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preprocessing.md

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Pre-processing pipelines

Here I detail each of the different pre-processing pipelines and their configuration parameters.

T1-weighted MRI

[graph] This pipeline will bias-field correct, segment the tissues, and register a T1-weighted image to MNI.

It is based in ANTS and SPM12. It is implemented in neuro_neuro_pypes.anat.attach_spm_anat_preprocessing.

A cortical thickness method is enabled with the anat_preproc.do_cortical_thickness boolean field. This performs the SPM+DiReCT method described in (Schwarz et al., 2016), which will use ANTs' KellyKapowski tool on SPM12 tissue segmentations.

These are the steps:

  1. Bias-field correction using ANTS/N4BiasFieldCorrection.
  2. Brain tissue segmentation and spatial normalization with SPM12 New Segment.
  3. Tissue maps spatial normalization to MNI using SPM12 Normalize.
  4. Create a brain mask based on tissue segmentations.

[optional]

  1. Warp atlas (or any file in SPM12-MNI space) to anatomical space.
  2. Measure Cortical Thickness with the SPM+DiReCT method.
Related settings
spm_dir: "~/Software/matlab_tools/spm12"

# anatomical image pre-processing
normalize_atlas: True
atlas_file: '/home/hansel/data/std_brains/atlases/hammers/Hammers_mith_atlas_n30r83_SPM5.nii.gz'

# this is similar to the SPM+DiReCT method described here:
# http://dx.doi.org/10.1016/j.nicl.2016.05.017
anat_preproc.do_cortical_thickness: True

# these are the KellyKapowski default parameters
# also used in antsCorticalThickness
direct.convergence: "[45,0.0,10]"
direct.gradient_step: 0.025
direct.smoothing_variance: 1.0
direct.smoothing_velocity_field: 1.5
direct.use_bspline_smoothing: False
direct.number_integration_points: 10
direct.thickness_prior_estimate: 10

Resting-state fMRI

[graph] This pipeline preprocess fMRI data for resting-state fMRI (rs-fMRI) analyses. It depends on the T1-weighted preprocessing pipeline.

It is based on SPM12, nipype ArtifactDetect and TSNR, Nipy motion correction, and nilearn.

It consists on two parts:

  1. fMRI data cleaning ( neuro_pypes.fmri.clean.attach_fmri_cleanup_wf) and
  2. warping and smoothing (neuro_pypes.fmri.warp.attach_spm_warp_fmri_wf).

The connection of both parts is in neuro_pypes.fmri.resting._attach_rest_preprocessing.

It's also possible to create a group template if you set that in the configuration file.

Steps:

  1. Trim the first 6 seconds from the fMRI data.
  2. Slice-time correction based on SPM12 SliceTiming.
  3. Motion correction with nipy.SpaceTimeRealigner.
  4. Tissue co-registration from anatomical space to fMRI space.
  5. Nuisance corrections including:
    1. time-course SNR (TSNR) estimation,
    2. artifact detection (nipype.rapidART),
    3. motion estimation and filtering,
    4. signal component regression from different tissues (nipype ACompCor), and
    5. global signal regression (GSR).
  6. Bandpass time filter. Settings: rest_input.lowpass_freq: 0.1, and rest_input.highpass_freq: 0.01.
  7. Spatial smoothing. smooth_fmri.fwhm: 8

The slice-time correction requires information in the headers of the NifTI files about acquisition slice-timing. NifTI files generated from most DICOM formats with a recent version of dcm2niix should have the necessary information.

The nuisance correction steps can be enabled/disabled and configurable through the configuration file. Note: There is one thing that can't be easily modified: you need to perform component regression for at least one tissue, e.g., CSF.

In the same way as for the MRI + FDG-PET pipeline (described below), there is 2 ways for registration. This is configured through the registration.fmri2mni option.

If registration.fmri2mni: True

  1. Cleaned-up versions of fMRI are directly warped to MNI using SPM12 Normalize.
  2. Smooth these warped images, in the same ways as the non-warped data, according to smooth_fmri.fwhm: 8.

If registration.fmri2mni: False

  1. Co-register fMRI to anatomical space.
  2. Apply the anat-to-MNI warp field to warp the cleaned-up versions of the fMRI data to MNI.
  3. Smooth these warped images, in the same ways as the non-warped data, according to smooth_fmri.fwhm: 8.
Related settings
registration.fmri2mni: True

# degree of b-spline used for rs-fmri interpolation
coreg_rest.write_interp: 3

## the last volume index to discard from the timeseries. default: 0
trim.begin_index: 5

# REST (COBRE DB)
# http://fcon_1000.projects.nitrc.org/indi/retro/cobre.html
# Rest scan:
# - collected in the Axial plane,
# - series ascending,
# - multi slice mode and
# - interleaved.
stc_input.slice_mode: alt_inc
stc_input.time_repetition: 2
#stc_input.num_slices: 33

# fMRI PREPROCESSING
# for any fmri warping, except group template creation (look below)
fmri_warp.write_voxel_sizes: [2, 2, 2]

# mid-process registration for group template creation
fmri_grptemplate_warp.write_voxel_sizes: [2, 2, 2]

# GROUP fMRI TEMPLATE
spm_epi_grouptemplate_smooth.fwhm: 8

# path to a common EPI template, if you don't want the average across subjects
spm_epi_grouptemplate_smooth.template_file: ""

# bandpass filter frequencies in Hz.
rest_input.lowpass_freq: 0.1 # the numerical upper bound
rest_input.highpass_freq: 0.01 # the numerical lower bound

# fwhm of smoothing kernel [mm]
smooth_fmri.fwhm: 8

## CompCor rsfMRI filters (at least compcor_csf should be True).
rest_filter.compcor_csf: True
rest_filter.compcor_wm: False
rest_filter.gsr: False

# filters parameters
## the corresponding filter must be enabled for these.

# motion regressors upto given order and derivative
# motion + d(motion)/dt + d2(motion)/dt2 (linear + quadratic)
motion_regressors.order: 0
motion_regressors.derivatives: 1

# number of polynomials to add to detrend
motart_parameters.detrend_poly: 2

# Compute TSNR on realigned data regressing polynomials up to order 2
tsnr.regress_poly: 2

# Threshold to use to detect motion-related outliers when composite motion is being used
detect_artifacts.use_differences: [True, False]
detect_artifacts.parameter_source: NiPy
detect_artifacts.mask_type: file
detect_artifacts.use_norm: True
detect_artifacts.zintensity_threshold: 3
detect_artifacts.norm_threshold: 1

# Number of principal components to calculate when running CompCor. 5 or 6 is recommended.
compcor_pars.num_components: 6

# Number of principal components to calculate when running Global Signal Regression. 1 is recommended.
gsr_pars.num_components: 1

Diffusion MRI

[graph] This pipeline performs Diffusion MRI (DTI) correction and pre-processing, tensor-fitting and tractography.

It is based on FSL Eddy, dipy, and UCL Camino.

Steps:

  1. Eddy-currents and motion correction through FSL Eddy.
  2. Non-Local Means from dipy for image de-noising with a Rician filter.
  3. Co-register the anatomical image to diffusion space.
  4. Rotate the b-vecs based on motion estimation.

[optional]

  1. Warp an atlas to diffusion space.

The Eddy-currents correction needs certain fields in the NifTI file to be able to create input parameters for Eddy. Any file converted from modern DICOM to NifTI with a recent version of dcm2nii or dcm2niix should work.

The atlas warping is needed if you'll perform tractography.

Related settings
normalize_atlas: True
atlas_file: ''

# degree of b-spline used for interpolation
coreg_b0.write_interp: 3
nlmeans_denoise.N: 12 # number of channels in the head coil

Positron-Emission Tomography

This is a spatial normalization pipeline for Positron-Emission Tomography (PET) images. This workflow has showed good registration results on FDG and FDOPA PET images.

It is based on SPM12. You can find its source code in neuro_pypes.pet.warp.attach_spm_pet_preprocessing.

Steps:

  1. Spatially normalize FDG-PET to MNI using SPM12 Normalize.

There is a group template option for PET: first a group template is created, then all subjects are normalized to this group template.

Related settings
# GROUP PET TEMPLATE
spm_pet_grouptemplate_smooth.fwhm: 8
# path to a common PET template, if you don't want the average across subjects
spm_pet_grouptemplate.template_file: ""

T1-Weighted MRI & Positron Emission Tomography

[graph] This is a partial volume correction (PVC) and spatial normalization pipeline for PET images.

It is based on PETPVC, nilearn and SPM12. It is implemented in neuro_pypes.pet.mrpet.attach_spm_mrpet_preprocessing.

This pipeline depends on the anatomical preprocessing pipeline. There is 2 different ways of co-registration, you can configure that by setting the registration.anat2pet boolean option to True or False.

If registration.anat2pet: True

  1. Co-register anatomical and tissues to PET space.
  2. Partial volume effect correction (PVC) with PETPVC in PET space.
  3. Directly normalize PET to MNI with SPM12 Normalize.

If registration.anat2pet: False

  1. Co-register PET to anatomical space.
  2. PVC with PETPVC in anatomical space.
  3. Normalize PET to MNI with SPM12 Normalize applying the anatomical-to-MNI warp field.

[optional]

  1. Warp atlas from anatomical to PET space.

The PVC is done based on tissue segmentations from the anatomical pipeline.

Related settings
normalize_atlas: True
atlas_file: ''

registration.anat2pet: False

# GROUP PET TEMPLATE with MR co-registration
spm_mrpet_grouptemplate_smooth.fwhm: 8
spm_mrpet_grouptemplate.do_petpvc: True
# path to a common PET template, if you don't want the average across subjects
spm_mrpet_grouptemplate.template_file: ""

# PET PVC
pvc.pvc: RBV
pvc.fwhm_x: 4.3
pvc.fwhm_y: 4.3
pvc.fwhm_z: 4.3