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Tract based spatial statistics using ANTs and FSL

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DOI Python Platform

TBSS repository is developed by Tashrif Billah, Sylvain Bouix, and Ofer Pasternak, Brigham and Women's Hospital (Harvard Medical School).

If this repository is useful in your research, please cite as below:

Billah, Tashrif; Bouix, Sylvain; Pasternak, Ofer; Generalized Tract Based Spatial Statistics (TBSS) pipeline, https://github.com/pnlbwh/tbss, 2019, DOI: https://doi.org/10.5281/zenodo.2662497

See documentation for details.

Table of Contents

Table of Contents created by gh-md-toc

Dependencies

  • ANTs = 2.2.0
  • FSL = 5.0.11
  • numpy = 1.16.2
  • pandas = 1.2.1
  • dipy = 0.16.0
  • nibabel = 2.3.0
  • nilearn = 0.5.2
  • pynrrd = 0.3.6
  • conversion = 2.0

NOTE The above versions were used for developing the repository. However, tbss should work on any advanced version.

Installation

1. Install prerequisites

You may ignore installation instruction for any software module that you have already.

i. Check system architecture

uname -a # check if 32 or 64 bit

ii. Python 3

Download Miniconda Python 3.6 bash installer (32/64-bit based on your environment):

sh Miniconda3-latest-Linux-x86_64.sh -b # -b flag is for license agreement

Activate the conda environment:

source ~/miniconda3/bin/activate # should introduce '(base)' in front of each line

iii. FSL

Follow the instruction to download and install FSL.

iv. ANTs

(Preferred) You should install pre-complied ANTs from PNL-BWH:

conda install -c pnlbwh ants

Installation with conda is more manageable. It will put the ANTs commands/scripts in your path when you do:

source ~/miniconda3/bin/activate    

Alternatively, you can build ANTs from source.

2. Install pipeline

Now that you have installed the prerequisite software, you are ready to install the pipeline:

git clone https://github.com/pnlbwh/tbss && cd tbss
./install setup test

If you would not like to run tests, just omit the test argument. But it is recommended to run tests before you use this pipeline to analyze your data.

3. Configure your environment

Make sure the following executables are in your path:

antsMultivariateTemplateConstruction2.sh
antsApplyTransforms
antsRegistrationSyNQuick.sh
tbss_1_preproc

You can check them as follows:

which tbss_1_preproc

If any of them does not exist, add that to your path:

export PATH=$PATH:/directory/of/executable

ANTs commands should be in ~/miniconda3/pkgs/ants-2.3.0-py3/bin and/or ~/miniconda3/bin directories. If they are not in your path already, use export PATH=$PATH:~/miniconda3/pkgs/ants-2.3.0-py3/bin to put all the commands in your path. Additionally, you should define define ANTSPATH:

export ANTSPATH=~/miniconda3/bin

Running

Upon successful installation, you should be able to see the help message

$ lib/tbss-pnl --help

See Useful commands for quick tips about running the pipeline.

Tests

Test includes both pipeline test and unit tests. It is recommended to run tests before analyzing your data.

1. pipeline

The repository comes with separate testing for three branches: --enigma, --fmrib, and --studyTemplate:

./install.sh test

Running the tests should take less than an hour.

2. unittest

You may run smaller and faster unit tests as follows.

pytest -v lib/tests/test_*.py

NOTE In the current release, unit tests are dependant upon the outputs of whole pipeline test. This is likely to change in future.

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