7.7 VA Reading - Smith 2006 NeuroImage (cont.)

Smith SM, Jenkinson M, Johansen-Berg H, Rueckert D, Nichols TE, Mackay CE, Watkins KE, Ciccarelli O, Cader MZ, Matthews PM, Behrens TEJ (2006). Tract-based spatial statistics: Voxelwise analysis of multi-subject diffusion data. NeuroImage; 31: 1487-505. 

Future directions
Use all available diffusion tensor information, rather than just FA, both in the alignment in preprocessing stage as well as feed into the final statistics. The improvement in accuracy may be modest, though.Can also include other imaging (such as T1-weighted structural images) to help drive the alignment. DTI data may suffer from low SNR, so instead of using FA map to drive the nonlinear registration, using T1-weighted images can qualitatively improve registration robustness. Can also be the segmented white matter image (from T1-weighted image) for registration, which may reduce noise and improve resolution, but have less rich contrast information.
Can extend to other diffusion measurements (mean diffusivity, tensor eigenvalues, principal tract direction, etc.). Also, selecting maximum FA is not necessary, e.g. measure tract thickness.
Useful and fairly straightforward to define a standard-space skeleton. If one was not concerned about inter-group biases resulting from such a predefined space, a standard-space mean FA image and derived skeleton could simplify TBSS analyses.
There is no reason why one has to carry out the cross-subject statistics separately for each voxel.