pipeline_config_ccs-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: blank

pipeline_setup:

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

  system_config:

    # 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: 10.0

# PREPROCESSING
# -------------
surface_analysis:

  # Will run Freesurfer for surface-based analysis. Will output traditional Freesurfer derivatives.
  # If you wish to employ Freesurfer outputs for brain masking or tissue segmentation in the voxel-based pipeline,
  # select those 'Freesurfer-' labeled options further below in anatomical_preproc.
  freesurfer:
    run_reconall: On

    # Add extra arguments to recon-all command
    reconall_args: -clean-bm -gcut

    # Ingress freesurfer recon-all folder
    ingress_reconall: On

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

  brain_extraction:
    run: On
    FreeSurfer-BET:

      # Template to be used for FreeSurfer-BET brain extraction in CCS-options pipeline
      T1w_brain_template_mask_ccs: /code/CPAC/resources/templates/MNI152_T1_1mm_first_brain_mask.nii.gz

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

  # Non-local means filtering via ANTs DenoiseImage
  non_local_means_filtering:

    # this is a fork option
    run: [On]

segmentation:

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

      # Use mri_binarize --erode option to erode segmentation masks
      erode: 1

      # Label values corresponding to CSF in FreeSurfer aseg segmentation file
      CSF_label: [4, 5, 43, 44, 31, 63]

      # Label values corresponding to White Matter in FreeSurfer aseg segmentation file
      WM_label: [2, 41, 7, 46, 251, 252, 253, 254, 255]

registration_workflows:
  anatomical_registration:
    run: On
    registration:
      FSL-FNIRT:

        # Interpolation method for writing out transformed anatomical images.
        # Possible values: trilinear, sinc, spline
        interpolation: trilinear

      # using: ['ANTS', 'FSL', 'FSL-linear']
      # this is a fork point
      #   selecting both ['ANTS', 'FSL'] will run both and fork the pipeline
      using: [FSL]

    # 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
      func_input_prep:

        # Choose whether to use the mean of the functional/EPI as the input to functional-to-anatomical registration or one of the volumes from the functional 4D timeseries that you choose.
        # input: ['Mean_Functional', 'Selected_Functional_Volume', 'fmriprep_reference']
        input: [Selected_Functional_Volume]
        Selected Functional Volume:

          # Only for when 'Use as Functional-to-Anatomical Registration Input' is set to 'Selected Functional Volume'.
          #Input the index of which volume from the functional 4D timeseries input file you wish to use as the input for functional-to-anatomical registration.
          func_reg_input_volume: 7

      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
      FNIRT_pipelines:

        # Interpolation method for writing out transformed functional images.
        # Possible values: trilinear, sinc, spline
        interpolation: trilinear

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]

    # use specified slice time pattern rather than one in header
    tpattern: alt+z

    # align each slice to given time offset
    # The default alignment time is the average of the 'tpattern' values (either from the dataset header or from the tpattern option).
    tzero: 0

  motion_estimates_and_correction:
    run: On
    motion_correction:

      # using: ['3dvolreg', 'mcflirt']
      # Forking is currently broken for this option.
      # Please use separate configs if you want to use each of 3dvolreg and mcflirt.
      # Follow https://github.com/FCP-INDI/C-PAC/issues/1935 to see when this issue is resolved.
      using: [3dvolreg]

  distortion_correction:

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

  func_masking:
    run: On

    # using: ['AFNI', 'FSL', 'FSL_AFNI', 'Anatomical_Refined', 'Anatomical_Based', 'Anatomical_Resampled', 'CCS_Anatomical_Refined']
    # FSL_AFNI: fMRIPrep-style BOLD mask. Ref: https://github.com/nipreps/niworkflows/blob/a221f612/niworkflows/func/util.py#L246-L514
    # Anatomical_Refined: 1. binarize anat mask, in case it is not a binary mask. 2. fill holes of anat mask 3. init_bold_mask : input raw func → dilate init func brain mask 4. refined_bold_mask : input motion corrected func → dilate anatomical mask 5. get final func mask
    # Anatomical_Based: Generate the BOLD mask by basing it off of the anatomical brain mask. Adapted from DCAN Lab's BOLD mask method from the ABCD pipeline.
    # Anatomical_Resampled: Resample anatomical brain mask in standard space to get BOLD brain mask in standard space. Adapted from DCAN Lab's BOLD mask method from the ABCD pipeline. ("Create fMRI resolution standard space files for T1w image, wmparc, and brain mask […] don't use FLIRT to do spline interpolation with -applyisoxfm for the 2mm and 1mm cases because it doesn't know the peculiarities of the MNI template FOVs")
    # CCS_Anatomical_Refined: Generate the BOLD mask by basing it off of the anatomical brain. Adapted from the BOLD mask method from the CCS pipeline.
    # this is a fork point
    using: [CCS_Anatomical_Refined]

  generate_func_mean:

    # Generate mean functional image
    run: On

  normalize_func:

    # Normalize functional image
    run: On

  despiking:

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

  coreg_prep:

    # Generate sbref
    run: On

nuisance_corrections:
  2-nuisance_regression:

    # Select which nuisance signal corrections to apply
    Regressors:
      - Name: Regressor_1
        Motion:
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
        GlobalSignal:
          summary: Mean
        CerebrospinalFluid:
          erode_mask: Off
          extraction_resolution: 2
          summary: Mean
        WhiteMatter:
          erode_mask: Off
          extraction_resolution: 2
          summary: Mean
        PolyOrt:
          degree: 1
        Bandpass:
          bottom_frequency: 0.01
          top_frequency: 0.1
          method: AFNI
      - Name: Regressor_2
        Motion:
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
        CerebrospinalFluid:
          erode_mask: Off
          extraction_resolution: 2
          summary: Mean
        WhiteMatter:
          erode_mask: Off
          extraction_resolution: 2
          summary: Mean
        PolyOrt:
          degree: 1
        Bandpass:
          bottom_frequency: 0.01
          top_frequency: 0.1
          method: AFNI

    # 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

timeseries_extraction:
  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:

  # space: Template or Native
  target_space: [Native]

regional_homogeneity:

  # space: Template or Native
  target_space: [Native]

# 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:
    /cpac_templates/CC400.nii.gz: Avg