Installation

Prerequisites

Before using the SML fMRI Preprocessing Template, ensure you have:

  • Access to a computing cluster with Slurm workload manager

  • Singularity/Apptainer for container execution

  • FreeSurfer license file

  • Git for version control

  • Python 3.6 or higher

Getting Started

  1. Create Repository from Template

    Click the β€œUse this template” button on the GitHub repository to create your own copy.

  2. Clone Your Repository

    git clone https://github.com/your-username/your-repo-name.git
cd your-repo-name

  3. Configure Settings

    Copy the settings template and customize for your study:

    cp settings.template.sh settings.sh
# Edit settings.sh with your study-specific parameters

  4. Set Up Subject List

    Create your subject list file:

    cp all-subjects.template.txt all-subjects.txt
# Add your subject IDs (one per line, just the number without "sub-" prefix)

  5. Verify Paths

    Ensure all paths in settings.sh are correct and accessible:

    • BASE_DIR - Your study’s root directory

    • RAW_DIR - BIDS-formatted raw data location

    • TRIM_DIR - Destination for processed data

    • FREESURFER_LICENSE - Path to FreeSurfer license file

System Requirements

Minimum Requirements:

  • 8 CPU cores per subject

  • 8GB RAM per subject

  • 100GB storage per subject (for preprocessed outputs)

Recommended:

  • 16 CPU cores per subject

  • 32GB RAM per subject

  • 200GB storage per subject

Software Dependencies:

  • Slurm workload manager

  • Singularity/Apptainer 3.0+

  • FreeSurfer (via container)

  • fMRIPrep (via container)

  • dcm2niix (via container or installed locally)

Next Steps

After installation, proceed to the Configuration guide to set up your preprocessing pipeline parameters.