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Diffusion Tensor Magnetic Resonance Imaging in the Analysis of Neurodegenerative Diseases
09:33

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Published on: July 28, 2013

Characteristics and variability of structural networks derived from diffusion tensor imaging.

Hu Cheng1, Yang Wang, Jinhua Sheng

  • 1Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA. hucheng@indiana.edu

Neuroimage
|March 28, 2012
PubMed
Summary

This study constructed structural brain networks using diffusion tensor imaging (DTI) and found that weighted networks, unlike binary ones, show distinct features. The developed methods demonstrate good reproducibility for analyzing brain network variability.

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Area of Science:

  • Neuroimaging
  • Network Science
  • Computational Neuroscience

Background:

  • Structural brain networks offer insights into brain organization.
  • Diffusion tensor imaging (DTI) and streamline tractography are key methods for mapping white matter tracts.
  • Understanding variability and reproducibility in network construction is crucial for reliable analysis.

Purpose of the Study:

  • To construct structural brain networks from DTI data in healthy adults.
  • To investigate the characteristics of binary versus weighted networks.
  • To assess the inter-subject variability and test-retest reliability of weighted brain networks.

Main Methods:

  • Construction of structural brain networks with 68 cortical nodes using DTI.
  • Application of two streamline tractography weighting schemes, including normalization by mean node volume.
  • Development of a novel thresholding method based on streamline count variance.
  • Network analysis including modularity, centrality, and small-worldness metrics.

Main Results:

  • Weighted networks exhibited a high correlation between nodal strength and betweenness centrality.
  • Binary and weighted networks showed distinct modularity and nodal betweenness centrality despite similar small-worldness.
  • Weighting scheme one demonstrated stable reproducibility for global efficiency, clustering coefficient, and diversity.
  • Considerable inter-subject variability was observed in edge weights and local metrics.

Conclusions:

  • Weighted and binary structural brain networks possess distinct topological properties.
  • The proposed methods for constructing weighted networks show reasonable reproducibility for global metrics.
  • Further investigation into the variability of weighted network metrics is warranted.