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: 6.13). 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.

  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: In brain regions containing crossing fibre bundles, voxel-average diffusion MRI measures such as Fractional Anisotropy (FA) are difficult to interpret, and lack within-voxel single fibre population specificity. Recent work has focused on the development of more interpretable quantitative measures that can be associated with a specific fibre population within a voxel containing crossing fibres (herein we use fixel to refer to a specific fibre population within a single voxel). Unfortunately, traditional 3D methods for smoothing and cluster-based statistical inference cannot be used for voxel-based analysis of these measures, since the local neighbourhood for smoothing and cluster formation can be ambiguous when adjacent voxels may have different numbers of fixels, or ill-defined when they belong to different tracts. Here we introduce a novel statistical method to perform whole-brain fixel-based analysis called connectivity-based fixel enhancement (CFE). CFE uses probabilistic tractography to identify structurally connected fixels that are likely to share underlying anatomy and pathology. Probabilistic connectivity information is then used for tract-specific smoothing (prior to the statistical analysis) and enhancement of the statistical map (using a threshold-free cluster enhancement-like approach). To investigate the characteristics of the CFE method, we assessed sensitivity and specificity using a large number of combinations of CFE enhancement parameters and smoothing extents, using simulated pathology generated with a range of test-statistic signal-to-noise ratios in five different white matter regions (chosen to cover a broad range of fibre bundle features). The results suggest that CFE input parameters are relatively insensitive to the characteristics of the simulated pathology. We therefore recommend a single set of CFE parameters that should give near optimal results in future studies where the group effect is unknown. We then demonstrate the proposed method by comparing apparent fibre density between motor neurone disease (MND) patients with control subjects. The MND results illustrate the benefit of fixel-specific statistical inference in white matter regions that contain crossing fibres. Copyright © 2015. Published by Elsevier Inc.
    NeuroImage 05/2015; 11. DOI:10.1016/j.neuroimage.2015.05.039 · 6.13 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: The objective of this work was to test the hypotheses that a) more frequent cognitive activity in late life is associated with higher brain diffusion anisotropy and lower trace of the diffusion tensor, and b) brain diffusion characteristics partially mediate the association of late life cognitive activity with cognition. As part of a longitudinal cohort study, 379 older people without dementia rated their frequency of participation in cognitive activities, completed a battery of cognitive function tests, and underwent diffusion tensor imaging. We used tract-based spatial statistics to test the association between late life cognitive activity and brain diffusion characteristics. Clusters with statistically significant findings defined regions of interest in which we tested the hypothesis that diffusion characteristics partially mediate the association of late life cognitive activity with cognition. More frequent cognitive activity in late life was associated with higher level of global cognition after adjustment for age, sex, education, and indicators of early life cognitive enrichment (p = 0.001). More frequent cognitive activity was also related to higher fractional anisotropy in the left superior and inferior longitudinal fasciculi, left fornix, and corpus callosum, and lower trace in the thalamus (p < 0.05, FWE-corrected). After controlling for fractional anisotropy or trace from these regions, the regression coefficient for the association of late life cognitive activity with cognition was reduced by as much as 26 %. These findings suggest that the association of late life cognitive activity with cognition may be partially mediated by brain diffusion characteristics.
    Brain Imaging and Behavior 05/2015; DOI:10.1007/s11682-015-9405-5 · 3.39 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Abnormalities within white matter (WM) have been identified in autism spectrum disorder (ASD). Although there is some support for greater neurobiological deficits among females with ASD, there is little research investigating sex differences in WM in ASD. We used diffusion tensor imaging (DTI) to investigate WM aberration in 25 adults with high-functioning ASD and 24 age-, sex- and IQ-matched controls. Tract-based spatial statistics (TBSS) was used to explore differences in WM in major tract bundles. The effects of biological sex were also investigated. TBSS revealed no differences in fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), or axial diffusivity (AD) between groups. There were no effects of biological sex. We consider whether methodological differences between past studies have contributed to the highly heterogeneous findings in the literature. Finally, we suggest that, among a high-functioning sample of adults with ASD, differences in WM microstructure may not be related to clinical impairment. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.
    Psychiatry Research Neuroimaging 05/2015; DOI:10.1016/j.pscychresns.2015.05.003 · 2.83 Impact Factor


Available from