%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: ndmg
output_directory:
# Quality control outputs
quality_control:
# Generate quality control pages containing preprocessing and derivative outputs.
generate_quality_control_images: On
system_config:
# 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:
run: On
acpc_alignment:
T1w_brain_ACPC_template: $FSLDIR/data/standard/MNI152_T1_1mm_brain.nii.gz
brain_extraction:
run: On
segmentation:
# Automatically segment anatomical images into white matter, gray matter,
# and CSF based on prior probability maps.
run: On
tissue_segmentation:
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: [FSL]
# option parameters
ANTs:
# ANTs parameters for T1-template-based registration
T1_registration:
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 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
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
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
CerebrospinalFluid:
extraction_resolution: 2
summary: Mean
PolyOrt:
degree: 2
aCompCor:
extraction_resolution: 2
summary:
components: 5
method: DetrendPC
tissues:
- WhiteMatter
- CerebrospinalFluid
# 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:
/ndmg_atlases/label/Human/AAL_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/Brodmann_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/CPAC200_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/Desikan_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DKT_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00071_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00096_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00108_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00140_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00195_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00278_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00350_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00446_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00583_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS00833_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/DS01216_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/HarvardOxfordcort-maxprob-thr25_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/HarvardOxfordsub-maxprob-thr25_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/JHU_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
/ndmg_atlases/label/Human/Princetonvisual-top_space-MNI152NLin6_res-2x2x2.nii.gz: Avg
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
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