| dc.description.abstract | By sampling the self-diffusion of water molecules, diffusion tensor imaging (DTI) is able to characterize the microstructure of brain white matter. Previous DTI studies in schizophrenia have reported white matter alterations as measured by changes in fractional anisotropy. However, DTI analysis is not capable of distinguishing between possible causes, such as a change in the fiber orientation coherence, a change in the intrinsic diffusivity of the fibers, or both. Compared with DTI, high angular resolution diffusion imaging (HARDI) provides more detailed structural information of underlying tissues. Fiber ORientation Estimated using Continuous Axially Symmetric Tensors (FORECAST) is a spherical deconvolution method to analyze HARDI data.
Based on Monte Carlo simulations, as well as bootstrap analysis of in vivo human data, the optimal imaging and processing parameters for conducting the FORECAST analysis within typical clinical constraints were determined, and the accuracy of the model was estimated.
In order to compare HARDI measurements between subjects, an algorithm was developed to transform the fiber orientation distribution (FOD) function, based on HARDI data, taking into account not only translation, but also rotation, scaling, and shearing effects of the spatial transformation. The algorithm was tested using simulated data, and intra-subject and inter-subject normalization of in vivo human data. All cases demonstrate reliable transformation of the FOD.
A voxel-based group comparison of the radial diffusivity and intravoxel fiber coherence was performed based on FORECAST analysis of the HARDI images from both healthy controls and patients with schizophrenia. Decreased FA and elevated radial diffusivity were found in a number of white matter regions in patients. Our results suggest that increased radial diffusivity is the major contributor to the FA reduction, while decreased intravoxel fiber coherence also plays a role in the white matter alterations. This set of techniques, as a step forward from conventional DTI analysis, will likely be helpful in clinical studies of other white matter diseases as well. | |
