Fig. 2.

Preprocessing and analysis pipeline for diffusion-weighted parcellation of BA10. a Diffusion data are eddy-current corrected and registered to the B0 image. Next probability density functions on up to two principal fibre directions were estimated at each voxel in the brain using the Bayesian estimation of diffusion parameters obtained using sampling techniques toolbox (BEDPOSTX) implemented in FSL. Diffusion data were also co-registered to a T1-weighted anatomical scan. Next, probabilistic tractography was run from every voxel in the BA10 seed (registered to each subject’s diffusion space) to the rest of the brain, in a lower resolution brain (voxel size 5 × 5 × 5 mm). This resulted in a matrix of the probability of connection of every voxel in the seed to every other voxel in the brain. b These matrices have been cross-correlated and clustered according to a K-factor, which represents the number of clusters output by the algorithm. The parcellations are shown on the T1-weighted MNI standard brain in FSL