Article

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

Oxford University Centre for Functional MRI of the Brain (FMRIB), Dept. Clinical Neurology, University of Oxford, UK.
NeuroImage (Impact Factor: 6.36). 08/2006; 31(4):1487-505. DOI: 10.1016/j.neuroimage.2006.02.024
Source: PubMed

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.

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    • "Using the Diffusion Toolbox, the diffusion tensor model was fitted to the data, from which fractional anisotropy (FA) images were calculated. Tract-based spatial statistics (TBSS) was used for voxel-based analyses of WM [14]. This involved registering all subjects' FA images to a common space (the FA158 MNI space template) using a combination of affine and nonlinear registration, creating the mean FA image, eroding it to a skeleton, and thresholding the skeleton at FA > 0.25. "
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    ABSTRACT: Functional and structural imaging studies have demonstrated the involvement of the brain in balance control. Nevertheless, how decisive grey matter density and white matter microstructural organisation are in predicting balance stability, and especially when linked to the effects of aging, remains unclear. Standing balance was tested on a platform moving at different frequencies and amplitudes in 30 young and 30 older adults, with eyes open and with eyes closed. Centre of pressure variance was used as an indicator of balance instability. The mean density of grey matter and mean white matter microstructural organisation were measured using voxel-based morphometry and diffusion tensor imaging, respectively. Mixed-effects models were built to analyse the extent to which age, grey matter density, and white matter microstructural organisation predicted balance instability. Results showed that both grey matter density and age independently predicted balance instability. These predictions were reinforced when the level of difficulty of the conditions increased. Furthermore, grey matter predicted balance instability beyond age and at least as consistently as age across conditions. In other words, for balance stability, the level of whole-brain grey matter density is at least as decisive as being young or old. Finally, brain grey matter appeared to be protective against falls in older adults as age increased the probability of losing balance in older adults with low, but not moderate or high grey matter density. No such results were observed for white matter microstructural organisation, thereby reinforcing the specificity of our grey matter findings.
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    • "Challenges of voxel-based analysis have been extensively studied by (Ashburner and Friston, 2000) and one of the key challenges is registration errors, which may cause unnecessary deformations or distortions that do not appear in the original image. One of the ways to minimize the registration errors in statistical inference is through skeletonization as suggested in Tract-Based Spatial Statistics (TBSS) of (Smith et al., 2006) and our approach is to focus only on voxels within the white matter region that satisfy further criteria such as goodness of fit. "
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    Full-text · Article · Feb 2016 · NeuroImage
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    • "A value of 0.2 has most commonly been used in studies of adult cases and was chosen despite the young age of some subjects within the analysis to restrict the FA skeleton to white matter regions, as judged by visual inspection of the skeleton. Each subject's aligned FA data was then projected onto this skeleton and the resulting data fed into voxel-wise cross subject statistics[15]. Three group analyses were performed: "
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    Full-text · Article · Jan 2016 · Neuroradiology
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