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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Providing traceability for neuroimaging analyses.

Richard McClatchey1, Andrew Branson, Ashiq Anjum

  • 1Centre for Complex Cooperative Systems, CEMS Faculty, University of the West of England, Coldharbour Lane, Frenchay, Bristol BS16 1QY, United Kingdom. richard.mcclatchey@cern.ch

International Journal of Medical Informatics
|June 15, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new Provenance Service to manage neuroimaging data for Alzheimer's disease research. The service enhances data traceability and workflow reconstruction, enabling collaborative medical research.

Keywords:
Analysis trackingGrid computingNeuroimagingProvenance dataService-oriented architectures

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Area of Science:

  • Neuroscience
  • Medical Informatics
  • Biomedical Research

Background:

  • Increasingly digital biomedical data necessitates accurate information capture, traceability, and accessibility for researchers.
  • Grid- and Cloud-based technologies, particularly Service Oriented Architectures (SOA), offer solutions for managing distributed data and algorithms in biomedicine.
  • Neuroscientific image analysis requires robust traceability of processes and datasets for collaborative study, a capability often lacking.

Purpose of the Study:

  • To address the need for deployed medical Grids that provide essential research data traceability for complex analyses.
  • To outline the findings of a requirements study and a resulting system architecture for services supporting neuroscientific studies of Alzheimer's disease biomarkers.
  • To develop and evaluate data management and provenance services for 21st-century medical research.

Main Methods:

  • Developed a software infrastructure and services based on a Service Oriented Architecture (SOA).
  • Integrated the CRISTAL software for provenance management within the SOA.
  • Deployed the system to manage neuroimaging projects focused on Alzheimer's disease biomarkers.

Main Results:

  • A Provenance Service was delivered within the neuGRID and N4U projects.
  • The service captures and reconstructs workflow information essential for neuroimaging analyses.
  • The system enables tracking of workflow and dataset evolution, as well as analysis outcomes.

Conclusions:

  • The Provenance Service provides essential traceability throughout the lifecycle of neuroimaging studies.
  • The generic design allows application across the broader medical domain as a reusable tool.
  • Enables distributed collaborative medical analysis programs for research communities.