CTA needs.Comment: proceedings to CHEP 2013 conference : http://www.chep2013.org
A How-To for the Mock LISA Data Challenges
The LISA International Science Team Working Group on Data Analysis
(LIST-WG1B) is sponsoring several rounds of mock data challenges, with the
purpose of fostering development of LISA data-analysis capabilities, and of
demonstrating technical readiness for the maximum science exploitation of the
LISA data. The first round of challenge data sets were released at this
Symposium. We describe the models and conventions (for LISA and for
gravitational-wave sources) used to prepare the data sets, the file format used
to encode them, and the tools and resources available to support challenge
participants.Comment: 10 pages, 1 figure, in Proceedings of the Sixth International LISA
Symposium (AIP, 2006
Brain networks under attack : robustness properties and the impact of lesions
A growing number of studies approach the brain as a complex network, the so-called âconnectomeâ. Adopting this framework, we examine what types or extent of damage the brain can withstandâreferred to as network ârobustnessââand conversely, which kind of distortions can be expected after brain lesions. To this end, we review computational lesion studies and empirical studies investigating network alterations in brain tumour, stroke and traumatic brain injury patients. Common to these three types of focal injury is that there is no unequivocal relationship between the anatomical lesion site and its topological characteristics within the brain network. Furthermore, large-scale network effects of these focal lesions are compared to those of a widely studied multifocal neurodegenerative disorder, Alzheimerâs disease, in which central parts of the connectome are preferentially affected. Results indicate that human brain networks are remarkably resilient to different types of lesions, compared to other types of complex networks such as random or scale-free networks. However, lesion effects have been found to depend critically on the topological position of the lesion. In particular, damage to network hub regionsâand especially those connecting different subnetworksâwas found to cause the largest disturbances in network organization. Regardless of lesion location, evidence from empirical and computational lesion studies shows that lesions cause significant alterations in global network topology. The direction of these changes though remains to be elucidated. Encouragingly, both empirical and modelling studies have indicated that after focal damage, the connectome carries the potential to recover at least to some extent, with normalization of graph metrics being related to improved behavioural and cognitive functioning. To conclude, we highlight possible clinical implications of these findings, point out several methodological limitations that pertain to the study of brain diseases adopting a network approach, and provide suggestions for future research
The SkyMapper Transient Survey
The SkyMapper 1.3 m telescope at Siding Spring Observatory has now begun
regular operations. Alongside the Southern Sky Survey, a comprehensive digital
survey of the entire southern sky, SkyMapper will carry out a search for
supernovae and other transients. The search strategy, covering a total
footprint area of ~2000 deg2 with a cadence of days, is optimised for
discovery and follow-up of low-redshift type Ia supernovae to constrain cosmic
expansion and peculiar velocities. We describe the search operations and
infrastructure, including a parallelised software pipeline to discover variable
objects in difference imaging; simulations of the performance of the survey
over its lifetime; public access to discovered transients; and some first
results from the Science Verification data.Comment: 13 pages, 11 figures; submitted to PAS
Towards Individualized Transcranial Electric Stimulation Therapy through Computer Simulation
Transkranielle Elektrostimulation (tES) beschreibt eine Gruppe von Hirnstimulationstechniken, die einen schwachen elektrischen Strom ĂŒber zwei nicht-invasiv am Kopf angebrachten Elektroden applizieren. Handelt es sich dabei um einen Gleichstrom, spricht man von transkranieller Gleichstromstimulation, auch tDCS abgekĂŒrzt. Die allgemeine Zielstellung aller Hirnstimulationstechniken ist Hirnfunktion durch ein VerstĂ€rken oder DĂ€mpfen von HirnaktivitĂ€t zu beeinflussen. Unter den Stimulationstechniken wird die transkranielle Gleichstromstimulation als ein adjuvantes Werkzeug zur UnterstĂŒtzung der mikroskopischen Reorganisation des Gehirnes in Folge von Lernprozessen und besonders der Rehabilitationstherapie nach einem Schlaganfall untersucht. Aktuelle Herausforderungen dieser Forschung sind eine hohe VariabilitĂ€t im erreichten Stimulationseffekt zwischen den Probanden sowie ein unvollstĂ€ndiges VerstĂ€ndnis des Zusammenspiels der der Stimulation zugrundeliegenden Mechanismen. Als SchlĂŒsselkomponente fĂŒr das VerstĂ€ndnis der Stimulationsmechanismen wird das zwischen den Elektroden im Kopf des Probanden aufgebaute elektrische Feld erachtet. Einem grundlegenden Konzept folgend wird angenommen, dass Hirnareale, die einer gröĂeren elektrischen FeldstĂ€rke ausgesetzt sind, ebenso einen höheren Stimulationseffekt erfahren. Damit kommt der Positionierung der Elektroden eine entscheidende Rolle fĂŒr die Stimulation zu. Allerdings verteilt sich das elektrische Feld wegen des heterogenen elektrischen LeitfĂ€higkeitsprofil des menschlichen Kopfes nicht uniform im Gehirn der Probanden. AuĂerdem ist das Verteilungsmuster auf Grund anatomischer Unterschiede zwischen den Probanden verschieden. Die triviale AbschĂ€tzung der Ausbreitung des elektrischen Feldes anhand der bloĂen Position der Stimulationselektroden ist daher nicht ausreichend genau fĂŒr eine zielgerichtete Stimulation.
Computerbasierte, biophysikalische Simulationen der transkraniellen Elektrostimulation ermöglichen die individuelle Approximation des Verteilungsmusters des elektrischen Feldes in Probanden basierend auf deren medizinischen Bildgebungsdaten. Sie werden daher zunehmend verwendet, um tDCS-Anwendungen zu planen und verifizieren, und stellen ein wesentliches Hilfswerkzeug auf dem Weg zu individualisierter Schlaganfall-Rehabilitationstherapie dar. Softwaresysteme, die den dahinterstehenden individualisierten Verarbeitungsprozess erleichtern und fĂŒr ein breites Feld an Forschern zugĂ€nglich machen, wurden in den vergangenen Jahren fĂŒr den Anwendungsfall in gesunden Erwachsenen entwickelt. Jedoch bleibt die Simulation von Patienten mit krankhaftem Hirngewebe und strukturzerstörenden LĂ€sionen eine nicht-triviale Aufgabe.
Daher befasst sich das hier vorgestellte Projekt mit dem Aufbau und der praktischen Anwendung eines Arbeitsablaufes zur Simulation transkranieller Elektrostimulation. Dabei stand die Anforderung im Vordergrund medizinische Bildgebungsdaten insbesondere neurologischer Patienten mit krankhaft verĂ€ndertem Hirngewebe verarbeiten zu können. Der grundlegende Arbeitsablauf zur Simulation wurde zunĂ€chst fĂŒr gesunde Erwachsene entworfen und validiert. Dies umfasste die Zusammenstellung medizinischer Bildverarbeitungsalgorithmen zu einer umfangreichen Verarbeitungskette, um elektrisch relevante Strukturen in den Magnetresonanztomographiebildern des Kopfes und des Oberkörpers der Probanden zu identifizieren und zu extrahieren. Die identifizierten Strukturen mussten in Computermodelle ĂŒberfĂŒhrt werden und das zugrundeliegende, physikalische Problem der elektrischen Volumenleitung in biologischen Geweben mit Hilfe numerischer Simulation gelöst werden.
Im Verlauf des normalen Alterns ist das Gehirn strukturellen VerĂ€nderungen unterworfen, unter denen ein Verlust des Hirnvolumens sowie die Ausbildung mikroskopischer VerĂ€nderungen seiner Nervenfaserstruktur die Bedeutendsten sind. In einem zweiten Schritt wurde der Arbeitsablauf daher erweitert, um diese PhĂ€nomene des normalen Alterns zu berĂŒcksichtigen. Die vordergrĂŒndige Herausforderung in diesem Teilprojekt war die biophysikalische Modellierung der verĂ€nderten Hirnmikrostruktur, da die resultierenden VerĂ€nderungen im LeitfĂ€higkeitsprofil des Gehirns bisher noch nicht in der Literatur quantifiziert wurden. Die Erweiterung des Simulationsablauf zeichnete sich vorrangig dadurch aus, dass mit unsicheren elektrischen LeitfĂ€higkeitswerten gearbeitet werden konnte. Damit war es möglich den Einfluss der ungenau bestimmbaren elektrischen LeitfĂ€higkeit der verschiedenen biologischen Strukturen des menschlichen Kopfes auf das elektrische Feld zu ermitteln. In einer Simulationsstudie, in der Bilddaten von 88 Probanden einflossen, wurde die Auswirkung der verĂ€nderten Hirnfaserstruktur auf das elektrische Feld dann systematisch untersucht. Es wurde festgestellt, dass sich diese GewebsverĂ€nderungen hochgradig lokal und im Allgemeinen gering auswirken.
SchlieĂlich wurden in einem dritten Schritt Simulationen fĂŒr Schlaganfallpatienten durchgefĂŒhrt. Ihre groĂen, strukturzerstörenden LĂ€sionen wurden dabei mit einem höheren Detailgrad als in bisherigen Arbeiten modelliert und physikalisch abermals mit unsicheren LeitfĂ€higkeiten gearbeitet, was zu unsicheren elektrischen FeldabschĂ€tzungen fĂŒhrte. Es wurden individuell berechnete elektrische Felddaten mit der Hirnaktivierung von 18 Patienten in Verbindung gesetzt, unter BerĂŒcksichtigung der inhĂ€renten Unsicherheit in der Bestimmung der elektrischen Felder. Das Ziel war zu ergrĂŒnden, ob die Hirnstimulation einen positiven Einfluss auf die HirnaktivitĂ€t der Patienten im Kontext von Rehabilitationstherapie ausĂŒben und so die Neuorganisierung des Gehirns nach einem Schlaganfall unterstĂŒtzen kann. WĂ€hrend ein schwacher Zusammenhang hergestellt werden konnte, sind weitere Untersuchungen nötig, um diese Frage abschlieĂend zu klĂ€ren.:Kurzfassung
Abstract
Contents
1 Overview
2 Anatomical structures in magnetic resonance images
2 Anatomical structures in magnetic resonance images
2.1 Neuroanatomy
2.2 Magnetic resonance imaging
2.3 Segmentation of MR images
2.4 Image morphology
2.5 Summary
3 Magnetic resonance image processing pipeline
3.1 Introduction to human body modeling
3.2 Description of the processing pipeline
3.3 Intermediate and final outcomes in two subjects
3.4 Discussion, limitations & future work
3.5 Conclusion
4 Numerical simulation of transcranial electric
stimulation
4.1 Electrostatic foundations
4.2 Discretization of electrostatic quantities
4.3 The numeric solution process
4.4 Spatial discretization by volume meshing
4.5 Summary
5 Simulation workflow
5.1 Overview of tES simulation pipelines
5.2 My implementation of a tES simulation workflow
5.3 Verification & application examples
5.4 Discussion & Conclusion
6 Transcranial direct current stimulation in the aging brain
6.1 Handling age-related brain changes in tES simulations
6.2 Procedure of the simulation study
6.3 Results of the uncertainty analysis
6.4 Findings, limitations and discussion
7 Transcranial direct current stimulation in stroke patients
7.1 Bridging the gap between simulated electric fields and brain activation in
stroke patients
7.2 Methodology for relating simulated electric fields to functional MRI data
7.3 Evaluation of the simulation study and correlation analysis
7.4 Discussion & Conclusion
8 Outlooks for simulations of transcranial electric stimulation
List of Figures
List of Tables
Glossary of Neuroscience Terms
Glossary of Technical Terms
BibliographyTranscranial electric current stimulation (tES) denotes a group of brain stimulation techniques that apply a weak electric current over two or more non-invasively, head-mounted electrodes. When employing a direct-current, this method is denoted transcranial direct current stimulation (tDCS). The general aim of all tES techniques is the modulation of brain function by an up- or downregulation of brain activity. Among these, transcranial direct current stimulation is investigated as an adjuvant tool to promote processes of the microscopic reorganization of the brain as a consequence of learning and, more specifically, rehabilitation therapy after a stroke. Current challenges of this research are a high variability in the achieved stimulation effects across subjects and an incomplete understanding of the interplay between its underlying mechanisms. A key component to understanding the stimulation mechanism is considered the electric field, which is exerted by the electrodes and distributes in the subjects' heads. A principle concept assumes that brain areas exposed to a higher electric field strength likewise experience a higher stimulation. This attributes the positioning of the electrodes a decisive role for the stimulation. However, the electric field distributes non-uniformly across subjects' brains due to the heterogeneous electrical conductivity profile of the human head. Moreover, the distribution pattern is variable between subjects due to their individual anatomy. A trivial estimation of the distribution of the electric field solely based on the position of the stimulating electrodes is, therefore, not precise enough for a well-targeted stimulation.
Computer-based biophysical simulations of transcranial electric stimulation enable the individual approximation of the distribution pattern of the electric field in subjects based on their medical imaging data. They are, thus, increasingly employed for the planning and verification of tDCS applications and constitute an essential tool on the way to individualized stroke rehabilitation therapy. Software pipelines facilitating the underlying individualized processing for a wide range of researchers have been developed for use in healthy adults over the past years, but, to date, the simulation of patients with abnormal brain tissue and structure disrupting lesions remains a non-trivial task.
Therefore, the presented project was dedicated to establishing and practically applying a tES simulation workflow. The processing of medical imaging data of neurological patients with abnormal brain tissue was a central requirement in this process. The basic simulation workflow was first designed and validated for the simulation of healthy adults. This comprised compiling medical image processing algorithms into a comprehensive workflow to identify and extract electrically relevant physiological structures of the human head and upper torso from magnetic resonance images. The identified structures had to be converted to computational models. The underlying physical problem of electric volume conduction in biological tissue was solved by means of numeric simulation.
Over the course of normal aging, the brain is subjected to structural alterations, among which a loss of brain volume and the development of microscopic alterations of its fiber structure are the most relevant. In a second step, the workflow was, thus, extended to incorporate these phenomena of normal aging. The main challenge in this subproject was the biophysical modeling of the altered brain microstructure as the resulting alterations to the conductivity profile of the brain were so far not quantified in the literature. Therefore, the augmentation of the workflow most notably included the modeling of uncertain electrical properties. With this, the influence of the uncertain electrical conductivity of the biological structures of the human head on the electric field could be assessed. In a simulation study, including imaging data of 88 subjects, the influence of the altered brain fiber structure on the electric field was then systematically investigated. These tissue alterations were found to exhibit a highly localized and generally low impact.
Finally, in a third step, tDCS simulations of stroke patients were conducted. Their large, structure-disrupting lesions were modeled in a more detailed manner than in previous stroke simulation studies, and they were physically, again, modeled by uncertain electrical conductivity resulting in uncertain electric field estimates. Individually simulated electric fields were related to the brain activation of 18 patients, considering the inherently uncertain electric field estimations. The goal was to clarify whether the stimulation exerts a positive influence on brain function in the context of rehabilitation therapy supporting brain reorganization following a stroke. While a weak correlation could be established, further investigation will be necessary to answer that research question.:Kurzfassung
Abstract
Contents
1 Overview
2 Anatomical structures in magnetic resonance images
2 Anatomical structures in magnetic resonance images
2.1 Neuroanatomy
2.2 Magnetic resonance imaging
2.3 Segmentation of MR images
2.4 Image morphology
2.5 Summary
3 Magnetic resonance image processing pipeline
3.1 Introduction to human body modeling
3.2 Description of the processing pipeline
3.3 Intermediate and final outcomes in two subjects
3.4 Discussion, limitations & future work
3.5 Conclusion
4 Numerical simulation of transcranial electric
stimulation
4.1 Electrostatic foundations
4.2 Discretization of electrostatic quantities
4.3 The numeric solution process
4.4 Spatial discretization by volume meshing
4.5 Summary
5 Simulation workflow
5.1 Overview of tES simulation pipelines
5.2 My implementation of a tES simulation workflow
5.3 Verification & application examples
5.4 Discussion & Conclusion
6 Transcranial direct current stimulation in the aging brain
6.1 Handling age-related brain changes in tES simulations
6.2 Procedure of the simulation study
6.3 Results of the uncertainty analysis
6.4 Findings, limitations and discussion
7 Transcranial direct current stimulation in stroke patients
7.1 Bridging the gap between simulated electric fields and brain activation in
stroke patients
7.2 Methodology for relating simulated electric fields to functional MRI data
7.3 Evaluation of the simulation study and correlation analysis
7.4 Discussion & Conclusion
8 Outlooks for simulations of transcranial electric stimulation
List of Figures
List of Tables
Glossary of Neuroscience Terms
Glossary of Technical Terms
Bibliograph
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