Biomedical Informatics Research Network (BIRN) Data Repository

The primary goal of this project was to streamline the submission process for researchers to categorize, approve, curate, and publish these data sets. By supporting the sharing and exchange of these data types in fulfillment of the National Institute of Health guidelines the BIRN Data Repository will have the ability to grow as the need develops for these researchers to easily share information

The INCF Digital Atlasing Program: Report on Digital Atlasing Standards in the Rodent Brain

The goal of the INCF Digital Atlasing Program is to provide the vision and direction necessary to make the rapidly growing collection of multidimensional data of the rodent brain (images, gene expression, etc.) widely accessible and usable to the international research community. This Digital Brain Atlasing Standards Task Force was formed in May 2008 to investigate the state of rodent brain digital atlasing, and formulate standards, guidelines, and policy recommendations.

Our first objective has been the preparation of a detailed document that includes the vision and specific description of an infrastructure, systems and methods capable of serving the scientific goals of the community, as well as practical issues for achieving
the goals. This report builds on the 1st INCF Workshop on Mouse and Rat Brain Digital Atlasing Systems (Boline et al., 2007, _Nature Preceedings_, doi:10.1038/npre.2007.1046.1) and includes a more detailed analysis of both the current state and desired state of digital atlasing along with specific recommendations for achieving these goals

A web portal for Portuguese brain imaging network

Mestrado em Engenharia de Computadores e TelemáticaA Imagiologia Cerebral (IC) está na fronteira entre a neurologia, engenharia e física. écnicas de imagens médicas multimodais, tais como a Ressonância Magnética (MRI e fMRI) e Espectroscopia (MRS), Tomografia Computadorizada por Emissão de Fotões/Positrões (SPECT/PET), entre outros, são emergentes ferramentas de pesquisa médica que pode fornecer informações valiosas para o diagnóstico de doenças do cérebro. Eletroencefalograma de alta resolução (HR-EEG), técnicas para sincronizar e fundir seus resultados de análise e várias técnicas de imagem são também parte de IC. Em Portugal, dado o facto que a maioria das áreas relacionadas com IC (por exemplo, medicina, engenharia ou física) são assuntos de investigação em muitos grupos de P&D, um consórcio de universidades de Aveiro, Coimbra, Minho e Porto criou a Rede Nacional de Imagiologia Funcional Cerebral (RNIFC). A RNIFC é uma associação sem fins lucrativos que foi formalizada e assinada em fevereiro de 2009. Actualmente, com o suporte de sistemas digitais para armazenar imagens médicas, é possível partilhar dados entre essas instituições para melhorar o diagnóstico, e permitir investigações entre a comunidade médica de diferentes instituições. O principal objectivo desta dissertação é descrever a implementação dos serviços de sistemas de informação essenciais para a Brain Imaging Network (BIN) que suportam actualmente o RNIFC acessível através do Portal BIN, o principal ponto de entrada para a BING. O Portal BIN permite aos pesquisadores na comunidade BING espalhadas pelo país e no estrangeiro, quer para solicitar o acesso a instrumentos científicos ou para recuperar os seus casos e executar as suas análises. ABSTRACT: Brain Imaging is in the frontier between neurology, engineering and physics. Multimodal medical imaging techniques, such as Magnetic Resonance Imaging (MRI and fMRI) and Spectroscopy (MRS), Single Photon/Positron Emitting Tomography (SPECT/PET) among others, are emergent medical research tools that can provide valuable information for diagnosis of brain diseases. High-resolution electroencephalogram (HR-EEG), techniques for synchronizing and fuse its analysis results and several imaging techniques are also part of BI. In Portugal, given fact that most of the BI related areas (e.g. medical, engineering or physics) are subjects of research in many R&D groups, a consortium of the universities of Aveiro, Coimbra, Minho and Porto created the National Functional Brain Imaging Network (RNIFC). The RNIFC is a non-profitable association that was formalized and signed in February 2009. Currently, with the support of digital systems to store medical images, it is possible to share data among these institutions to improve diagnosis, and allow investigations by the medical community among different institutions. The main objective of this thesis is to describe the implementation of the essential Brain Imaging Network (BIN) information systems services that currently support the RNIFC accessible through the BIN Portal, the main entry point for the BING. BIN Portal enables researchers in the BING community scattered along the country and abroad either to apply for access to the scientific instruments or to retrieve their cases and run their analysis

Experiences of Engineering Grid-Based Medical Software

Objectives: Grid-based technologies are emerging as potential solutions for managing and collaborating distributed resources in the biomedical domain. Few examples exist, however, of successful implementations of Grid-enabled medical systems and even fewer have been deployed for evaluation in practice. The objective of this paper is to evaluate the use in clinical practice of a Grid-based imaging prototype and to establish directions for engineering future medical Grid developments and their subsequent deployment. Method: The MammoGrid project has deployed a prototype system for clinicians using the Grid as its information infrastructure. To assist in the specification of the system requirements (and for the first time in healthgrid applications), use-case modelling has been carried out in close collaboration with clinicians and radiologists who had no prior experience of this modelling technique. A critical qualitative and, where possible, quantitative analysis of the MammoGrid prototype is presented leading to a set of recommendations from the delivery of the first deployed Grid-based medical imaging application. Results: We report critically on the application of software engineering techniques in the specification and implementation of the MammoGrid project and show that use-case modelling is a suitable vehicle for representing medical requirements and for communicating effectively with the clinical community. This paper also discusses the practical advantages and limitations of applying the Grid to real-life clinical applications and presents the consequent lessons learned.Comment: 18 pages, 2 tables, 5 figures. In press International Journal of Medical Informatics. Elsevier publisher

Science 2.0 : sharing scientific data on the Grid

Mestrado em Engenharia de Computadores e TelemáticaA computação assumese cada vez mais como um recurso essencial em ciência, levando ao surgimento do termo eCiência para designar a utilização de tecnologias de computação avançada para suportar a realização de experiências científicas, a preservação e partilha do conhecimento. Uma das áreas de aplicação do conceito de eCiência é o tratamento e análise de imagens médicas. Os processos que lidam com imagem médica, tanto ao nível clínico como de investigação, são exigentes em relação ao suporte computacional, devido aos algoritmos de processamento de imagem que requerem e à elevada capacidade de armazenamento relacionada com volume das imagens geradas. As políticas públicas e os avanços tecnológicos recentes orientados para a eCiência, têm vindo a apoiar o desenvolvimento da computação em Grid, tanto a nível dos middlewares como da instalação de capacidade de produção, como um sistema de computação avançado que permite a partilha de recursos, instrumentos científicos e boas práticas em comunidades virtuais. Este trabalho tem como objectivo desenvolver uma estratégia e um protótipo para o armazenamento de dados médicos na Grid, visando a sua utilização em investigação. Uma preocupação diferenciadora prendese com o objectivo de colocar as potencialidades da Grid ao serviço de utilizadores não técnicos (e.g. médicos, investigadores), que acedem a serviços de processamento e de armazenamento e catalogação de dados de forma transparente, através de um portal Web. O protótipo desenvolvido permite a investigadores na área das neurociências, sem conhecimentos específicos da tecnologia Grid, armazenar imagens e analisálas em Grids de produção existentes, baseadas no middleware gLite. ABSTRACT: Computing has become an essential tool in modern science, leading to the appearance of the term eScience to designate the usage of advanced computing technologies to support the execution of scientific experiments, and the preservation and sharing of knowledge. One of eScience domain areas is the medical imaging analysis. The processes that deal with medical images, both at clinical and investigation level, are very demanding in terms of computational support, due to the analysis algorithms that involve large volumes of generated images, requiring high storage capabilities. The recent public policies and technological advances are eScience oriented, and have been supporting the development of Grid computing, both at the middleware level and at the installation of production capabilities in an advanced computing system, that allows the sharing of resources, scientific instrumentation and good practices among virtual communities. The main objective of this work is to develop a strategy and a prototype to allow the storage of medical data on the Grid, targeting a research environment. The differentiating concern of this work is the ability to provide the nonexperts users (e.g: doctors, researchers) access to the Grid services, like storage and processing, through a friendly Web interface. The developed prototype allows researchers in the field of neuroscience, without any specific knowledge of Grid technology, to store images and analyse them in production Grid infrastructures, based on the gLite middleware

Cross-domain neurobiology data integration and exploration

<p>Abstract</p> <p>Background</p> <p>Understanding the biomedical implications of data from high throughput experiments requires solutions for effective cross-scale and cross-domain data exploration. However, existing solutions do not provide sufficient support for linking molecular level data to neuroanatomical structures, which is critical for understanding high level neurobiological functions.</p> <p>Results</p> <p>Our work integrates molecular level data with high level biological functions and we present results using anatomical structure as a scaffold. Our solution also allows the sharing of intermediate data exploration results with other web applications, greatly increasing the power of cross-domain data exploration and mining.</p> <p>Conclusions</p> <p>The Flex-based PubAnatomy web application we developed enables highly interactive visual exploration of literature and experimental data for understanding the relationships between molecular level changes, pathways, brain circuits and pathophysiological processes. The prototype of PubAnatomy is freely accessible at:[<url>http://brainarray.mbni.med.umich.edu/Brainarray/prototype/PubAnatomy</url>]</p

Multi-voxel fMRI analysis using an high throughput grid framework

Mestrado em Engenharia Biomédica - Instrumentação, Sinal e ImagemO presente trabalho apresenta uma nova abordagem à análise de imagens de RMf do cérebro, especificamente a utilização de medidas associativas na análise de séries temporais de RMf. Este tipo específico de análise, computacionalmente intensivo, requer recursos que normalmente não se encontram disponíveis em ambientes clínicos. Redes Grid é um novo paradigma de computação distribuída de elevada performance que pode ser utilizado para potenciar a utilização deste tipo de análise, disponibilizando a capacidade de computação necessária. Implementouse um framework que permite a utilização de uma infraestrutura Grid para correr este tipo de análise de forma transparente, viabilizando a sua utilização em ambientes clínicos, onde o tempo é um factor crítico. ABSTRACT: This work, introduces a new approach to fMRI brain image analysis, namely multivoxel fMRI association analysis. The problem associated with this type of approach is that requires a large computing capacity that is not normally available at clinical sites. To enable this specific type of analysis we are required to use High Performance Computing paradigms. In this context we analysed the use of Grid computing and implemented a framework that allows running the multivoxel fMRI association analysis using a grid infrastructure resources. The use of this framework makes this type of analysis usable in clinical environments where time constraints can have a vital importance

A formal architecture-centric and model driven approach for the engineering of science gateways

From n-Tier client/server applications, to more complex academic Grids, or even the most recent and promising industrial Clouds, the last decade has witnessed significant developments in distributed computing. In spite of this conceptual heterogeneity, Service-Oriented Architecture (SOA) seems to have emerged as the common and underlying abstraction paradigm, even though different standards and technologies are applied across application domains. Suitable access to data and algorithms resident in SOAs via so-called ‘Science Gateways’ has thus become a pressing need in order to realize the benefits of distributed computing infrastructures.In an attempt to inform service-oriented systems design and developments in Grid-based biomedical research infrastructures, the applicant has consolidated work from three complementary experiences in European projects, which have developed and deployed large-scale production quality infrastructures and more recently Science Gateways to support research in breast cancer, pediatric diseases and neurodegenerative pathologies respectively. In analyzing the requirements from these biomedical applications the applicant was able to elaborate on commonly faced issues in Grid development and deployment, while proposing an adapted and extensible engineering framework. Grids implement a number of protocols, applications, standards and attempt to virtualize and harmonize accesses to them. Most Grid implementations therefore are instantiated as superposed software layers, often resulting in a low quality of services and quality of applications, thus making design and development increasingly complex, and rendering classical software engineering approaches unsuitable for Grid developments.The applicant proposes the application of a formal Model-Driven Engineering (MDE) approach to service-oriented developments, making it possible to define Grid-based architectures and Science Gateways that satisfy quality of service requirements, execution platform and distribution criteria at design time. An novel investigation is thus presented on the applicability of the resulting grid MDE (gMDE) to specific examples and conclusions are drawn on the benefits of this approach and its possible application to other areas, in particular that of Distributed Computing Infrastructures (DCI) interoperability, Science Gateways and Cloud architectures developments

The Neuroscience Information Framework: A Data and Knowledge Environment for Neuroscience

With support from the Institutes and Centers forming the NIH Blueprint for Neuroscience Research, we have designed and implemented a new initiative for integrating access to and use of Web-based neuroscience resources: the Neuroscience Information Framework. The Framework arises from the expressed need of the neuroscience community for neuroinformatic tools and resources to aid scientific inquiry, builds upon prior development of neuroinformatics by the Human Brain Project and others, and directly derives from the Society for Neuroscience’s Neuroscience Database Gateway. Partnered with the Society, its Neuroinformatics Committee, and volunteer consultant-collaborators, our multi-site consortium has developed: (1) a comprehensive, dynamic, inventory of Web-accessible neuroscience resources, (2) an extended and integrated terminology describing resources and contents, and (3) a framework accepting and aiding concept-based queries. Evolving instantiations of the Framework may be viewed at http://nif.nih.gov, http://neurogateway.org, and other sites as they come on line

Uma rede telemática para a prestação regional de cuidados de saúde

Doutoramento em Engenharia InformáticaAs tecnologias de informação e comunicação na área da saúde não são só um instrumento para a boa gestão de informação, mas antes um fator estratégico para uma prestação de cuidados mais eficiente e segura. As tecnologias de informação são um pilar para que os sistemas de saúde evoluam em direção a um modelo centrado no cidadão, no qual um conjunto abrangente de informação do doente deve estar automaticamente disponível para as equipas que lhe prestam cuidados, independentemente de onde foi gerada (local geográfico ou sistema). Este tipo de utilização segura e agregada da informação clínica é posta em causa pela fragmentação generalizada das implementações de sistemas de informação em saúde. Várias aproximações têm sido propostas para colmatar as limitações decorrentes das chamadas “ilhas de informação” na saúde, desde a centralização total (um sistema único), à utilização de redes descentralizadas de troca de mensagens clínicas. Neste trabalho, propomos a utilização de uma camada de unificação baseada em serviços, através da federação de fontes de informação heterogéneas. Este agregador de informação clínica fornece a base necessária para desenvolver aplicações com uma lógica regional, que demostrámos com a implementação de um sistema de registo de saúde eletrónico virtual. Ao contrário dos métodos baseados em mensagens clínicas ponto-a-ponto, populares na integração de sistemas em saúde, desenvolvemos um middleware segundo os padrões de arquitetura J2EE, no qual a informação federada é expressa como um modelo de objetos, acessível através de interfaces de programação. A arquitetura proposta foi instanciada na Rede Telemática de Saúde, uma plataforma instalada na região de Aveiro que liga oito instituições parceiras (dois hospitais e seis centros de saúde), cobrindo ~350.000 cidadãos, utilizada por ~350 profissionais registados e que permite acesso a mais de 19.000.000 de episódios. Para além da plataforma colaborativa regional para a saúde (RTSys), introduzimos uma segunda linha de investigação, procurando fazer a ponte entre as redes para a prestação de cuidados e as redes para a computação científica. Neste segundo cenário, propomos a utilização dos modelos de computação Grid para viabilizar a utilização e integração massiva de informação biomédica. A arquitetura proposta (não implementada) permite o acesso a infraestruturas de e-Ciência existentes para criar repositórios de informação clínica para aplicações em saúde.Modern health information technology is not just a supporting instrument to good information management but a strategic requirement to provide more efficient and safer health care. Health information technology is a cornerstone to build the future patient-centric health care systems in which a comprehensive set of patient data will be available to the relevant care teams, in spite of where (system or service point) it was generated. Such secure and efficient use of clinical data is challenged by the existing fragmentation of health information systems implementation. Several approaches have been proposed to address the limitations of the so called “information silos” in healthcare, ranging from full centralization (a single system) to full-decentralized clinical message exchange networks. In this work we advocate the use of a service-based unification layer, by federating distributed heterogeneous information sources. This clinical information hub provides the basis to build regional-level applications, which we have demonstrated by implementing a virtual Electronic Health Record system. Unlike the message-driven, point-to-point approaches popular in health care systems integration, we developed a middleware layer, using J2EE architectural patterns, in which the common information is represented as an object model, accessible through programming interfaces. The proposed architecture was instantiated in the Rede Telemática da Saúde network, a platform deployed in the region of Aveiro connecting eight partner institutions (two hospitals and six primary care units), covering ~ 350,000 citizens, indexing information on more than 19,000,000 episodes of care and used by ~350 registered professionals. In addition to the regional health information collaborative platform (RTSys), we introduce a second line of research towards bridging the care networks and the science networks. In the later scenario, we propose the use of Grid computing to enable the massive use and integration of biomedical information. The proposed architecture (not implemented) enables to access existing e-Science infrastructures to create clinical information repositories for health applications