White matter changes in primary dystonia determined by 2D distribution analysis of diffusion tensor images

DIFFUSION TENSOR IMAGING (DTI) has been used extensively to investigate the properties of brain tissues at different levels of architectural organization and to diagnose many diseases (1,2). The most commonly used DTI parameters are mean diffusivity (MD or Dav) and fractional anisotropy index (FA). Previously, studies investigated the use of DTI to characterize normal and pathological tissues (3–7) according to the diffusion characteristics. In these studies, whole-brain mean diffusivity histograms were obtained from the entire brain. The whole-brain histogram data can be modeled by three (3,5,7) compartments each described by a one-dimensional (1D) Gaussian function. The first compartment represents the brain tissue consisting of white and gray matter. The second compartment is brain tissue mixed with cerebrospinal fluid (CSF) (ie, partial volume compartment) and the third compartment consists of CSF. One disadvantage of this 1D modeling utilizing only the average diffusion constant is that white and gray matter cannot be distinguished from each other since average diffusion constants of these are similar. In order to differentiate white and gray matter, a whole-brain FA histogram can be used instead. But there is still a considerable overlap of FA values in these tissue types, and usually prior to the distribution analysis, segmentation of the brain is necessary using an additional image set T1-weighted registered to the DTI images. The prior removing of CSF voxels may also be necessary by using the additional image set. Despite its limitations, 1D whole-brain diffusion histograms have been used extensively in the past, including in studies of breast imaging (8), aging (3,9,10), multiple sclerosis (4), brain maturation (5,11), traumatic brain injury (12–14), intracranial hemorrhage (15), lupus (7,16), chemoirradiation damage (17), Parkinson’s disease (18), and Batten’s disease (19). A distribution analysis tool that can differentiate the tissues according to the inherent diffusion characteristics (5,20–22) can be useful, especially in the diseases that cause microstructural damage, or have a nonfocal disease burden. 2D histogram analysis of DTI has been used previously to segment white and gray matter (WM and GM) (23). In that work, each tissue class (WM, GM, and CSF) was assumed to be modeled by a 2D Gaussian surface. In another work that used DTI for tissue segmentation (24), mean diffusivity was used to classify CSF and non-CSF tissue, and FA was used to classify WM and non-WM tissues. In this study we used 2D distribution analysis of DTIs with a four-compartmental physical brain model (WM, GM, CSF, and mixture). Each tissue class was also modeled by a 2D Gaussian function. FA and diffusion constant information from each voxel were determined and used concurrently in four 2D Gaussians describing the tissue types. The analysis included a fourth compartment (mixture) to account for partial-volume voxels as well as pathology. We applied this 2D distribution analysis on previously acquired DTI data from carriers of DYT1 mutation for primary dystonia (25). Clinical magnetic resonance imaging (MRI) of dystonia subjects normally do not show any pathology. Primary dystonia is a brain disorder in which affected individuals exhibit abnormal involuntary twisting movements and abnormal postures of the trunk and limbs (26). Hereditary dystonia has been associated with over 10 distinct genetic mutations. The DYT1 mutation is the most common of these and is inherited as an autosomal dominant trait, with a clinical penetrance rate of ~30%. Although the pathology of hereditary dystonias is not completely understood, the disorder is thought to involve abnormalities of neural pathways linking the cerebellum to the basal ganglia-thalamo-cortical motor regions (25,27,28). To demonstrate the applicability of this method to the investigation of brain diseases, the fitted parameters of the four-compartment brain model were used for comparison of DYT1 gene carriers and control subjects. We also segmented brain tissue according to the diffusion characteristics to look for obvious pathology.