Optimization of seed density in DTI tractography for structural networks

Hu Cheng, Yang Wang, Jinhua Sheng, Olaf Sporns, William G. Kronenberger, Vincent P. Mathews, Tom A. Hummer, Andrew J. Saykin

Research output: Contribution to journalArticlepeer-review

38 Scopus citations

Abstract

Diffusion tensor imaging (DTI) has been used for mapping the structural network of the human brain. The network can be constructed by choosing various brain regions as nodes and fiber tracts connecting those regions as links. The structural network generated from DTI data can be affected by noise in the scans and the choice of tractography algorithm. This study aimed to examine the effect of the number of seeds in tractography on the variance of structural networks. The variance of the network was characterized using an approach similar to the National Electrical Manufacturers Association (NEMA) standards for measurement of image noise. It was shown that the variance of the network is inversely related to the square root of seed density. Consequently, the number of seeds has a large impact on local characteristics and metrics of the network architecture. As the number of seeds increased, increased stability of structural network metrics was observed. However, more seeds can also lead to more spurious fibers and thus affect nodal degrees and edge weights, and proper thresholding may be necessary to create an appropriate weighted network. Because the variance of the network is also influenced by other imaging factors, further increase in the number of seeds has little effect in reducing the network variance. The selection of the seed number should be a balance between the network variance and computational effort.

Original languageEnglish (US)
Pages (from-to)264-272
Number of pages9
JournalJournal of Neuroscience Methods
Volume203
Issue number1
DOIs
StatePublished - Jan 15 2012

Keywords

  • Diffusion tensor imaging
  • Seeds
  • Structural brain network
  • Tractography
  • Variance

ASJC Scopus subject areas

  • Neuroscience(all)

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