Ensemble Tractography

This page describes how to use Ensemble Tractography (ET) for identifying white matter fascicles from diffusion MRI data. ET is an idea proposed by Takemura, Caiafa, Wandell & Pestilli (2016) PLoS Comput Biol.

This page is still under construction. Final goal of this wiki is to describe the detailed instructions with sample dataset and scripts for performing ET analysis.

System Requirements

ET is an extension of the LiFE Pestilli et al, 2014 Nat Methods. Recent LiFE release includes specific functions for performing ET analyses.

For ET project, we are mostly developing all codes under Ubuntu 14.04 LTS or Ubuntu 12. We have tested basic LiFE codes worked under Mac OS X and Windows 8, but not extensively tested in many OS environments.

Required software packages

• MATLAB (2012b or later; not extensively tested in MATLAB2015b)
• Vistasoft
• LiFE

Optional packages

• MRTrix
• [Camino] (http://www.camino.org.uk)

Before starting

The ET analysis requires the preprocessed diffusion MRI dataset in nifti format, with b-value and b-vecs reorientation throughout the correction. Please see [the page] (http://web.stanford.edu/group/vista/cgi-bin/wiki/index.php/DTI_Preprocessing) describing the preprocessing methods in vistasoft.

For using MrTrix for tractography, you have to convert nifti files into MRTrix (.mif) format. Please see [this page] (https://github.com/vistalab/vistasoft/wiki/Use%20MrTrix%20with%20vistasoft) for an instruction.

Performing Tractography using multiple parameter settings

For performing ET, we have to run tractography using different parameter settings.

[feTrack] (https://github.com/francopestilli/life/blob/master/track/feTrack.m) includes option to use the different parameters, such as curvature threshold and stopping criterion.

Example MATLAB script to create five different connectomes using different curvature threshold:

  dtFile = '/data/humandata/diffusion/S1/dti64trilin/dt6.mat';
  fibersFolder = '/data/humandata/diffusion/S1/dti64trilin/fibers/ET_candidate;
  lmax = 8; % CSD parameter
  nSeeds = 500000; % Number of streamlines generated
  wmMask = '/data/humandata/diffusion/S1/dti64trilin/ROIs/t1_class_twovalued.mif';
  curvature = [0.25 0.5 1 2 4];
  for i = 1:length(curvature)
   feTrack('prob', dtFile,fibersFolder,lmax,nSeeds,wmMask,curvature(i))
  end

For controlling other MRTrix tractography parameters, see the instruction in [MRTrix wiki] (http://jdtournier.github.io/mrtrix-0.2/commands/streamtrack.html).

Coverting tractography results into vistasoft format

MRTrix produces streamlines in .tck format. The first step is to convert .tck format to .pdb format used in vistasoft.

In order to convert .tck file, please use please use mrtrix_tck2pdb.m in vistasoft.

mrtrix_tck2pdb('S1_SDPROB_lmax8_500000.tck', 'S1_SDPROB_lmax8_500000.pdb')

Creating candidate Ensemble Tractography Connectome

Once you converted each .tck file into .pdb file, the next step is concatenating the connectome to create a candidate Ensemble Tractography Connectome.

To do so, we could use [et_concatenateconnectomes.m] (https://github.com/francopestilli/life/blob/master/utility/ET/et_concatenateconnectomes.m) under LiFE distribution.

Example usage:

fginput = {'S1_LH_Occipital_curv0p25SPC_cand.pdb','S1_LH_Occipital_curv0p5SPC_cand.pdb',...
'S1_LH_Occipital_curv1SPC_cand.pdb','S1_LH_Occipital_curv2SPC_cand.pdb','S1_LH_Occipital_curv4SPC_cand.pdb'}
fname = 'S1_LH_Occipital_ETCrand_cand.mat';
et_concatenateconnectomes(fginput, fname) 

This function enables us to combine the connectome filed (.pdb files derived from different tractography parameters) into a single connectome file (in this case .mat format, but this can be .pdb format too).

Run LiFE

ET-preselection model

Computational load on ET

Sample dataset and script

Hiromasa Takemura is planning to organize the sample dataset to run example Ensemble Tractography analysis. To be announced.

Reference for Ensemble Tractography

Takemura H, Caiafa CF, Wandell BA, Pestilli F (2016) Ensemble Tractography. PLoS Comput Biol 12(2): e1004692. doi:10.1371/journal.pcbi.1004692