FSL

### BEDPOSTX

BEDPOSTX stands for Bayesian Estimation of Diffusion Parameters Obtained using Sampling Techniques. The X stands for modelling Crossing Fibres. bedpostx runs Markov Chain Monte Carlo sampling to build up distributions on diffusion parameters at each voxel. It creates all the files necessary for running probabilistic tractography. For an overview of the modelling carried out within bedpostx see the appendix.

bedpostx now allows to model crossing fibres within each voxel on the brain. Crucially, bedpostx allows to automatically determine the number of crossing fibres per voxel. For details on the model used in this case, see Behrens et al, NeuroImage 2007, 34:144-55.

bedpostx takes about 24 hours to run but will automatically batch if run on an SGE-capable system..

To call the FDT GUI, either run Fdt, or run fsl and press the FDT button. Use the top left drop down menu to select BEDPOSTX.

Input directory: Use the browse button to select an input directory. That directory must contain the following files:

- data: A 4D series of data volumes. This will include diffusion-weighted volumes and volume(s) with no diffusion weighting.
- nodif_brain_mask: 3D binary brain mask volume derived from running bet on nodif (i.e. on a volume with no diffusion weighting).
- bvecs: A text file containing a list of gradient directions applied during diffusion weighted volumes. The order of entries in this file must match the order of volumes in data.
  The format is

      x_1 x_2 x_3 ... x_n
      y_1 y_2 y_3 ... y_n
      z_1 z_2 z_3 ... z_n

  Vectors are normalised to unit length within the bedpostx code. For volumes in which there was no diffusion weighting, the entry should still be present, although the direction of the vector does not matter!
- bvals: A text file containing a list of bvalues applied during each volume acquisition. The order of entries in this file must match the order of volumes in the input data and entries in the gradient directions text file.
  The format is

      b_1 b_2 b_3 ... b_n

  The order of bvals must match the order of data.

Tip: Run bedpostx_datacheck in command line to check if your input directory contains the correct files required for bedpostx.

#### Outputs of BEDPOSTX

bedpostx creates a new directory at the same level as the input directory called <indir>.bedpostX which contains all the files you need for probabilistic tractography. Highlights are (<i> indicates the i-th fibre. It ranges from 1 to the maximum number of fibres set in the advanced options.):

- merged_th<i>samples - 4D volume - Samples from the distribution on theta
- merged_ph<i>samples - Samples from the distribution on phi

- theta and phi together represent the principal diffusion direction in spherical polar co-ordinates

- merged_f<i>samples - 4D volume - Samples from the distribution on anisotropic volume fraction (see technical report).
- mean_th<i>samples - 3D Volume - Mean of distribution on theta
- mean_ph<i>samples - 3D Volume - Mean of distribution on phi
- mean_f<i>samples - 3D Volume - Mean of distribution on f anisotropy

- Note that in each voxel, fibres are ordered according to a decreasing mean f-value

- mean_dsamples - 3D Volume - Mean of distribution on diffusivity d
- mean_S0samples - 3D Volume - Mean of distribution on S0 intensity
- dyads<i> - Mean of PDD distribution in vector form. Note that this file can be loaded into fslview for easy viewing of diffusion directions
- dyads<i>_dispersion - 3D Volume - Uncertainty on the estimated fibre orientation. Characterizes how wide the orientation distribution is around the respective dyad.
- nodif_brain - brain extracted version of nodif - copied from input directory
- nodif_brain_mask - binary mask created from nodif_brain - copied from input directory

#### Advanced Options

You may change some options before running bedpostx, depending on the questions you want to ask or the quality of your diffusion data. The default values of these parameters are the ones used in the corresponding paper (Behrens et al, NeuroImage 2007, 34:144-55).

- Fibres: Number of fibres modelled per voxel.
- Weight: Multiplicative factor for the prior on the additional modelled fibres. A smaller factor means more weighting for the additional fibres.
- Burnin: Number of iterations before starting the sampling. These might be increased if the data are noisy, and the MCMC needs more iterations to converge.

#### Command line utility

bedpostx <indir> [-n <nfibres>] [-w <weight>] [-b <niter>]