pipeline_config_monkey-ABCD.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_default_monkey_skullstrip
  output_directory:

    # Quality control outputs
    quality_control:

      # Generate quality control pages containing preprocessing and derivative outputs.
      generate_quality_control_images: 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: 9

    # 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: 3

  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

  Amazon-AWS:

    # Enable server-side 256-AES encryption on data to the S3 bucket
    s3_encryption: On

anatomical_preproc:
  run: On
  acpc_alignment:
    T1w_brain_ACPC_template: /cpac_templates/MacaqueYerkes19_T1w_0.5mm_brain.nii.gz

    # Choose a tool to crop the FOV in ACPC alignment.
    # Using FSL's robustfov or flirt command.
    # Default: robustfov for human data, flirt for monkey data.
    FOV_crop: flirt
    T2w_ACPC_template: /cpac_templates/MacaqueYerkes19_T2w_0.5mm.nii.gz
    T2w_brain_ACPC_template: /cpac_templates/MacaqueYerkes19_T2w_0.5mm_brain.nii.gz
    run: On

    # ACPC Target
    # options: 'brain' or 'whole-head'
    #   note: 'brain' requires T1w_brain_ACPC_template below to be populated
    acpc_target: brain

    # ACPC aligned template
    T1w_ACPC_template: /cpac_templates/MacaqueYerkes19_T1w_0.5mm.nii.gz

  brain_extraction:
    run: On

    # option parameters
    AFNI-3dSkullStrip:

      # Set it as True if processing monkey data with AFNI
      monkey: On

    FSL-BET:

      # Set the threshold value controling the brain vs non-brain voxels, default is 0.5
      frac: 0.3

  run_t2: On

  # Bias field correction based on square root of T1w * T2w
  t1t2_bias_field_correction:
    run: On

segmentation:

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

    # using: ['FSL-FAST', 'Template_Based', 'ANTs_Prior_Based', 'FreeSurfer']
    # this is a fork point
    using: [ANTs_Prior_Based]

    # 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: []

    ANTs_Prior_Based:

      # Generate white matter, gray matter, CSF masks based on antsJointLabelFusion
      # ANTs Prior-based Segmentation workflow that has shown optimal results for non-human primate data.
      # The atlas image assumed to be used in ANTs Prior-based Segmentation.
      template_brain_list:
        - s3://fcp-indi/resources/cpac/resources/MacaqueYerkes19_T1w_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_11mo_atlas_nACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_11mo_atlas_oACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_21mo_atlas_nACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_21mo_atlas_oACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_36mo_atlas_nACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_36mo_atlas_oACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_4mo_atlas_nACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_4mo_atlas_oACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_6mo_atlas_nACQ_194x252x160space_0.5mm/T1w_brain.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_6mo_atlas_oACQ_194x252x160space_0.5mm/T1w_brain.nii.gz

      # The atlas segmentation images.
      # For performing ANTs Prior-based segmentation method
      # the number of specified segmentations should be identical to the number of atlas brain image sets.
      # eg.
      # ANTs_prior_seg_template_brain_list :
      #   - atlas1.nii.gz
      #   - atlas2.nii.gz
      # ANTs_prior_seg_template_segmentation_list:
      #   - segmentation1.nii.gz
      #   - segmentation1.nii.gz
      template_segmentation_list:
        - s3://fcp-indi/resources/cpac/resources/MacaqueYerkes19_T1w_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_11mo_atlas_nACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_11mo_atlas_oACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_21mo_atlas_nACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_21mo_atlas_oACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_36mo_atlas_nACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_36mo_atlas_oACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_4mo_atlas_nACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_4mo_atlas_oACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_6mo_atlas_nACQ_194x252x160space_0.5mm/Segmentation.nii.gz
        - s3://fcp-indi/resources/cpac/resources/J_Macaque_6mo_atlas_oACQ_194x252x160space_0.5mm/Segmentation.nii.gz

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

        # The resolution to which anatomical images should be transformed during registration.
        # This is the resolution at which processed anatomical files will be output.
        # specifically for monkey pipeline
        ref_resolution: 1mm

        # Template to be used during registration.
        # It is for monkey pipeline specifically.
        FNIRT_T1w_brain_template: /cpac_templates/MacaqueYerkes19_T1w_1.0mm_brain.nii.gz

        # Template to be used during registration.
        # It is for monkey pipeline specifically.
        FNIRT_T1w_template: /cpac_templates/MacaqueYerkes19_T1w_1.0mm.nii.gz

        # Reference mask for FSL registration.
        ref_mask: /cpac_templates/MacaqueYerkes19_T1w_1.0mm_brain_mask.nii.gz

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

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

    # The resolution to which anatomical images should be transformed during registration.
    # This is the resolution at which processed anatomical files will be output.
    resolution_for_anat: 0.5mm

    # 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: /cpac_templates/MacaqueYerkes19_T1w_0.5mm_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: /cpac_templates/MacaqueYerkes19_T1w_0.5mm.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: /cpac_templates/MacaqueYerkes19_T1w_0.5mm_brain_mask.nii.gz

    # 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

      # Choose coregistration interpolation
      interpolation: spline

      # Choose coregistration cost function
      cost: mutualinfo

      # Extra arguments for FSL flirt
      arguments: -searchrx -30 30 -searchry -30 30 -searchrz -30 30

    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
      apply_transform:

        # options: 'default', 'abcd', 'single_step_resampling_from_stc', 'dcan_nhp'
        # 'default': apply func-to-anat and anat-to-template transforms on motion corrected functional image.
        # 'abcd': apply motion correction, func-to-anat and anat-to-template transforms on each of raw functional volume using FSL applywarp based on ABCD-HCP pipeline.
        # 'single_step_resampling_from_stc': apply motion correction, func-to-anat and anat-to-template transforms on each of slice-time-corrected functional volume using ANTs antsApplyTransform based on fMRIPrep pipeline.
        #   - if 'single_step_resampling_from_stc', 'template' is the only valid option for ``nuisance_corrections: 2-nuisance_regression: space``
        using: dcan_nhp

      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: 1.5mm

        # 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: 1.5mm

      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: /cpac_templates/MacaqueYerkes19_T1w_1.5mm_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: /cpac_templates/MacaqueYerkes19_T1w_0.5mm.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: /cpac_templates/MacaqueYerkes19_T1w_1.5mm_brain_mask.nii.gz

          # a standard template for resampling if using float resolution
          T1w_template_for_resample: /cpac_templates/MacaqueYerkes19_T1w_0.5mm.nii.gz

      FNIRT_pipelines:

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

    EPI_registration:
      ANTs:

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

functional_preproc:
  run: On
  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: [mcflirt]

      # Choose motion correction reference. Options: mean, median, selected_volume, fmriprep_reference
      motion_correction_reference: [selected_volume]

  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: []

  func_masking:
    run: On
    FSL_AFNI:
      brain_mask:
      brain_probseg:

    # 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_Based]

  generate_func_mean:

    # Generate mean functional image
    run: On

  normalize_func:

    # Normalize functional image
    run: On

  coreg_prep:

    # Generate sbref
    run: On

nuisance_corrections:
  2-nuisance_regression:

    # Select which nuisance signal corrections to apply
    Regressors:
      - Name: Regressor_1
        Bandpass:
          bottom_frequency: 0.01
          top_frequency: 0.1
        CerebrospinalFluid:
          summary:
            components: 5
            method: PC
        GlobalSignal:
          summary: Mean
        Motion:
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
        PolyOrt:
          degree: 2
        WhiteMatter:
          summary:
            components: 5
            method: PC
      - Name: Regressor_2
        CerebrospinalFluid:
          summary:
            components: 5
            method: PC
        GlobalSignal:
          summary: Mean
        Motion:
          include_delayed: On
          include_delayed_squared: On
          include_squared: On
        PolyOrt:
          degree: 2
        WhiteMatter:
          summary:
            components: 5
            method: PC

    # 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

    # Standard Lateral Ventricles Binary Mask
    # used in CSF mask refinement for CSF signal-related regressions
    lateral_ventricles_mask:

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]

  # 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:
    /Template/DMN-Ghahremani2016/thr_bin_hjin/HRzstat_DMN_1_thr4.nii.gz: Avg
    /Template/DMN-Ghahremani2016/thr_bin_hjin/HRzstat_DMN_2_thr4.nii.gz: Avg
    /Template/DMN-Ghahremani2016/thr_bin_hjin/HRzstat_DMN_3_thr4.nii.gz: Avg
    s3://fcp-indi/resources/cpac/resources/parcellation/Markov91_L_0.5mm.nii.gz: Avg
    s3://fcp-indi/resources/cpac/resources/parcellation/Markov91_R_0.5mm.nii.gz: Avg

amplitude_low_frequency_fluctuation:

  # space: Template or Native
  target_space: [Native]

regional_homogeneity:

  # space: Template or Native
  target_space: [Native]

voxel_mirrored_homotopic_connectivity:
  symmetric_registration:

    # Included as part of the 'Image Resource Files' package available on the Install page of the User Guide.
    # It is not necessary to change this path unless you intend to use a non-standard symmetric template.
    T1w_brain_template_symmetric: /cpac_templates/MacaqueYerkes19_T1w_1.0mm_brain.nii.gz

    # A reference symmetric brain template for resampling
    T1w_brain_template_symmetric_for_resample: /cpac_templates/MacaqueYerkes19_T1w_1.0mm.nii.gz

    # Included as part of the 'Image Resource Files' package available on the Install page of the User Guide.
    # It is not necessary to change this path unless you intend to use a non-standard symmetric template.
    T1w_template_symmetric: /cpac_templates/MacaqueYerkes19_T1w_1.0mm.nii.gz

    # A reference symmetric skull template for resampling
    T1w_template_symmetric_for_resample: /cpac_templates/MacaqueYerkes19_T1w_1.0mm.nii.gz

    # Included as part of the 'Image Resource Files' package available on the Install page of the User Guide.
    # It is not necessary to change this path unless you intend to use a non-standard symmetric template.
    dilated_symmetric_brain_mask: /cpac_templates/MacaqueYerkes19_T1w_2mm_brain_mask.nii.gz

    # A reference symmetric brain mask template for resampling
    dilated_symmetric_brain_mask_for_resample: /cpac_templates/MacaqueYerkes19_T1w_2mm_brain_mask.nii.gz

network_centrality:

  # 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
  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:

    # 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

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

  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:
    /Template/DMN-Ghahremani2016/HRzstat_DMN_1.nii.gz: DualReg
    /Template/DMN-Ghahremani2016/HRzstat_DMN_2.nii.gz: DualReg
    /Template/DMN-Ghahremani2016/HRzstat_DMN_3.nii.gz: DualReg
    /Template/DMN-Ghahremani2016/thr_bin_hjin/HRzstat_DMN_1_thr4.nii.gz: Avg
    /Template/DMN-Ghahremani2016/thr_bin_hjin/HRzstat_DMN_2_thr4.nii.gz: Avg
    /Template/DMN-Ghahremani2016/thr_bin_hjin/HRzstat_DMN_3_thr4.nii.gz: Avg
    s3://fcp-indi/resources/cpac/resources/parcellation/Markov91_L_0.5mm.nii.gz: Avg
    s3://fcp-indi/resources/cpac/resources/parcellation/Markov91_R_0.5mm.nii.gz: Avg