pipeline_config_fmriprep-ingress.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: blank

pipeline_setup:

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

    # Quality control outputs
    quality_control:

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

  outdir_ingress:
    run: On
    Template: MNI152NLin2009cAsym

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

  brain_extraction:

    # using: ['3dSkullStrip', 'BET', 'UNet', 'niworkflows-ants', 'FreeSurfer-ABCD', 'FreeSurfer-BET-Tight', 'FreeSurfer-BET-Loose', 'FreeSurfer-Brainmask']
    # this is a fork option
    using: [BET]
    FSL-BET:

      # Robust brain center estimation. Mutually exclusive with functional,reduce_bias,robust,padding,remove_eyes,surfaces
      robust: On

segmentation:
  tissue_segmentation:

    # option parameters
    FSL-FAST:
      use_priors:

        # Use template-space tissue priors to refine the binary tissue masks generated by segmentation.
        run: Off

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: default
        Bandpass:
          bottom_frequency: 0.01
          top_frequency: 0.1
          method: default

    # 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

    # this is not a fork point
    # Run nuisance regression in native or template space
    #   - If set to template, will use the brain mask configured in
    #     ``functional_preproc: func_masking: FSL_AFNI: brain_mask``
    #   - If ``registration_workflows: functional_registration: func_registration_to_template: apply_trasnform: using: single_step_resampling_from_stc``, this must be set to template
    space: template
    ingress_regressors:
      run: On
      Regressors:
        Columns: [global_signal, white_matter]

    # switch to Off if nuisance regression is off and you don't want to write out the regressors
    create_regressors: 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]

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
  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: [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]

# 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: [nonsmoothed]

  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: [raw]