Figure 1.

a, In DTI, a set of diffusion-sensitized images is acquired, with diffusion-sensitizing magnetic field gradients of the same strength, but oriented in different directions on an imaginary sphere. Each voxel in DTI contains multidimensional data (in our study, the dimension is 27). For example, b shows the raw diffusion data from a single voxel (the pink point in a) in all diffusion-weighted images from one of the subjects. Water diffusion is antipodally symmetric along a specific direction, so one diffusion-sensitized image gives a pair of data points with identical values on the sphere. Each point on the sphere indicates the MR signal attenuation caused by water diffusion in the direction of the corresponding diffusion gradient, with greater signal attenuation (color-coded as red; magnitude of MR signal attenuation increases from yellow to red) indicating more rapid rates of water diffusion along that gradient direction. Water diffusion can be estimated by fitting the raw diffusion data in b to a diffusion ellipsoid (c), whose axes correspond to the eigenvalue-eigenvector pairs of a diffusion tensor (Basser and Pierpaoli, 1996). An image of the diffusion tensors estimated from all the voxels of the raw diffusion data is displayed in d (see Fig. 2 for details). Analysis of DTI scans is computationally expensive due to their high information content, especially for the covariance structure analysis in twin studies, so it is more practical to first estimate anisotropy maps of scalar values, such as FA (e).