pipeline_config_rodent.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: analysis
  output_directory:

    # Quality control outputs
    quality_control:

      # Generate quality control pages containing preprocessing and derivative outputs.
      generate_quality_control_images: 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: 6.0

    # 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: 4

    # 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: 4

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

  brain_extraction:
    run: On

registration_workflows:
  functional_registration:
    coregistration:
      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

      # apply the functional-to-template (EPI template) registration transform to the functional data
      run_EPI: On
      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: 2.3mmx2.3mmx6mm

        # 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: 2.3mmx2.3mmx6mm

      target_template:

        # choose which template space to transform derivatives towards
        # using: ['T1_template', 'EPI_template']
        # this is a fork point
        # NOTE:
        #   this will determine which registration transform to use to warp the functional
        #   outputs and derivatives to template space
        using: [EPI_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: /template/study_based_functional_template_sk.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: /template/study_based_functional_template.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:

          # a standard template for resampling if using float resolution
          T1w_template_for_resample:

        EPI_template:

          # EPI template for direct functional-to-template registration
          # (bypassing coregistration and the anatomical-to-template transforms)
          EPI_template_funcreg: /cpac_templates/chd8_functional_template_sk.nii

          # a standard template for resampling if using float resolution
          EPI_template_for_resample:

      ANTs_pipelines:

        # Interpolation method for writing out transformed functional images.
        # Possible values: Linear, BSpline, LanczosWindowedSinc
        interpolation: Linear

    EPI_registration:

      # directly register the mean functional to an EPI template
      #   instead of applying the anatomical T1-to-template transform to the functional data that has been
      #   coregistered to anatomical/T1 space
      run: On

      # EPI template for direct functional-to-template registration
      # (bypassing coregistration and the anatomical-to-template transforms)
      EPI_template: /cpac_templates/chd8_functional_template_sk.nii
      ANTs:

        # EPI registration configuration - synonymous with T1_registration
        # parameters under anatomical registration above
        parameters:
          - dimensionality: 3
          - initial-moving-transform:
              initializationFeature: 1
          - transforms:
            - Affine:
                convergence:
                  iteration: 100x100x30
                gradientStep: 0.25
                metric:
                  metricWeight: 1
                  radius: 2
                  type: CC
                shrink-factors: 5x3x1
                smoothing-sigmas: 5x3x0
            - SyN:
                convergence:
                  iteration: 100x100x30
                gradientStep: 0.15
                metric:
                  metricWeight: 1
                  radius: 2
                  type: CC
                shrink-factors: 5x3x1
                smoothing-sigmas: 5x3x0
                totalFieldVarianceInVoxelSpace: 1
                updateFieldVarianceInVoxelSpace: 5

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]

      # option parameters
      AFNI-3dvolreg:

        # This option is useful when aligning high-resolution datasets that may need more alignment than a few voxels.
        functional_volreg_twopass: Off

  func_masking:
    run: On
    FSL-BET:

      # Set an intensity threshold to improve skull stripping performances of FSL BET on rodent scans.
      functional_mean_thr:
        run: On

      # Bias correct the functional mean image to improve skull stripping performances of FSL BET on rodent scans
      functional_mean_bias_correction: On

      # Apply to 4D FMRI data, if bold_bet_functional_mean_boolean : Off.
      # Mutually exclusive with functional, reduce_bias, robust, padding, remove_eyes, surfaces
      # It must be 'on' if select 'reduce_bias', 'robust', 'padding', 'remove_eyes', or 'bet_surfaces' on
      functional_mean_boolean: On

      # Integer value of head radius
      radius: 50

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

  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:

    # 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: Regressor_1
        Bandpass:
          bottom_frequency: 0.01
          top_frequency: 0.1
          method: AFNI
        CerebrospinalFluid:
          erode_mask: Off
          summary: Mean
        GlobalSignal:
          summary: Mean
        Motion:
          include_delayed: Off
          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

    # Standard Lateral Ventricles Binary Mask
    # used in CSF mask refinement for CSF signal-related regressions
    lateral_ventricles_mask: /cpac_templates/chd8_functional_template_noise_mask_ag.nii

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]

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: /template/study_based_functional_template_sk.nii.gz

    # A reference symmetric brain template for resampling
    T1w_brain_template_symmetric_for_resample: /template/study_based_functional_template_sk.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: /template/study_based_functional_template.nii.gz

    # A reference symmetric skull template for resampling
    T1w_template_symmetric_for_resample: /template/study_based_functional_template.nii.g

    # 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: /template/study_based_functional_template_mask.nii.gz

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

# 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]

    # 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:
    run: On