Effects of physiological noise in population analysis of diffusion tensor MRI data

Abstract

The goal of this study is to characterize the potential effect of artifacts originating from physiological noise on statistical analysis of diffusion tensor MRI (DTI) data in a population. DTI derived quantities including mean diffusivity (Trace(D)), fractional anisotropy (FA), and principal eigenvector (ε(1)) are computed in the brain of 40 healthy subjects from tensors estimated using two different methods: conventional nonlinear least-squares, and robust fitting (RESTORE). RESTORE identifies artifactual data points as outliers and excludes them on a voxel-by-voxel basis. We found that outlier data points are localized in specific spatial clusters in the population, indicating a consistency in brain regions affected across subjects. In brain parenchyma RESTORE slightly reduces inter-subject variance of FA and Trace(D). The dominant effect of artifacts, however, is bias. Voxel-wise analysis indicates that inclusion of outlier data points results in clusters of under- and over-estimation of FA, while Trace(D) is always over-estimated. Removing outliers affects ε(1) mostly in low anisotropy regions. It was found that brain regions known to be affected by cardiac pulsation - cerebellum and genu of the corpus callosum, as well as regions not previously reported, splenium of the corpus callosum-show significant effects in the population analysis. It is generally assumed that statistical properties of DTI data are homogenous across the brain. This assumption does not appear to be valid based on these results. The use of RESTORE can lead to a more accurate evaluation of a population, and help reduce spurious findings that may occur due to artifacts in DTI data.

Figure 1

Mean and standard deviation of the population (N=40) for FA after diffeomorphic tensor registration. The contribution of mis-registration to the voxelwise variability is improved compared to affine registration (not shown), particularly in the major white matter tracts and the cerebellum. Higher levels of variability can be observed in peripheral region, particularly at the top of the brain.

Figure 2

a) mean FA map with ORP overlayed in pink (ORP > 2%); b) outlier rejection probability (ORP) map is the mean outlier map of the 40 subjects. Note the well defined clusters of outlier data points within the brain; c) standard deviation of the 40 subjects' outlier maps. Areas of high probability outlier rejection also have a higher standard deviation within the population than areas not affected by outliers.

Figure 3

Subtraction maps of a) FA and b) Trace(D) standard deviation across 40 subjects. Bright areas indicate that processing with RESTORE reduces the variance, while dark areas indicate that processing with RESTORE increases the variance. Largest decreases in variance are seen in the cerebellum (large white arrows) and insular regions (thin white arrows), largest increase in variance in CSF regions (black arrows).

Figure 4

Subtraction maps of a) FA and b) Trace(D) mean value across 40 subjects. Bright areas indicate that processing with RESTORE reduces the mean value, while dark areas indicate that processing with RESTORE increases the mean value.

Figure 5

Cluster maps from the Randomise TFCE analysis overlaid on a) mean FA and b) mean Trace(D) images. All clusters shown are p<0.05. Blue clusters indicate areas of significantly lower mean value with conventional fitting. Red clusters indicate areas of significantly higher mean value with conventional fitting.

Figure 6

Top Row; Angular difference between NLS and RESTORE fitting of the average principal eigenvector (ε₁) for the population, overlaid on the mean FA map of the population. This reflects the orientational bias in ε₁ introduced by outliers. Bottom Row; Subtraction of the normalized area of the cone of uncertainty of the average ε₁ for the population. Bright regions indicate lower dispersion of the individual principal eigenvectors about the mean when outliers are removed by RESTORE and vice-versa for dark regions.