pipeline_config_regtest-4.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: regtest-3
  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: 3

    # 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: 3

    # The number of participant workflows to run at the same time.
    # - The maximum number of cores your run can possibly employ will be this setting
    #   multiplied by the number of cores dedicated to each participant (the 'Maximum Number of Cores Per Participant' setting).
    num_participants_at_once: 15

  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

    # option parameters
    AFNI-3dSkullStrip:

      # Set the threshold value controlling the brain vs non-brain voxels. Default is 0.6.
      shrink_factor: 0.62

      # The shrink factor bottom limit sets the lower threshold when varying the shrink factor. Default is 0.4, for when edge detection is used (which is On by default), otherwise the default value is 0.65.
      shrink_factor_bot_lim: 0.42

      # Set the number of iterations. Default is 250.The number of iterations should depend upon the density of your mesh.
      n_iterations: 252

      # Perform nearest neighbor coordinate interpolation every few iterations. Default is 72.
      NN_smooth: 74

      # Perform final surface smoothing after all iterations. Default is 20.
      smooth_final: 22

  # Non-local means filtering via ANTs DenoiseImage
  non_local_means_filtering:

    # this is a fork option
    run: [On]

  # N4 bias field correction via ANTs
  n4_bias_field_correction:

    # 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:

    # option parameters
    FSL-FAST:
      use_priors:

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

    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
    registration:

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

    EPI_registration:
      ANTs:

        # EPI registration configuration - synonymous with T1_registration
        # parameters under anatomical registration above
        parameters:

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
    motion_estimates:

      # calculate motion statistics BEFORE slice-timing correction
      calculate_motion_first: 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: [Anatomical_Refined]

  generate_func_mean:

    # Generate mean functional image
    run: On

  normalize_func:

    # Normalize functional image
    run: On

  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: 5

    # Last timepoint to include in analysis.
    # Default is None or End (end of timeseries).
    # Last 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.
    stop_tr: 100

  coreg_prep:

    # Generate sbref
    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, Off]

    # 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_P
            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

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

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

  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
    /cpac_templates/PNAS_Smith09_rsn10.nii.gz: DualReg