Group analysis of DTI fiber tract statistics with application to neurodevelopment

Diffusion tensor imaging (DTI) provides a unique source of information about the underlying tissue structure of brain white matter in vivo including both the geometry of major fiber bundles as well as quantitative information about tissue properties represented by derived tensor measures. This paper presents a method for statistical comparison of fiber bundle diffusion properties between populations of diffusion tensor images. Unbiased diffeomorphic atlas building is used to compute a normalized coordinate system for populations of diffusion images. The diffeomorphic transformations between each subject and the atlas provide spatial normalization for the comparison of tract statistics. Diffusion properties, such as fractional anisotropy (FA) and tensor norm, along fiber tracts are modeled as multivariate functions of arc length. Hypothesis testing is performed non-parametrically using permutation testing based on the Hotelling T(2) statistic. The linear discriminant embedded in the T(2) metric provides an intuitive, localized interpretation of detected differences. The proposed methodology was tested on two clinical studies of neurodevelopment. In a study of 1 and 2 year old subjects, a significant increase in FA and a correlated decrease in Frobenius norm was found in several tracts. Significant differences in neonates were found in the splenium tract between controls and subjects with isolated mild ventriculomegaly (MVM) demonstrating the potential of this method for clinical studies.

[1]  Derek K. Jones,et al.  Spatial Normalization and Averaging of Diffusion Tensor MRI Data Sets , 2002, NeuroImage.

[2]  W. Eric L. Grimson,et al.  A unified framework for clustering and quantitative analysis of white matter fiber tracts , 2008, Medical Image Anal..

[3]  Xavier Pennec,et al.  A Riemannian Framework for Tensor Computing , 2005, International Journal of Computer Vision.

[4]  E. Bullmore,et al.  Formal characterization and extension of the linearized diffusion tensor model , 2005, Human brain mapping.

[5]  Carl-Fredrik Westin,et al.  Tract-Based Morphometry , 2007, MICCAI.

[6]  G. Dullerud.  A Computational Framework , 1996 .

[7]  L. Lotspeich,et al.  White matter structure in autism: preliminary evidence from diffusion tensor imaging , 2004, Biological Psychiatry.

[8]  James C. Gee,et al.  Spatial transformations of diffusion tensor magnetic resonance images , 2001, IEEE Transactions on Medical Imaging.

[9]  Derek K. Jones,et al.  The effect of filter size on VBM analyses of DT-MRI data , 2005, NeuroImage.

[10]  Guido Gerig,et al.  Unbiased diffeomorphic atlas construction for computational anatomy , 2004, NeuroImage.

[11]  Hangyi Jiang,et al.  Pediatric diffusion tensor imaging: Normal database and observation of the white matter maturation in early childhood , 2006, NeuroImage.

[12]  John H. Gilmore,et al.  Improved Correspondence for DTI Population Studies Via Unbiased Atlas Building , 2006, MICCAI.

[13]  P. Basser,et al.  In vivo fiber tractography using DT‐MRI data , 2000, Magnetic resonance in medicine.

[14]  Carl-Fredrik Westin,et al.  DTI and MTR abnormalities in schizophrenia: Analysis of white matter integrity , 2005, NeuroImage.

[15]  Susumu Mori,et al.  MRI Atlas of Human White Matter 2nd Edition , 2010 .

[16]  Hervé Delingette,et al.  A Computational Framework for the Statistical Analysis of Cardiac Diffusion Tensors: Application to a Small Database of Canine Hearts , 2007, IEEE Transactions on Medical Imaging.

[17]  S. Lawrie,et al.  Structural disconnectivity in schizophrenia: a diffusion tensor magnetic resonance imaging study , 2003, British Journal of Psychiatry.

[18]  Harald Hampel,et al.  Fiber connections between the cerebral cortex and the corpus callosum in Alzheimer's disease: a diffusion tensor imaging and voxel-based morphometry study. , 2007, Cerebral cortex.

[19]  John H. Gilmore,et al.  Group Statistics of DTI Fiber Bundles Using Spatial Functions of Tensor Measures , 2008, MICCAI.

[20]  B. Silverman,et al.  Functional Data Analysis , 1997 .

[21]  Dongrong Xu,et al.  Seamless Warping of Diffusion Tensor Fields , 2008, IEEE Transactions on Medical Imaging.

[22]  Christian Beaulieu,et al.  Voxel based versus region of interest analysis in diffusion tensor imaging of neurodevelopment , 2007, NeuroImage.

[23]  J. Petrella,et al.  Evaluation of white matter anisotropy in Krabbe disease with diffusion tensor MR imaging: initial experience. , 2001, Radiology.

[24]  Hong Sun,et al.  Quantitative analysis along the pyramidal tract by length-normalized parameterization based on diffusion tensor tractography: Application to patients with relapsing neuromyelitis optica , 2006, NeuroImage.

[25]  P. Thomas Fletcher,et al.  Riemannian geometry for the statistical analysis of diffusion tensor data , 2007, Signal Process..

[26]  S. Wakana,et al.  MRI Atlas of Human White Matter , 2005 .

[27]  John H. Gilmore,et al.  Prenatal Mild Ventriculomegaly Predicts Abnormal Development of the Neonatal Brain , 2008, Biological Psychiatry.

[28]  G. Kindlmann,et al.  Orthogonal tensor invariants and the analysis of diffusion tensor magnetic resonance images , 2006, Magnetic resonance in medicine.

[29]  Guido Gerig,et al.  Fiber tract-oriented statistics for quantitative diffusion tensor MRI analysis. , 2006, Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention.

[30]  Carl-Fredrik Westin,et al.  Comparison of fiber tracts derived from in-vivo DTI tractography with 3D histological neural tract tracer reconstruction on a macaque brain , 2007, NeuroImage.

[31]  Carl-Fredrik Westin,et al.  Delineating white matter structure in diffusion tensor MRI with anisotropy creases , 2007, Medical Image Anal..

[32]  N. Ayache,et al.  Log‐Euclidean metrics for fast and simple calculus on diffusion tensors , 2006, Magnetic resonance in medicine.

[33]  Ross T. Whitaker,et al.  A Volumetric Approach to Quantifying Region-to-Region White Matter Connectivity in Diffusion Tensor MRI , 2007, IPMI.

[34]  Carl-Fredrik Westin,et al.  Automatic Tractography Segmentation Using a High-Dimensional White Matter Atlas , 2007, IEEE Transactions on Medical Imaging.

[35]  J. Gilmore,et al.  Mild ventriculomegaly detected in utero with ultrasound: clinical associations and implications for schizophrenia 1 Presented in part at the 36th Annual Meeting, American College of Neuropsychopharmacology, Waikoloa, Hawaii, 10 December 1997. 1 , 1998, Schizophrenia Research.

[36]  Sharon Peled,et al.  New Perspectives on the Sources of White Matter DTI Signal , 2007, IEEE Transactions on Medical Imaging.

[37]  B. Vemuri,et al.  Generalized scalar measures for diffusion MRI using trace, variance, and entropy , 2005, Magnetic resonance in medicine.

[38]  Thomas E. Nichols,et al.  Nonparametric permutation tests for functional neuroimaging: A primer with examples , 2002, Human brain mapping.

[39]  D. Billheimer.  Functional Data Analysis, 2nd edition edited by J. O. Ramsay and B. W. Silverman , 2007 .

[40]  Daniel Rueckert,et al.  Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data , 2006, NeuroImage.

[41]  P. Basser,et al.  Microstructural and physiological features of tissues elucidated by quantitative-diffusion-tensor MRI. 1996. , 1996, Journal of magnetic resonance.

[42]  J. K. Smith,et al.  Early Postnatal Development of Corpus Callosum and Corticospinal White Matter Assessed with Quantitative Tractography , 2007, American Journal of Neuroradiology.

[43]  Carlo Pierpaoli,et al.  PASTA: Pointwise assessment of streamline tractography attributes , 2005, Magnetic resonance in medicine.

[44]  D. Shen,et al.  Spatial normalization of diffusion tensor fields , 2003, Magnetic resonance in medicine.

[45]  Paul A. Yushkevich,et al.  Structure-specific statistical mapping of white matter tracts , 2007, NeuroImage.

[46]  Denis Le Bihan,et al.  Looking into the functional architecture of the brain with diffusion MRI , 2003, Nature Reviews Neuroscience.

[47]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[48]  Marc Niethammer,et al.  Finsler Tractography for White Matter Connectivity Analysis of the Cingulum Bundle , 2007, MICCAI.

[49]  Brian B. Avants,et al.  High-Dimensional Spatial Normalization of Diffusion Tensor Images Improves the Detection of White Matter Differences: An Example Study Using Amyotrophic Lateral Sclerosis , 2007, IEEE Transactions on Medical Imaging.

[50]  David Bonekamp,et al.  Diffusion tensor imaging in children and adolescents: Reproducibility, hemispheric, and age-related differences , 2007, NeuroImage.