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ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation.

A M Adeshina1, R Hashim2

  • 1Faculty of Computer Science and Information Technology, Universiti Tun Hussein Onn Malaysia, 86400, Parit Raja, Batu Pahat, Johor, Malaysia. codedengineer@yahoo.com.

Interdisciplinary Sciences, Computational Life Sciences
|August 12, 2015
PubMed
Summary

This study introduces an accelerated brain connectomic visualization framework. It efficiently maps brain networks with enhanced interactivity and speed on normal hardware, aiding stroke research.

Keywords:
Brain connectivityCUDAConnectomeNetwork analysisStrokeVisualization

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Area of Science:

  • Neuroscience
  • Computer Science
  • Medical Imaging

Background:

  • Stroke is a leading cause of mortality and disability worldwide.
  • Brain function relies on intricate vascular networks; disruptions like stroke can cause severe neurological deficits.
  • Visualizing the brain's connectome, or wiring, is computationally intensive and faces challenges with memory, speed, and interactivity.

Purpose of the Study:

  • To propose an accelerated framework for brain connectomic visualization.
  • To address limitations of existing methods, including high memory usage, slow execution, and poor interactivity.
  • To enhance the understanding of brain networks, particularly in the context of cerebrovascular incidents like stroke.

Main Methods:

  • Developed an accelerated brain structural connectivity framework utilizing graph theory and compute unified device architecture (CUDA).
  • Extended existing frameworks like SurLens Visualization and computer-aided hepatocellular carcinoma.
  • Evaluated the framework using stripped brain datasets from the University of North Carolina, USA.

Main Results:

  • The framework efficiently generates and extracts network points (nodes) and connections (edges) from datasets.
  • It displays nodes and edges, mapping data volume to the brain surface with simultaneous visualization of surfaces, nodes, and edges.
  • Achieved greater interactivity and intuitive representation of network features at interactive speeds.

Conclusions:

  • The proposed framework offers a significantly faster and more efficient method for brain connectomic visualization.
  • It overcomes previous limitations in memory consumption and execution speed.
  • The framework provides an intuitive and interactive tool for mapping brain datasets, valuable for neurological research and stroke studies.