Abstract
There is much interest in using magnetic resonance diffusion imaging to provide information on anatomical connectivity in the brain by measuring the diffusion of water in white matter tracts. Among the measures, the most commonly derived from diffusion data is fractional anisotropy (FA), which quantifies local tract directionality and integrity. Many multi-subject imaging studies are using FA images to localize brain changes related to development, degeneration and disease. In a recent paper, we presented a new approach, tract-based spatial statistics (TBSS), which aims to solve crucial issues of cross-subject data alignment, allowing localized cross-subject statistical analysis. This works by transforming the data from the centers of the tracts that are consistent across a study's subjects into a common space. In this protocol, we describe the MRI data acquisition and analysis protocols required for TBSS studies of localized change in brain connectivity across multiple subjects.
NOTE: In the version of this article originally published online, the URL given in EQUIPMENT SETUP, under “Computing equipment”, should have been http://www.fmrib.ox.ac.uk/fsl. In the first line of Box 2, “head motion” should have read “head motion effects”. In the legend to Figure 1, “and a ˙ b value" should have been “and a b value” and "an signal-to-noise ratio" should have been "a signal-to-noise ratio". These errors have been corrected in all versions of the article.
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Change history
29 March 2007
In the version of this article originally published online, the URL given in EQUIPMENT SETUP, under “Computing equipment”, should have been http://www.fmrib.ox.ac.uk/fsl. In the first line of Box 2, “head motion” should have read “head motion effects”. In the legend to Figure 1, “and a ˙ b value" should have been “and a b value” and "an signal-to-noise ratio" should have been "a signal-to-noise ratio". These errors have been corrected in all versions of the article.
References
Moseley, M.E. et al. Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system. Radiology 176, 439–445 (1990).
Le Bihan, D. Looking into the functional architecture of the brain with diffusion MRI. Nat. Rev. Neurosci. 4, 469–480 (2003).
Horsfield, M.A. & Jones, D.K. Application of diffusion-weighted and diffusion tensor MRI to white matter diseases—a review. NMR Biomed. 15, 570–577 (2002).
Lim, K.O. & Helpern, J.A. Neuropsychiatric applications of DTI—a review. NMR Biomed. 15, 587–593 (2002).
Moseley, M. Diffusion tensor imaging and aging—a review. NMR Biomed. 15, 553–560 (2002).
Neil, J., Miller, J., Mukherjee, P. & Huppi, P.S. Diffusion tensor imaging of normal and injured developing human brain—a technical review. NMR Biomed. 15, 543–552 (2002).
Buxton, R.B. Diffusion and the MR signal. In Introduction to Functional Magnetic Resonance Imaging 185–216 (Cambridge University Press, Cambridge, 2002).
Mori, S., Wakana, S., Nagae-Poetscher, L.M. & van Zijl, P.C.M. MRI Atlas of Human White Matter. Elsevier, Amsterdam, The Netherlands (2005).
Pierpaoli, C. & Basser, P.J. Toward a quantitive assessment of diffusion anisotropy. Magn. Reson. Med. 36, 893–906 (1996).
Beaulieu, C. The basis of anisotropic water diffusion in the nervous system—a technical review. NMR Biomed. 15, 435–455 (2002).
Ashburner, J. & Friston, K.J. Voxel-based morphometry—the methods. NeuroImage 11, 805–821 (2000).
Smith, S.M. et al. Tract-based spatial statistics: voxelwise analysis of multi-subject diffusion data. NeuroImage 31, 1487–1505 (2006).
Rueckert, D. et al. Nonrigid registration using free-form deformations: application to breast MR images. IEEE Trans. Med. Imaging 18, 712–721 (1999).
Heiervang, E., Behrens, T.E., Mackay, C.E., Robson, M.D. & Johansen-Berg, H. Between session reproducibility and between subject variability of diffusion MR and tractography measures. NeuroImage 33, 867–877 (2006).
Jones, D.K., Symms, M.R., Cercignani, M. & Howard, R.J. The effect of filter size on VBM analyses of DT-MRI data. NeuroImage 26, 546–554 (2005).
Worsley, K.J. et al. A unified statistical approach for determining significant signals in images of cerebral activation. Hum. Brain Mapp. 4, 58–73 (1996).
Nichols, T.E. & Holmes, A.P. Nonparametric permutation tests for functional neuroimaging: a primer with examples. Hum. Brain Mapp. 15, 1–25 (2002).
Jones, D.K., Horsfield, M.A. & Simmons, A. Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imaging. Magn. Reson. Med. 42, 515–525 (1999).
Jones, D.K. The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: a Monte Carlo study. Magn. Reson. Med. 51, 807–815 (2004).
Behrens, T.E.J. et al. Characterization and propagation of uncertainty in diffusion-weighted MR imaging. Magn. Reson. Med. 50, 1077–1088 (2003).
Gudbjartsson, H. & Patz, S. The Rician distribution of noisy MRI data. Magn. Reson. Med. 34, 910–914 (1995).
Andersson, J.L.R., Skare, S. & Ashburner, J. How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imaging. NeuroImage 20, 870–888 (2003).
Liu, C., Bammer, R., Kim, D.-H. & Moseley, M.E. Self-navigated interleaved spiral (SNAILS): application to high-resolution diffusion tensor imaging. Magn. Reson. Med. 52, 1388–1396 (2004).
Pipe, J.G., Farthing, V.G. & Forbes, K.P. Multishot diffusion-weighted FSE using PROPELLER MRI. Magn. Reson. Med. 47, 42–52 (2002).
Alexander, D.C. & Barker, G.J. Optimal imaging parameters for fiber-orientation estimation in diffusion MRI. NeuroImage 27, 357–367 (2005).
Pierpaoli, C., Marenco, S., Rohde, G., Jones, D.K. & Barnett, A.S. Analyzing the contribution of cardiac pulsation to the variability of quantities derived from the diffusion tensor. Proc. Intl. Soc. Mag. Reson. Med. 70 (2003).
Pruessmann, K.P. Encoding and reconstruction in parallel MRI. NMR Biomed. 19, 288–299 (2006).
Bammer, R. et al. Diffusion tensor imaging using single-shot SENSE-EPI. Magn. Reson. Med. 48, 128–136 (2002).
Smith, S.M., Johansen-Berg, H., Mackay, C., Behrens, T.E. & Bartsch, A.J. Voxelwise analysis of FA data: session and subject variability, Gaussianity and dependence on acquisition. Proc. Intl. Soc. Mag. Reson. Med. 1063 (2006).
Acknowledgements
The authors are supported by the UK Engineering and Physical Sciences Research Council, the Medical Research Council and the Wellcome Trust.
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Smith, S., Johansen-Berg, H., Jenkinson, M. et al. Acquisition and voxelwise analysis of multi-subject diffusion data with Tract-Based Spatial Statistics. Nat Protoc 2, 499–503 (2007). https://doi.org/10.1038/nprot.2007.45
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DOI: https://doi.org/10.1038/nprot.2007.45
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