FawN

FSL analysis with NiPype

by Florian Krause

Introduction

FawN is a collection of NiPype workflows for building FSL-style fMRI analyisis pipelines.

Currently available workflows:

• "resampling"
  Resample images into size of reference image
• "timecourse_extraction"
  Extract timecourses from binary masks
• "first_level"
  Analysis of single functional runs
• "session_level"
  Analysis across all functional runs (average) of a single session (convenience workflow)
• "higher_level"
  Analysis across runs/sessions/subject
• "thresholding"
  Thresholding of results on voxel-level (FWE corrected) and cluster-level

The workflows expect preprocessed images (see also https://github.com/can-lab/finish-the-job).

Prerequisites

1. Install FSL
2. Install nipype with

   pip3 install nipype fslpy
   

3. Download FawN
4. Install with

   pip3 install FawN-X.X.X.zip
   

   (replace X.X.X with latest release version)

Donders cluster

If you are working on the compute cluster of the Donders Institute, please follow the following steps:

1. Load Anaconda3 module by running command: module load anaconda3
2. Create new environment in home directory by running command: cd && python3 -m venv fawn_env
3. Activate new environment by running command: source fawn_env/bin/activate
4. Install Nipype into environment by running command: pip3 install nipype fslpy
5. Download FawN
6. Install with

   pip3 install FawN-X.X.X.zip
   

   (replace X.X.X with latest release version)

Usage

See examples.

Donders cluster

If you are working on the compute cluster of the Donders Institute, please follow the following steps:

1. Start a new interactive job by running command: qsub -I -l 'procs=8, mem=64gb, walltime=24:00:00'
2. Load Anaconda3 module by running command: module load anaconda3
3. Activate environment by running command: source fawn_env/bin/activate
4. Write script mystudy_fawn.py (see examples)
5. Run script by running command: python3 mystudy_fawn.py