Smith, Stephen M., Jenkinson, Mark, Johansen-Berg, Heidi, Rueckert, Daniel, Nichols, Thomas E., Mackay, Clare E., Watkins, Kate E., Ciccarelli, Olga, Cader, M. Zaheer, Matthews, Paul M. and Behrens, Timothy E. J.. (2006) Tract-based spatial statistics : voxelwise analysis of multi-subject diffusion data. NeuroImage, Vol.31 (No.4). pp. 1487-1505. ISSN 10538119

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Abstract

There has been much recent interest in using magnetic resonance diffusion imaging to provide information about anatomical connectivity in the brain, by measuring the anisotropic diffusion of water in white matter tracts. One of the measures most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies how strongly directional the local tract structure is. Many imaging studies are starting to use FA images in voxelwise statistical analyses, in order to localise brain changes related to development, degeneration and disease. However, optimal analysis is compromised by the use of standard registration algorithms; there has not to date been a satisfactory solution to the question of how to align FA images from multiple subjects in a way that allows for valid conclusions to be drawn from the subsequent voxelwise analysis. Furthermore, the arbitrariness of the choice of spatial smoothing extent has not yet been resolved. In this paper, we present a new method that aims to solve these issues via (a) carefully tuned non-linear registration, followed by (b) projection onto an alignment-invariant tract representation (the “mean FA skeleton”). We refer to this new approach as Tract-Based Spatial Statistics (TBSS). TBSS aims to improve the sensitivity, objectivity and interpretability of analysis of multi-subject diffusion imaging studies. We describe TBSS in detail and present example TBSS results from several diffusion imaging studies.

Item Type:	Journal Article
Subjects:	Q Science > QA Mathematics R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Divisions:	Faculty of Science > Statistics
Library of Congress Subject Headings (LCSH):	Diffusion magnetic resonance imaging -- Data processing, Spatial analysis (Statistics), Brain -- Imaging -- Statistical methods, Brain -- Imaging -- Data processing
Journal or Publication Title:	NeuroImage
Publisher:	Elsevier
ISSN:	10538119
Official Date:	19 April 2006
Dates:	Date Event 19 April 2006 ["eprint_fieldopt_dates_date_type_available" not defined]
Volume:	Vol.31
Number:	No.4
Page Range:	pp. 1487-1505
Identifier:	10.1016/j.neuroimage.2006.02.024
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
URI:	http://wrap.warwick.ac.uk/id/eprint/38225

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