The diffusion anisotropy (DA) in the brain measured by diffusion tensor imaging (DTI) is related to white matter (WM) structure and is thus an indicator for changes associated with degenerative WM diseases. Estimation of DA is also a necessary precursor to fiber tract mapping (FTM), which holds the potential for understanding brain connectivity. In DTI, signal variations as a function of encoding direction are related to the local tissue diffusion (LTD) from which DA is estimated. In standard DTI, DA is based upon a simplified LTD model requiring only a few encoding directions. But recent work has shown that LTD complexity may not be well fit by the standard model and high angular resolution diffusion (HARD) and multiple diffusion weightings (q-space) are needed to better characterize LTD. This poses three major problems: 1) Analysis and display of HARD and q-space data, and DA and FTM results, is complicated;2) FTM with HARD data is no longer possible by simple "streamline" methods, and 3) Quantitation of errors in DA and FTM methods is difficult. We have recently developed a DTI analysis plugin to the program AFNI (Analysis of Functional Neurolmages). The primary goal of the current proposal is to further develop this software to address these problems to provide a DTI analysis and visualization platform accessible to all neuroscientists. Our first specific aim is to fully integrate, document, and "productize" our plugin, for dissemination in the AFNI distribution. Also, we have generalized DTI to the spherical harmonic decomposition (SHD), to incorporate more complex LTD models. The second specific aim is to develop rapid, accurate computation and display of the SHD, and our extension to q-space, the spherical wave decomposition (SWD), to produce more accurate DA maps. Our third specific aim is to develop FTM routines based on partial differential equation (PDE) diffusion equation solutions, augmented with prior information, with user-defined "seed" locations, and display within high-resolution anatomical data using advanced visualization methods. The fourth specific aim is to quantitate and display errors in DA and FTM. Motivated by our ongoing work showing changes in WM DA in alcohol abuse patients, and our recent work in fetal alcohol syndrome, Alzheimer's, and language disorders, our ultimate long term goal is to produce a quantitative DTI analysis and visualization tool to aid neuroscientists in their ongoing assessment of degenerate white matter diseases.