Whole brain voxel-wise analysis of single-subject serial DTI by permutation testing

Abstract

Diffusion tensor MRI (DTI) has been widely used to investigate brain microstructural changes in pathological conditions as well as for normal development and aging. In particular, longitudinal changes are vital to the understanding of progression but these studies are typically designed for specific regions of interest. To analyze changes in these regions traditional statistical methods are often employed to elucidate group differences which are measured against the variability found in a control cohort. However, in some cases, rather than collecting multiple subjects into two groups, it is necessary and more informative to analyze the data for individual subjects. There is also a need for understanding changes in a single subject without prior information regarding the spatial distribution of the pathology, but no formal statistical framework exists for these voxel-wise analyses of DTI. In this study, we present PERVADE (PERmutation Voxel-wise Analysis of Diffusion Estimates), a whole brain analysis method for detecting localized FA changes between two separate points in time of any given subject, without any prior hypothesis about where changes might occur. Exploiting the nature of DTI that it is calculated from multiple diffusion-weighted images of each region, permutation testing, a non-parametric hypothesis testing technique, was modified for the analysis of serial DTI data and implemented for voxel-wise hypothesis tests of diffusion metric changes, as well as for suprathreshold cluster analysis to correct for multiple comparisons. We describe PERVADE in detail and present results from Monte Carlo simulation supporting the validity of the technique as well as illustrative examples from a healthy subject and patients in the early stages of multiple sclerosis.

Diffusion Tensor MR Imaging and Fiber Tractography: Technical Considerations

“…To avoid the problem of non-normality, one can use nonparametric statistics such as permutation testing to perform whole-brain DTI analysis. 68 To avoid the pitfalls of group spatial normalization, one can even perform this testing in single subjects to examine DTI changes across serial examinations, though this has less statistical power than comparisons between large groups.…”

Connecting white matter injury and thalamic atrophy in clinically isolated syndromes

“…The TC and non-TC white matter regions were further masked to remove lesions and therefore determine values only in normalappearing white matter (TC and non-TC NAWM). The mean for each of the DTI metrics (mean diffusivity: MD, fractional anisotropy: FA, major eigenvalue: λ1, and average of the minor eigenvalues: λT) was calculated (as described elsewhere [15,16]) in each of these 8 TC ROI (separately in the NAWM and lesions). The metrics in the left and right regions were not significantly different and therefore were averaged for further analyses.…”

Longitudinal Change Detection: Inference on the Diffusion Tensor Along White-Matter Pathways

“…Diffusivity profiles obtained from DW-MRI acquisitions are usually modeled by diffusion tensors of rank 2 (DTs). Some previous work has addressed change detection between DT-derived scalar images, [2], [4], or between DT fields, [3], [6], [5]. In [6], statistical tests have been developed to compare two sets of tensors with application to the comparison of two groups of subjects.…”