pipeline_config_rbc-options.yml

%YAML 1.1
---
# CPAC Pipeline Configuration YAML file
# Version 1.8.8.dev2
#
# http://fcp-indi.github.io for more info.
#
# Tip: This file can be edited manually with a text editor for quick modifications.
FROM: fmriprep-options

pipeline_setup:

  # Name for this pipeline configuration - useful for identification.
  # This string will be sanitized and used in filepaths
  pipeline_name: RBCv0
  output_directory:

    # Quality control outputs
    quality_control:

      # Generate eXtensible Connectivity Pipeline-style quality control files
      generate_xcpqc_files: On

  system_config:

    # Stop worklow execution on first crash?
    fail_fast: On

    # Random seed used to fix the state of execution.
    # If unset, each process uses its own default.
    # If set, a `random.log` file will be generated logging the random seed and each node to which that seed was applied.
    # If set to a positive integer (up to 2147483647), that integer will be used to seed each process that accepts a random seed.
    # If set to 'random', a random positive integer (up to 2147483647) will be generated and that seed will be used to seed each process that accepts a random seed.
    random_seed: 77742777

registration_workflows:
  anatomical_registration:

    # Template to be used during registration.
    # It is not necessary to change this path unless you intend to use a non-standard template.
    T1w_brain_template: $FSLDIR/data/standard/MNI152_T1_${resolution_for_anat}_brain.nii.gz

    # Template to be used during registration.
    # It is not necessary to change this path unless you intend to use a non-standard template.
    T1w_template: $FSLDIR/data/standard/MNI152_T1_${resolution_for_anat}.nii.gz

    # Template to be used during registration.
    # It is not necessary to change this path unless you intend to use a non-standard template.
    T1w_brain_template_mask: $FSLDIR/data/standard/MNI152_T1_${resolution_for_anat}_brain_mask.nii.gz

  functional_registration:
    func_registration_to_template:
      output_resolution:

        # The resolution (in mm) to which the preprocessed, registered functional timeseries outputs are written into.
        # NOTE:
        #   selecting a 1 mm or 2 mm resolution might substantially increase your RAM needs- these resolutions should be selected with caution.
        #   for most cases, 3 mm or 4 mm resolutions are suggested.
        # NOTE:
        #   this also includes the single-volume 3D preprocessed functional data,
        #   such as the mean functional (mean EPI) in template space
        func_preproc_outputs: 2mm

        # 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

      target_template:
        T1_template:

          # Standard Skull Stripped Template. Used as a reference image for functional registration.
          # This can be different than the template used as the reference/fixed for T1-to-template registration.
          T1w_brain_template_funcreg: $FSLDIR/data/standard/MNI152_T1_${func_resolution}_brain.nii.gz

          # Standard Anatomical Brain Image with Skull.
          # This can be different than the template used as the reference/fixed for T1-to-template registration.
          T1w_template_funcreg: $FSLDIR/data/standard/MNI152_T1_${func_resolution}.nii.gz

          # Template to be used during registration.
          # It is not necessary to change this path unless you intend to use a non-standard template.
          T1w_brain_template_mask_funcreg: $FSLDIR/data/standard/MNI152_T1_${func_resolution}_brain_mask.nii.gz

          # a standard template for resampling if using float resolution
          T1w_template_for_resample: $FSLDIR/data/standard/MNI152_T1_${func_resolution}_brain.nii.gz

functional_preproc:
  distortion_correction:

    # using: ['PhaseDiff', 'Blip', 'Blip-FSL-TOPUP']
    #   PhaseDiff - Perform field map correction using a single phase difference image, a subtraction of the two phase images from each echo. Default scanner for this method is SIEMENS.
    #   Blip - Uses AFNI 3dQWarp to calculate the distortion unwarp for EPI field maps of opposite/same phase encoding direction.
    #   Blip-FSL-TOPUP - Uses FSL TOPUP to calculate the distortion unwarp for EPI field maps of opposite/same phase encoding direction.
    using: [PhaseDiff, Blip-FSL-TOPUP]

  func_masking:
    FSL_AFNI:
      brain_mask: $FSLDIR/data/standard/MNI152_T1_${resolution_for_anat}_brain_mask.nii.gz
      brain_probseg: $FSLDIR/data/standard/MNI152_T1_${resolution_for_anat}_brain_mask.nii.gz

  truncation:

    # First timepoint to include in analysis.
    # Default is 0 (beginning of timeseries).
    # First timepoint selection in the scan parameters in the data configuration file, if present, will over-ride this selection.
    # Note: the selection here applies to all scans of all participants.
    start_tr: 2

  despiking:

    # Run AFNI 3dDespike
    # this is a fork point
    #   run: [On, Off] - this will run both and fork the pipeline
    run: [On]
    space: template

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: 36_parameter
        Bandpass:
          bottom_frequency: 0.01
          top_frequency: 0.1
        CerebrospinalFluid:
          erode_mask: Off
          extraction_resolution: 2
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
          summary: Mean
        GlobalSignal:
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
          summary: Mean
        Motion:
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
        WhiteMatter:
          erode_mask: Off
          extraction_resolution: 2
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
          summary: Mean
      - Name: aCompCor
        Bandpass:
          bottom_frequency: 0.01
          top_frequency: 0.1
        CerebrospinalFluid:
          erode_mask: Off
          extraction_resolution: 2
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
          summary: Mean
        aCompCor:
          summary:
            method: DetrendPC
            components: 5
          tissues:
          - WhiteMatter
          - CerebrospinalFluid
          extraction_resolution: 2
        Motion:
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
        WhiteMatter:
          erode_mask: Off
          extraction_resolution: 2
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
          summary: Mean

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

        # Target volume ratio, if using erosion.
        # Default proportion is None for cerebrospinal fluid mask.
        # If using erosion, using both proportion and millimeters is not recommended.
        csf_erosion_prop: 0.9

timeseries_extraction:
  run: On
  connectivity_matrix:

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

  # 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:
    /ndmg_atlases/label/Human/AAL_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/Brodmann_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/Glasser_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/Slab907_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/HarvardOxfordcort-maxprob-thr25_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/HarvardOxfordsub-maxprob-thr25_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/Juelich_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /cpac_templates/CC200.nii.gz: Avg
    /cpac_templates/CC400.nii.gz: Avg
    /cpac_templates/Schaefer2018_space-FSLMNI152_res-2mm_desc-200Parcels17NetworksOrder.nii.gz: Avg
    /cpac_templates/Schaefer2018_space-FSLMNI152_res-2mm_desc-300Parcels17NetworksOrder.nii.gz: Avg
    /cpac_templates/Schaefer2018_space-FSLMNI152_res-2mm_desc-400Parcels17NetworksOrder.nii.gz: Avg
    /cpac_templates/Schaefer2018_space-FSLMNI152_res-2mm_desc-1000Parcels17NetworksOrder.nii.gz: Avg
    /ndmg_atlases/label/Human/Yeo-17-liberal_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/Yeo-17_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/Yeo-7-liberal_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
    /ndmg_atlases/label/Human/Yeo-7_space-MNI152NLin6_res-2x2x2.nii.gz: Avg

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: [Template]

regional_homogeneity:

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

  # space: Template or Native
  target_space: [Template]

network_centrality:

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

  # Full path to a NIFTI file describing the mask. Centrality will be calculated for all voxels within the mask.
  template_specification_file: /cpac_templates/Mask_ABIDE_85Percent_GM.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]

  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: []

  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]

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

    # Smooth the derivative outputs.
    # Set as ['nonsmoothed'] to disable smoothing. Set as ['smoothed', 'nonsmoothed'] to get both.
    #
    # Options:
    #     ['smoothed', 'nonsmoothed']
    output: [smoothed, nonsmoothed]

    # Tool to use for smoothing.
    # 'FSL' for FSL MultiImageMaths for FWHM provided
    # 'AFNI' for AFNI 3dBlurToFWHM for FWHM provided
    smoothing_method: [AFNI]

    # Full Width at Half Maximum of the Gaussian kernel used during spatial smoothing.
    # this is a fork point
    #   i.e. multiple kernels - fwhm: [4,6,8]
    fwhm: [6]

  z-scoring:

    # z-score standardize the derivatives. This may be needed for group-level analysis.
    # Set as ['raw'] to disable z-scoring. Set as ['z-scored', 'raw'] to get both.
    #
    # Options:
    #     ['z-scored', 'raw']
    output: [z-scored, raw]