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CIVILITY: Cloud based Interactive Visualization of Tractography Brain Connectome.

Danaële Puechmaille1, Martin Styner1, Juan C Prieto1

  • 1Neuro Imaging Reasearch and Analysis Laboratory, Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina, United States.

Proceedings of Spie--The International Society for Optical Engineering
|January 16, 2018
PubMed
Summary
This summary is machine-generated.

CIVILITY is a cloud-based tool for visualizing brain connectome data. It uses advanced pipelines and Hierarchical Edge Bundling for interactive analysis, offering a new resource for neuroimaging research.

Keywords:
Tractographyapplicationbrain connectomecloudconnectivitydiffusion tensor imagingopen source

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Area of Science:

  • Neuroimaging
  • Computational Neuroscience
  • Medical Imaging Analysis

Background:

  • Brain connectome analysis requires significant computational resources.
  • Existing visualization tools may lack advanced interactive capabilities for complex datasets.

Purpose of the Study:

  • To introduce Cloud based Interactive Visualization of Tractography Brain Connectome (CIVILITY), a novel cloud-based platform.
  • To provide an accessible and state-of-the-art tool for brain connectome visualization and analysis.

Main Methods:

  • Utilizes a cloud-based architecture to submit tractography tasks to remote computing grids.
  • Employs the CIVILITY-tractography pipeline, including FSL tools (bedpostx, probtrackx2), for connectivity analysis.
  • Visualizes the resulting brain connectome using Hierarchical Edge Bundling.

Main Results:

  • Successfully deployed a cloud-based pipeline for brain connectome analysis.
  • Enabled interactive visualization of brain connectivity matrices derived from tractography data.
  • The application lists running tasks and presents completed analyses to the user.

Conclusions:

  • CIVILITY offers a valuable new resource for the neuroimaging community by providing a scalable and interactive platform for brain connectome analysis.
  • The tool addresses the need for efficient processing of computationally intensive tasks in medical applications.
  • Source code is available on NITRC, promoting community engagement and further development.