Elsevier

NeuroImage

Volume 31, Issue 4, 15 July 2006, Pages 1487-1505
NeuroImage

Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data

https://doi.org/10.1016/j.neuroimage.2006.02.024Get rights and content

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.

Introduction

The diffusion of water in brain tissue is affected by the local tissue microstructure; for example, it diffuses more easily along the major axis of a white matter fibre bundle than perpendicular to it (Moseley et al., 1990). Magnetic resonance diffusion tensor imaging (DTI) is sensitive to water diffusion characteristics (such as the principal diffusion direction and the diffusion anisotropy) and has therefore been developed as a tool for investigating the local properties of brain tissues such as white matter tracts (Le Bihan, 2003). There has also been a great deal of interest in using diffusion anisotropy as a marker for white matter tract integrity, for example, for disease diagnosis, tracking disease progression, finding disease sub-categories, studying normal development/aging, and as complementary information to investigating normal brain function (Horsfield and Jones, 2002, Lim and Helpern, 2002, Moseley, 2002, Neil et al., 2002, Pagani et al., 2005).

Diffusion anisotropy describes how variable the diffusion is in different directions and is most commonly quantified via a measure known as fractional anisotropy (FA) (Pierpaoli and Basser, 1996). It is highest in major white matter tracts (maximum theoretical value 1) and lower in grey matter while approaching 0 in cerebro-spinal fluid. As a marker for tract integrity, FA is a useful quantity to compare across subjects as it is computable voxelwise and is a scalar value that is independent of the local fibre orientation (and therefore a relatively objective and straightforward measure to compare across subjects). Some researchers have simply summarised diffusion characteristics globally (for example, histogram-based summary measures of fractional anisotropy (Cercignani et al., 2001, Cercignani et al., 2003)), in order to compare different subjects. However, most recent work has been interested in spatially localising interesting diffusion-related changes. Many studies have, to this end, followed similar approaches to voxel-based morphometry (VBM, originally developed for finding local changes in grey matter density in T1-weighted structural brain images (Ashburner and Friston, 2000, Good et al., 2001)). In VBM-style FA analysis, each subject's FA image is registered into a standard space, and then voxelwise statistics are carried out to find areas which correlate to the covariate of interest (e.g., patients vs. normals, disability score, age).

There has been much debate about the strengths and limitations of VBM (Bookstein, 2001, Ashburner and Friston, 2001, Davatzikos, 2004, Ashburner and Friston, 2004). Some researchers continue to doubt the general interpretability of the results from this approach, primarily because there can be ambiguity as to whether apparent changes are really due to change in grey matter density or simply due to local misalignment, though it does seem that through careful application and validation, structural imaging studies using VBM can draw valid conclusions (e.g., Watkins et al., 2002). However, the potential problems with VBM-style approaches for data such as multi-subject FA images have not yet been investigated fully. In particular, this use raises a serious question, which has not yet been satisfactorily answered: how can one guarantee that any given standard space voxel contains data from the same part of the same white matter (WM) tract from each and every subject? In other words, how can we guarantee that registration of every subject's data to a common space has been totally successful, both in terms of resolving topological variabilities and in terms of the exact alignment of the very fine structures present in such data? A second problem relates to the standard practice of spatially smoothing data before computing voxelwise statistics — the amount of smoothing can greatly affect the final results, but there is no principled way of deciding how much smoothing is the “correct” amount (Jones et al., 2005). (Smoothing also increases effective partial voluming, a problem with VBM-style approaches particularly when applied to data such as FA; see Discussion for more comment on this.)

In this paper, we propose an approach to carrying out localised statistical testing of FA (and other diffusion-related) data that should alleviate the alignment problems. We project individual subjects' FA data into a common space in a way that is not dependent on perfect nonlinear registration. This is achieved through the use of (a) an initial approximate nonlinear registration, followed by (b) projection onto an alignment-invariant tract representation (the “mean FA skeleton”). No spatial smoothing is necessary in the image processing. We refer to this new approach as Tract-Based Spatial Statistics (TBSS). In the next section, we discuss, in slightly more depth, VBM-style approaches, and review some alternative approaches published to date. In following sections, we describe the proposed approach in detail, giving various example images illustrating the different analysis stages involved. Finally, we present example TBSS results from several DTI-based imaging studies.

Section snippets

VBM — overview and application to diffusion data

Voxel-based morphometry (Ashburner and Friston, 2000, Good et al., 2001) has been used in many structural imaging studies, looking for localised differences in grey matter density, typically between two groups of subjects. The common approach can be simply summarised:

  • (Optional) Create a study-specific registration template by aligning all subjects' structural images to an existing standard space template image (such as the MNI152). Average all aligned images to create the new template, and

Overview of TBSS

As discussed above, strengths of VBM-style analyses are that they are fully automated, simple to apply, investigate the whole brain, and do not require prespecifying and prelocalising regions or features of interest. Limitations include problems caused by alignment inaccuracies, and the lack of a principled way for choosing smoothing extent. Tractography-based approaches have fairly complementary advantages and disadvantages. They can overcome alignment problems by working in the space of

Results

In the following sections, we present example results and quantitations from different stages of the TBSS analysis, followed by example results from several diffusion imaging studies. The data generally used to illustrate TBSS are taken from a study of amyotrophic lateral sclerosis (ALS, a progressive neurodegenerative disease most prominently affecting the motor system). The diffusion acquisition parameters for this and all other data used in this paper are given in Fig. 5.

Discussion

In this final section, we discuss some of the limitations of our approach, as well as presenting some potentially interesting areas for future research.

Acknowledgments

We are grateful to Karla Miller for many discussions regarding the TBSS approach, Andreas Bartsch for valuable advice on this paper, Einar Heiervang for supplying the reproducibility data, and David Flitney and Brian Patenaude for work on the figure generation. We gratefully acknowledge funding from EPSRC, BBSRC, MRC, the Wellcome Trust and the Multiple Sclerosis Society.

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