In anisotropic tissue something interferes with the free diffusion of water molecules, for instance cell membranes or microtubles. This means that diffusion will be faster parallel to an axon and slower perpendicular to it. In DTI, the diffusion coefficient will miss these local effects and thus the diffusion coefficient will vary depending upon the orientation in which the tissue is measured. By measuring a given area of tissue (i.e., a voxel) from 6+ directions, you can describe the orientation of average axons in a vector. Following some fancy math, you can determine white matter pathways between voxels as well as connectivity probabilities.
Gong et al recently used diffusion tensor imaging on the whole brains of 80 right-handed young adults in 3-mm slice thickness (no gaps) for 40 overall slices from 6 diffusion directions with a b value of 1000 s/mm^2. Note higher b values lead to greater image contrast. They then interpolated the diffusion-weighted images into 1-mm isotropic dimensions. They partitioned the cerebral cortex into 78 cortical regions and restricted the trajectory of fiber bundles to white matter voxels to evaluate their connectivity to the adjacent cortical region. They then counted number of fiber bundles connecting each pair of regions and focused on the connections consistent across their subjects, to account for the variability in brain anatomy between individuals.
The researchers found 329 statistically significant anatomical connections between cortical regions out of 3003 potential between-region connections, a “sparsity” of 11%. They also identified the nodes and edges in their network that have betweeness values 1 SD above the mean. Betweeness is a measure of how often that vertex occurs on the shortest path between other vertices, and its relative importance to the network. Kind of cool because they can evaluate whether those vertices have also been shown to be important in previous non-DTI studies.
DTI will be a big part of the forthcoming human connectome project of the NIH. Resolution on the individual neuron scale is considered unrealistic by many, as Gong et al noted in their paper, and DTI is a viable alternative. Next steps would be connecting structure more with function, determining changes to the anatomy as a result of neurodegenerative diseases, and fixing methodological snags. DTI in the brain is poised to be very useful in the coming decades.
Gong G, et al. 2009 Mapping anatomical connectivity patterns of human cerebral cortex using in vivo diffusion tensor imaging tractography. Cerebral Cortex 19:524-536.