Access the full text.
Sign up today, get DeepDyve free for 14 days.
K. Lim, J. Helpern, J. Helpern (2002)
Neuropsychiatric applications of DTI – a reviewNMR in Biomedicine, 15
R. Bammer, M. Auer, S. Keeling, M. Augustin, L. Stables, R. Prokesch, R. Stollberger, M. Moseley, F. Fazekas (2002)
Diffusion tensor imaging using single‐shot SENSE‐EPIMagnetic Resonance in Medicine, 48
S. Wakana, L. Nagae-Poetscher, P. Zijl, B. Crain (2005)
MRI Atlas of Human White Matter
R. Buxton (2002)
Diffusion and the MR Signal
Derek Jones (2004)
The effect of gradient sampling schemes on measures derived from diffusion tensor MRI: A Monte Carlo study †Magnetic Resonance in Medicine, 51
Derek Jones, M. Horsfield, A. Simmons (1999)
Optimal strategies for measuring diffusion in anisotropic systems by magnetic resonance imagingMagnetic Resonance in Medicine, 42
M. Horsfield, Derek Jones (2002)
Applications of diffusion‐weighted and diffusion tensor MRI to white matter diseases – a reviewNMR in Biomedicine, 15
D. Alexander, G. Barker (2005)
Optimal imaging parameters for fiber-orientation estimation in diffusion MRINeuroImage, 27
H. Gudbjartsson, S. Patz (1995)
The rician distribution of noisy mri dataMagnetic Resonance in Medicine, 34
K. Worsley, Sean Marrett, P. Neelin, A. Vandal, Karl Friston, Alan Evans (1996)
A unified statistical approach for determining significant signals in images of cerebral activationHuman Brain Mapping, 4
Thomas Nichols, A. Holmes (2002)
Nonparametric permutation tests for functional neuroimaging: A primer with examplesHuman Brain Mapping, 15
M. Moseley, Y. Cohen, J. Kucharczyk, J. Mintorovitch, H. Asgari, M. Wendland, J. Tsuruda, D. Norman (1990)
Diffusion-weighted MR imaging of anisotropic water diffusion in cat central nervous system.Radiology, 176 2
C. Pierpaoli, P. Basser (1996)
Toward a quantitative assessment of diffusion anisotropyMagnetic Resonance in Medicine, 36
Stephen Smith, M. Jenkinson, H. Johansen-Berg, D. Rueckert, Thomas Nichols, C. Mackay, K. Watkins, O. Ciccarelli, M. Cader, P. Matthews, Timothy Behrens (2006)
Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion dataNeuroImage, 31
J. Ashburner, Karl Friston (2000)
Voxel-Based Morphometry—The MethodsNeuroImage, 11
M. Moseley (2002)
Diffusion tensor imaging and aging – a reviewNMR in Biomedicine, 15
D. Rueckert, L. Sonoda, C. Hayes, D. Hill, M. Leach, D. Hawkes (1999)
Nonrigid registration using free-form deformations: application to breast MR imagesIEEE Transactions on Medical Imaging, 18
C. Beaulieu (2002)
The basis of anisotropic water diffusion in the nervous system – a technical reviewNMR in Biomedicine, 15
Chunlei Liu, R. Bammer, Dong-Hyun Kim, M. Moseley (2004)
Self‐navigated interleaved spiral (SNAILS): Application to high‐resolution diffusion tensor imagingMagnetic Resonance in Medicine, 52
K. Pruessmann (2006)
Encoding and reconstruction in parallel MRINMR in Biomedicine, 19
J. Pipe, Victoria Farthing, K. Forbes (2002)
Multishot diffusion‐weighted FSE using PROPELLER MRIMagnetic Resonance in Medicine, 47
D. Bihan (2003)
Looking into the functional architecture of the brain with diffusion MRINature Reviews Neuroscience, 4
J. Neil, Jeffrey Miller, Pratik Mukherjee, P. Hüppi, P. Hüppi (2002)
Diffusion tensor imaging of normal and injured developing human brain ‐ a technical reviewNMR in Biomedicine, 15
J. Andersson, S. Skare, J. Ashburner (2003)
How to correct susceptibility distortions in spin-echo echo-planar images: application to diffusion tensor imagingNeuroImage, 20
E. Heiervang, Timothy Behrens, C. Mackay, M. Robson, H. Johansen-Berg (2006)
Between session reproducibility and between subject variability of diffusion MR and tractography measuresNeuroImage, 33
Timothy Behrens, M. Woolrich, M. Jenkinson, H. Johansen-Berg, R. Nunes, S. Clare, P. Matthews, J. Brady, S.M. Smith (2003)
Characterization and propagation of uncertainty in diffusion‐weighted MR imagingMagnetic Resonance in Medicine, 50
RB Buxton (2002)
Introduction to Functional Magnetic Resonance Imaging
Derek Jones, M. Symms, M. Cercignani, R. Howard (2005)
The effect of filter size on VBM analyses of DT-MRI dataNeuroImage, 26
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.
Nature Protocols – Springer Journals
Published: Mar 15, 2007
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.