This paper specifically identifies and characterizes the U-shapes of diffusion imaging derived streamline fibers via a novel fiber clustering framework and examines their co-localization patterns with cortical sulci based on DTI, HARDI, and DSI datasets of human, chimpanzee and macaque brains.
Diffusion tensor imaging (DTI) analyses the three-dimensional shape of the diffusion, also known as diffusion tensor.
We observed that the effect of molecular size and shape on diffusion is characterized by an effective molecular volume, Veff, instead of the liquid volume at 0 K. Veff is calculated from an effective molecular diameter which is obtained by applying a weighting function over the three dimensions of the solvent molecule.
FA is defined as the ratio of the anisotropic component of the diffusion tensor to the whole diffusion tensor and serves as a rotationally invariant scalar that quantifies the shape of the diffusion tensor.
Attempts to use statistical analysis of the shape of the diffusion curve to infer the presence of social and asocial learning have been discredited [15], [16].
However, if morphological changes of glial cells underlie the measured signal changes then changes in the shape of the diffusion profile (reflected by the FA) would be more probable then changes in mean diffusivity.
If the capillaries in white matter have a preferential direction then the changes in blood flow within a voxel may be anisotropic an alter the shape of the diffusion profile, which would be reflected by a change in FA.
FA is useful to characterize the shape of the diffusion ellipsoid.
The case of a self-similar surface is presented in detail, and numerical results for an electrode in the shape of a diffusion-limited aggregate (DLA) are shown.
The three-dimensional shape and magnitude of diffusion differ between various brain structures.
