pipeline_config_benchmark-ANTS.yml

%YAML 1.1
---
# CPAC Pipeline Configuration YAML file
# Version 1.8.5
#
# http://fcp-indi.github.io for more info.
#
# Tip: This file can be edited manually with a text editor for quick modifications.
FROM: blank

pipeline_setup:

  # Name for this pipeline configuration - useful for identification.
  # This string will be sanitized and used in filepaths
  pipeline_name: benchmark-ANTS
  output_directory:

    # Quality control outputs
    quality_control:

      # Generate quality control pages containing preprocessing and derivative outputs.
      generate_quality_control_images: On

    # Include extra versions and intermediate steps of functional preprocessing in the output directory.
    write_func_outputs: On

    # Include extra outputs in the output directory that may be of interest when more information is needed.
    write_debugging_outputs: On

  system_config:

    # Select Off if you intend to run CPAC on a single machine.
    # If set to On, CPAC will attempt to submit jobs through the job scheduler / resource manager selected below.
    on_grid:
      SGE:

        # SGE Parallel Environment to use when running CPAC.
        # Only applies when you are running on a grid or compute cluster using SGE.
        parallel_environment: cpac

    # The maximum amount of memory each participant's workflow can allocate.
    # Use this to place an upper bound of memory usage.
    # - Warning: 'Memory Per Participant' multiplied by 'Number of Participants to Run Simultaneously'
    #   must not be more than the total amount of RAM.
    # - Conversely, using too little RAM can impede the speed of a pipeline run.
    # - It is recommended that you set this to a value that when multiplied by
    #   'Number of Participants to Run Simultaneously' is as much RAM you can safely allocate.
    maximum_memory_per_participant: 3

    # The maximum amount of cores (on a single machine) or slots on a node (on a cluster/grid)
    # to allocate per participant.
    # - Setting this above 1 will parallelize each participant's workflow where possible.
    #   If you wish to dedicate multiple cores to ANTS-based anatomical registration (below),
    #   this value must be equal or higher than the amount of cores provided to ANTS.
    # - The maximum number of cores your run can possibly employ will be this setting multiplied
    #   by the number of participants set to run in parallel (the 'Number of Participants to Run
    #   Simultaneously' setting).
    max_cores_per_participant: 2

    # The number of cores to allocate to ANTS-based anatomical registration per participant.
    # - Multiple cores can greatly speed up this preprocessing step.
    # - This number cannot be greater than the number of cores per participant.
    num_ants_threads: 2

  working_directory:

    # Deletes the contents of the Working Directory after running.
    # This saves disk space, but any additional preprocessing or analysis will have to be completely re-run.
    remove_working_dir: Off

anatomical_preproc:
  run: On
  acpc_alignment:
    T1w_brain_ACPC_template: $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz

  brain_extraction:
    run: On
    FSL-BET:

      # Mask created along with skull stripping. It should be `On`, if selected functionalMasking :  ['Anatomical_Refined'] and `FSL` as skull-stripping method.
      mask_boolean: Off

segmentation:

  # Automatically segment anatomical images into white matter, gray matter,
  # and CSF based on prior probability maps.
  run: On
  tissue_segmentation:
    Template_Based:

      # These masks should be in the same space of your registration template, e.g. if
      # you choose 'EPI Template' , below tissue masks should also be EPI template tissue masks.
      #
      # Options: ['T1_Template', 'EPI_Template']
      template_for_segmentation: []

registration_workflows:
  anatomical_registration:
    run: On

    # Register skull-on anatomical image to a template.
    reg_with_skull: Off

  functional_registration:
    coregistration:

      # functional (BOLD/EPI) registration to anatomical (structural/T1)
      run: On
      boundary_based_registration:

        # this is a fork point
        #   run: [On, Off] - this will run both and fork the pipeline
        run: [On]

    func_registration_to_template:

      # these options modify the application (to the functional data), not the calculation, of the
      # T1-to-template and EPI-to-template transforms calculated earlier during registration
      # apply the functional-to-template (T1 template) registration transform to the functional data
      run: On
      output_resolution:

        # The resolution (in mm) to which the registered derivative outputs are written into.
        # NOTE:
        #   this is for the single-volume functional-space outputs (i.e. derivatives)
        #   thus, a higher resolution may not result in a large increase in RAM needs as above
        func_derivative_outputs: 2mm

functional_preproc:
  run: On
  slice_timing_correction:

    # Interpolate voxel time courses so they are sampled at the same time points.
    # this is a fork point
    #   run: [On, Off] - this will run both and fork the pipeline
    run: [On]

  motion_estimates_and_correction:
    run: On

  distortion_correction:

    # this is a fork point
    #   run: [On, Off] - this will run both and fork the pipeline
    run: [On]

  generate_func_mean:

    # Generate mean functional image
    run: On

  normalize_func:

    # Normalize functional image
    run: On

nuisance_corrections:
  2-nuisance_regression:

    # this is a fork point
    #   run: [On, Off] - this will run both and fork the pipeline
    run: [On]

    # Select which nuisance signal corrections to apply
    Regressors:
      - Name: Regressor-1
        Bandpass:
          bottom_frequency: 0.01
          top_frequency: 0.1
        CerebrospinalFluid:
          extraction_resolution: 2
          summary: Mean
        Motion:
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
        PolyOrt:
          degree: 1
        aCompCor:
          extraction_resolution: 2
          summary:
            components: 5
            method: DetrendPC
          tissues:
          - WhiteMatter
          - CerebrospinalFluid
      - Name: Regressor-2
        CerebrospinalFluid:
          erode_mask: On
          extraction_resolution: 2
          summary: Mean
        GlobalSignal:
          summary: Mean
        Motion:
          include_delayed: On
          include_delayed_squared: Off
          include_squared: On
        aCompCor:
          extraction_resolution: 2
          summary:
            components: 5
            method: DetrendPC
          tissues:
          - CerebrospinalFluid
      - Name: Regressor-3
        Censor:
          method: Kill
          number_of_previous_trs_to_censor: 1
          number_of_subsequent_trs_to_censor: 1
          thresholds:
          - type: FD_J
            value: 0.3
        Motion:
          include_delayed: On
          include_delayed_squared: Off
          include_squared: Off

    # Process and refine masks used to produce regressors and time series for
    # regression.
    regressor_masks:
      erode_anatomical_brain_mask:

        # Erode brain mask in millimeters, default for brain mask is 30 mm
        # Brain erosion default is using millimeters.
        brain_mask_erosion_mm: 30

      erode_csf:

        # Erode cerebrospinal fluid mask in millimeters, default for cerebrospinal fluid is 30mm
        # Cerebrospinal fluid erosion default is using millimeters.
        csf_mask_erosion_mm: 30

      erode_wm:

        # Target volume ratio, if using erosion.
        # Default proportion is 0.6 for white matter mask.
        # If using erosion, using both proportion and millimeters is not recommended.
        # White matter erosion default is using proportion erosion method when use erosion for white matter.
        wm_erosion_prop: 0.6

      erode_gm:

        # Target volume ratio, if using erosion.
        # If using erosion, using both proportion and millimeters is not recommended.
        gm_erosion_prop: 0.6

  1-ICA-AROMA:

    # this is a fork point
    #   run: [On, Off] - this will run both and fork the pipeline
    run: [On, Off]

timeseries_extraction:
  run: On
  connectivity_matrix:

    # Create a connectivity matrix from timeseries data
    # Options:
    #  ['AFNI', 'Nilearn', 'ndmg']
    using: [Nilearn, ndmg]

    # Options:
    #  ['Pearson', 'Partial']
    # Note: These options are not configurable for ndmg, which will ignore these options
    measure: [Pearson, Partial]

  # Enter paths to region-of-interest (ROI) NIFTI files (.nii or .nii.gz) to be used for time-series extraction, and then select which types of analyses to run.
  # Denote which analyses to run for each ROI path by listing the names below. For example, if you wish to run Avg and SpatialReg, you would enter: '/path/to/ROI.nii.gz': Avg, SpatialReg
  # available analyses:
  #   /path/to/atlas.nii.gz: Avg, Voxel, SpatialReg
  tse_roi_paths:
    s3://fcp-indi/resources/cpac/resources/rois_2mm.nii.gz: Avg, Voxel, SpatialReg

amplitude_low_frequency_fluctuation:

  # ALFF & f/ALFF
  # Calculate Amplitude of Low Frequency Fluctuations (ALFF) and fractional ALFF (f/ALFF) for all voxels.
  run: On

  # space: Template or Native
  target_space: [Native]

regional_homogeneity:

  # ReHo
  # Calculate Regional Homogeneity (ReHo) for all voxels.
  run: On

  # space: Template or Native
  target_space: [Native]

voxel_mirrored_homotopic_connectivity:

  # VMHC
  # Calculate Voxel-mirrored Homotopic Connectivity (VMHC) for all voxels.
  run: On

network_centrality:

  # Calculate Degree, Eigenvector Centrality, or Functional Connectivity Density.
  run: On

  # Maximum amount of RAM (in GB) to be used when calculating Degree Centrality.
  # Calculating Eigenvector Centrality will require additional memory based on the size of the mask or number of ROI nodes.
  memory_allocation: 3.0

  # Full path to a NIFTI file describing the mask. Centrality will be calculated for all voxels within the mask.
  template_specification_file: s3://fcp-indi/resources/cpac/resources/mask-thr50-3mm.nii.gz
  degree_centrality:

    # Enable/Disable degree centrality by selecting the connectivity weights
    #   weight_options: ['Binarized', 'Weighted']
    # disable this type of centrality with:
    #   weight_options: []
    weight_options: [Binarized, Weighted]

  eigenvector_centrality:

    # Enable/Disable eigenvector centrality by selecting the connectivity weights
    #   weight_options: ['Binarized', 'Weighted']
    # disable this type of centrality with:
    #   weight_options: []
    weight_options: [Binarized, Weighted]

  local_functional_connectivity_density:

    # Enable/Disable lFCD by selecting the connectivity weights
    #   weight_options: ['Binarized', 'Weighted']
    # disable this type of centrality with:
    #   weight_options: []
    weight_options: [Binarized, Weighted]

    # Select the type of threshold used when creating the lFCD adjacency matrix.
    # options:
    #   'Significance threshold', 'Correlation threshold'
    correlation_threshold_option: Significance threshold

    # Based on the Threshold Type selected above, enter a Threshold Value.
    # P-value for Significance Threshold
    # Sparsity value for Sparsity Threshold
    # Pearson's r value for Correlation Threshold
    correlation_threshold: 0.001

# OUTPUTS AND DERIVATIVES
# -----------------------
post_processing:
  spatial_smoothing:
    run: On

  z-scoring:
    run: On

seed_based_correlation_analysis:

  # Enter paths to region-of-interest (ROI) NIFTI files (.nii or .nii.gz) to be used for seed-based correlation analysis, and then select which types of analyses to run.
  # Denote which analyses to run for each ROI path by listing the names below. For example, if you wish to run Avg and MultReg, you would enter: '/path/to/ROI.nii.gz': Avg, MultReg
  # available analyses:
  #   /path/to/atlas.nii.gz: Avg, DualReg, MultReg
  sca_roi_paths:
    s3://fcp-indi/resources/cpac/resources/rois_2mm.nii.gz: Avg, MultReg