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Article
Application of a Novel Quantitative Tractography Based Analysis of Diffusion Tensor Imaging to Examine Fiber Bundle Length in Human Cerebral White Matter
Psychology Faculty Works
  • Robert H. Paul, UMSL
  • Laurie M. Baker
  • Ryan P. Cabeen
  • Sarah Cooley
Document Type
Article
Disciplines
Keywords
  • Psychology,
  • Journal Article,
  • Paul Robert,
  • Clinical Psychology
Abstract

This paper reviews basic methods and recent applications of length-based fiber bundle analysis of cerebral white matter using diffusion magnetic resonance imaging (dMRI). Diffusion weighted imaging (DWI) is a dMRI technique that uses the random motion of water to probe tissue microstructure in the brain. Diffusion tensor imaging (DTI) is an extension of DWI that measures the magnitude and direction of water diffusion in cerebral white matter, using either voxel-based scalar metrics or tractography-based analyses. More recently, quantitative tractography based on diffusion tensor imaging (qtDTI) technology has been developed to help quantify aggregate structural anatomical properties of white matter fiber bundles, including both scalar metrics of bundle diffusion and more complex morphometric properties, such as fiber bundle length (FBL). Unlike traditional scalar diffusion metrics, FBL reflects the direction and curvature of white matter pathways coursing through the brain and is sensitive to changes within the entire tractography model. In this paper, we discuss applications of this approach to date that have provided new insights into brain organization and function. We also discuss opportunities for improving the methodology through more complex anatomical models and potential areas of new application for qtDTI.

Publication Date
4-1-2016
DOI
10.21300/18.1.2016.21
Citation Information
Baker, L. M., Cabeen, R. P., Cooley, S., Laidlaw, D. H., & Paul, R. H. (2016). Application of a Novel Quantitative Tractography Based Analysis of Diffusion Tensor Imaging to Examine Fiber Bundle Length in Human Cerebral White Matter. Technology and Innovation, 18(1), 21–29. https://doi.org/10.21300/18.1.2016.21