4.7 Article

Tractography atlas-based spatial statistics: Statistical analysis of diffusion tensor image along fiber pathways

Journal

NEUROIMAGE
Volume 125, Issue -, Pages 301-310

Publisher

ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2015.10.032

Keywords

DTI; FA; TBSS; Statistical analysis; Tractography atlas

Funding

  1. Research Grants Council of the Hong Kong Special Administrative Region, China [CUHK 416712, CUHK 14113214, CUHK 475711, CUHK 473012]
  2. Shenzhen Science and Technology Innovation Committee [CXZZ20140606164105361]
  3. National Natural Science Foundation of China [81271653, 81201157]
  4. Lui Che Woo Foundation

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The quantitative analysis of diffusion tensor image (DTI) data has attracted increasing attention in recent decades for studying white matter (WM) integrity and development. Among the current DTI analysis methods, tract-based spatial statistics (TBSS), as a pioneering approach for the voxelwise analysis of DTI data, has gained a lot of popularity due to its user-friendly framework. However, in recent years, the reliability and interpretability of TBSS have been challenged by several works, and several improvements over the original TBSS pipeline have been suggested. In this paper, we propose a new DTI statistical analysis method, named tractography atlas-based spatial statistics (TABSS). It doesn't rely on the accurate alignment of fractional anisotropy (FA) images for population analysis and gets rid of the skeletonization procedures of TBSS, which have been indicated as the major sources of error. Furthermore, TABSS improves the interpretability of results by directly reporting the resulting statistics on WM tracts, waiving the need of a WM atlas in the interpretation of the results. The feasibility of TABSS was evaluated in an example study to show age-related FA alternation pattern of healthy human brain. Through this preliminary study, it is validated that TABSS can provide detailed statistical results in a comprehensive and easy-to-understand way. (C) 2015 Elsevier Inc. All rights reserved.

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