%YAML 1.1
---
# CPAC Pipeline Configuration YAML file
# Version 1.8.8.dev1
#
# 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 size of brain in z-dimension in mm.
# Default: 150mm for human data.
brain_size: 70
# ACPC aligned template
T1w_ACPC_template: /cpac_templates/MacaqueYerkes19_T1w_0.5mm.nii.gz
brain_extraction:
run: On
# using: ['3dSkullStrip', 'BET', 'UNet', 'niworkflows-ants', 'FreeSurfer-ABCD', 'FreeSurfer-BET-Tight', 'FreeSurfer-BET-Loose', 'FreeSurfer-Brainmask']
# this is a fork option
using: [UNet]
# 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
# 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:
# 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: []
registration_workflows:
anatomical_registration:
run: On
registration:
FSL-FNIRT:
# Reference mask for FSL registration.
ref_mask: /cpac_templates/MacaqueYerkes19_T1w_1.0mm_brain_mask.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: /cpac_templates/MacaqueYerkes19_T1w_1.0mm_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_1.0mm.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
func_input_prep:
Mean Functional:
# Run ANTs’ N4 Bias Field Correction on the input BOLD (EPI)
# this can increase tissue contrast which may improve registration quality in some data
n4_correct_func: 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
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: 2mm
# 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
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_2mm_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_2mm.nii.gz
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_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]
# 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
# 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
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:
# 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
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:
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/resources/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
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/parcellation/Markov91_L_0.5mm.nii.gz: Avg
s3://fcp-indi/resources/cpac/resources/parcellation/Markov91_R_0.5mm.nii.gz: Avg
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