Developers - API

The NiPreps community and contributing guidelines

fMRIPrep is a NiPreps application, and abides by the NiPreps Community guidelines. Please, make sure you have read and understood all the documentation provided in the NiPreps portal before you get started.

Setting up your development environment

We believe that fMRIPrep must be free to use, inspect, and critique. Correspondingly, you should be free to modify our software to improve it or adapt it to new use cases and we especially welcome contributions to improve it or its documentation.

We actively direct efforts into making the scrutiny and improvement processes as easy as possible. As part of such efforts, we maintain some tips and guidelines for developers to help minimize your burden if you want to modify the software.

Internal configuration system

A Python module to maintain unique, run-wide fMRIPrep settings.

This module implements the memory structures to keep a consistent, singleton config. Settings are passed across processes via filesystem, and a copy of the settings for each run and subject is left under <fmriprep_dir>/sub-<participant_id>/log/<run_unique_id>/fmriprep.toml. Settings are stored using ToML. The module has a to_filename() function to allow writing out the settings to hard disk in ToML format, which looks like:

Example file representation of fMRIPrep settings.
[environment]
cpu_count = 8
exec_env = "posix"
free_mem = 2.2
overcommit_policy = "heuristic"
overcommit_limit = "50%"
nipype_version = "1.5.0"
templateflow_version = "0.4.2"
version = "20.0.1"

[execution]
bids_dir = "ds000005/"
bids_description_hash = "5d42e27751bbc884eca87cb4e62b9a0cca0cd86f8e578747fe89b77e6c5b21e5"
boilerplate_only = false
fs_license_file = "/opt/freesurfer/license.txt"
fs_subjects_dir = "/opt/freesurfer/subjects"
log_dir = "/home/oesteban/tmp/fmriprep-ds005/out/fmriprep/logs"
log_level = 40
low_mem = false
md_only_boilerplate = false
notrack = true
output_dir = "/tmp"
output_spaces = "MNI152NLin2009cAsym:res-2 MNI152NLin2009cAsym:res-native fsaverage:den-10k fsaverage:den-30k"
reports_only = false
run_uuid = "20200306-105302_d365772b-fd60-4741-a722-372c2f558b50"
participant_label = [ "01",]
templateflow_home = "~/.cache/templateflow"
work_dir = "work/"
write_graph = false

[workflow]
anat_only = false
aroma_err_on_warn = false
aroma_melodic_dim = -200
bold2t1w_dof = 6
fmap_bspline = false
force_syn = false
hires = true
ignore = []
longitudinal = false
medial_surface_nan = false
project_goodvoxels = false
regressors_all_comps = false
regressors_dvars_th = 1.5
regressors_fd_th = 0.5
run_reconall = true
skull_strip_fixed_seed = false
skull_strip_template = "OASIS30ANTs"
t2s_coreg = false
use_aroma = false

[nipype]
crashfile_format = "txt"
get_linked_libs = false
memory_gb = 32
nprocs = 8
omp_nthreads = 8
plugin = "MultiProc"
resource_monitor = false
stop_on_first_crash = false

[nipype.plugin_args]
maxtasksperchild = 1
raise_insufficient = false

[execution.bids_filters.t1w]
reconstruction = "<Query.NONE: 1>"

[execution.bids_filters.t2w]
reconstruction = "<Query.NONE: 1>"

This config file is used to pass the settings across processes, using the load() function.

Configuration sections

class fmriprep.config.environment[source]

Read-only options regarding the platform and environment.

Crawls runtime descriptive settings (e.g., default FreeSurfer license, execution environment, nipype and fMRIPrep versions, etc.). The environment section is not loaded in from file, only written out when settings are exported. This config section is useful when reporting issues, and these variables are tracked whenever the user does not opt-out using the --notrack argument.

cpu_count = 2

Number of available CPUs.

exec_docker_version = None

Version of Docker Engine.

exec_env = 'posix'

A string representing the execution platform.

free_mem = 6.4

Free memory at start.

nipype_version = '1.8.6'

Nipype’s current version.

overcommit_limit = '50%'

Linux’s kernel virtual memory overcommit limits.

overcommit_policy = 'heuristic'

Linux’s kernel virtual memory overcommit policy.

templateflow_version = '24.2.0'

The TemplateFlow client version installed.

version = '23.2.3'

fMRIPrep’s version.

class fmriprep.config.execution[source]

Configure run-level settings.

bids_database_dir = None[source]

Path to the directory containing SQLite database indices for the input BIDS dataset.

bids_description_hash = None

Checksum (SHA256) of the dataset_description.json of the BIDS dataset.

bids_dir = None[source]

An existing path to the dataset, which must be BIDS-compliant.

bids_filters = None

A dictionary of BIDS selection filters.

boilerplate_only = False

Only generate a boilerplate.

country_code = 'CAN'

Country ISO code used by carbon trackers.

debug = []

Debug mode(s).

derivatives = []

Path(s) to search for pre-computed derivatives

echo_idx = None

Select a particular echo for multi-echo EPI datasets.

fmriprep_dir = None[source]

Root of fMRIPrep BIDS Derivatives dataset. Depends on output_layout.

fs_license_file = None[source]

An existing file containing a FreeSurfer license.

fs_subjects_dir = None[source]

FreeSurfer’s subjects directory.

classmethod init()[source]

Create a new BIDS Layout accessible with layout.

layout = None[source]

A BIDSLayout object, see init().

log_dir = None[source]

The path to a directory that contains execution logs.

log_level = 25

Output verbosity.

low_mem = None

Utilize uncompressed NIfTIs and other tricks to minimize memory allocation.

md_only_boilerplate = False

Do not convert boilerplate from MarkDown to LaTex and HTML.

me_output_echos = False

Output individual echo time series with slice, motion and susceptibility correction

notrack = False

Do not collect telemetry information for fMRIPrep.

output_dir = None[source]

Folder where derivatives will be stored.

output_layout = None

Layout of derivatives within output_dir.

output_spaces = None

List of (non)standard spaces designated (with the --output-spaces flag of the command line) as spatial references for outputs.

participant_label = None

List of participant identifiers that are to be preprocessed.

reports_only = False

Only build the reports, based on the reportlets found in a cached working directory.

run_uuid = '20240520-162954_26785346-7bf4-4827-a7fd-21bec75d184d'

Unique identifier of this particular run.

sloppy = False

Run in sloppy mode (meaning, suboptimal parameters that minimize run-time).

task_id = None

Select a particular task from all available in the dataset.

templateflow_home = PosixPath('/home/docs/.cache/templateflow')[source]

The root folder of the TemplateFlow client.

track_carbon = False

Tracks power draws using CodeCarbon package.

work_dir = PosixPath('/home/docs/checkouts/readthedocs.org/user_builds/fmriprep/checkouts/stable/docs/work')[source]

Path to a working directory where intermediate results will be available.

write_graph = False

Write out the computational graph corresponding to the planned preprocessing.

class fmriprep.config.workflow[source]

Configure the particular execution graph of this workflow.

anat_only = False

Execute the anatomical preprocessing only.

aroma_err_on_warn = None

Cast AROMA warnings to errors.

aroma_melodic_dim = None

Number of ICA components to be estimated by MELODIC (positive = exact, negative = maximum).

bold2t1w_dof = None

Degrees of freedom of the BOLD-to-T1w registration steps.

bold2t1w_init = 'register'

Whether to use standard coregistration (‘register’) or to initialize coregistration from the BOLD image-header (‘header’).

cifti_output = None

Generate HCP Grayordinates, accepts either '91k' (default) or '170k'.

dummy_scans = None

Set a number of initial scans to be considered nonsteady states.

fmap_bspline = None

Regularize fieldmaps with a field of B-Spline basis.

fmap_demean = None

Remove the mean from fieldmaps.

force_syn = None

Run fieldmap-less susceptibility-derived distortions estimation.

hires = None

Run FreeSurfer recon-all with the -hires flag.

ignore = None

Ignore particular steps for fMRIPrep.

level = None

Level of preprocessing to complete. One of [‘minimal’, ‘resampling’, ‘full’].

longitudinal = False

Run FreeSurfer recon-all with the -logitudinal flag.

me_t2s_fit_method = 'curvefit'

The method by which to estimate T2*/S0 for multi-echo data

medial_surface_nan = None

Fill medial surface with NaNs when sampling.

project_goodvoxels = False

Exclude voxels with locally high coefficient of variation from sampling.

regressors_all_comps = None

Return all CompCor components.

regressors_dvars_th = None

Threshold for DVARS.

regressors_fd_th = None

Threshold for FD.

run_msmsulc = True

Run Multimodal Surface Matching surface registration.

run_reconall = True

Run FreeSurfer’s surface reconstruction.

skull_strip_fixed_seed = False

Fix a seed for skull-stripping.

skull_strip_t1w = 'force'

Skip brain extraction of the T1w image (default is force, meaning that fMRIPrep will run brain extraction of the T1w).

skull_strip_template = 'OASIS30ANTs'

Change default brain extraction template.

slice_time_ref = 0.5

The time of the reference slice to correct BOLD values to, as a fraction acquisition time. 0 indicates the start, 0.5 the midpoint, and 1 the end of acquisition. The alias start corresponds to 0, and middle to 0.5. The default value is 0.5.

spaces = None[source]

Keeps the SpatialReferences instance keeping standard and nonstandard spaces.

use_aroma = None

Run ICA-AROMA.

use_bbr = None

Run boundary-based registration for BOLD-to-T1w registration.

use_syn_sdc = None

Run fieldmap-less susceptibility-derived distortions estimation in the absence of any alternatives.

class fmriprep.config.nipype[source]

Nipype settings.

crashfile_format = 'txt'

The file format for crashfiles, either text (txt) or pickle (pklz).

get_linked_libs = False

Run NiPype’s tool to enlist linked libraries for every interface.

classmethod get_plugin()[source]

Format a dictionary for Nipype consumption.

classmethod init()[source]

Set NiPype configurations.

memory_gb = None

Estimation in GB of the RAM this workflow can allocate at any given time.

nprocs = 2

Number of processes (compute tasks) that can be run in parallel (multiprocessing only).

omp_nthreads = None

Number of CPUs a single process can access for multithreaded execution.

plugin = 'MultiProc'

NiPype’s execution plugin.

plugin_args = {'maxtasksperchild': 1, 'raise_insufficient': False}

Settings for NiPype’s execution plugin.

remove_unnecessary_outputs = True

Clean up unused outputs after running

resource_monitor = False

Enable resource monitor.

stop_on_first_crash = True

Whether the workflow should stop or continue after the first error.

Usage

A config file is used to pass settings and collect information as the execution graph is built across processes.

from fmriprep import config
config_file = config.execution.work_dir / '.fmriprep.toml'
config.to_filename(config_file)
# Call build_workflow(config_file, retval) in a subprocess
with Manager() as mgr:
    from .workflow import build_workflow
    retval = mgr.dict()
    p = Process(target=build_workflow, args=(str(config_file), retval))
    p.start()
    p.join()
config.load(config_file)
# Access configs from any code section as:
value = config.section.setting

Logging

class fmriprep.config.loggers[source]

Keep loggers easily accessible (see init()).

cli = <Logger cli (WARNING)>[source]

Command-line interface logging.

default = <RootLogger root (WARNING)>[source]

The root logger.

classmethod init()[source]

Set the log level, initialize all loggers into loggers.

  • Add new logger levels (25: IMPORTANT, and 15: VERBOSE).

  • Add a new sub-logger (cli).

  • Logger configuration.

interface = <Logger nipype.interface (INFO)>[source]

NiPype’s interface logger.

utils = <Logger nipype.utils (INFO)>[source]

NiPype’s utils logger.

workflow = <Logger nipype.workflow (INFO)>[source]

NiPype’s workflow logger.

Other responsibilities

The config is responsible for other conveniency actions.

  • Switching Python’s multiprocessing to forkserver mode.

  • Set up a filter for warnings as early as possible.

  • Automated I/O magic operations. Some conversions need to happen in the store/load processes (e.g., from/to Path <-> str, BIDSLayout, etc.)

fmriprep.config.dumps()[source]

Format config into toml.

fmriprep.config.from_dict(settings, init=True, ignore=None)[source]

Read settings from a flat dictionary.

Parameters:
  • setting (dict) – Settings to apply to any configuration

  • init (bool or Container) – Initialize all, none, or a subset of configurations.

  • ignore (Container) – Collection of keys in setting to ignore

fmriprep.config.get(flat=False)[source]

Get config as a dict.

fmriprep.config.init_spaces(checkpoint=True)[source]

Initialize the spaces setting.

fmriprep.config.load(filename, skip=None, init=True)[source]

Load settings from file.

Parameters:
  • filename (os.PathLike) – TOML file containing fMRIPrep configuration.

  • skip (dict or None) – Sets of values to ignore during load, keyed by section name

  • init (bool or Container) – Initialize all, none, or a subset of configurations.

fmriprep.config.to_filename(filename)[source]

Write settings to file.

Workflows

fMRIPrep base processing workflows

fmriprep.workflows.base.init_fmriprep_wf()[source]

Build fMRIPrep’s pipeline.

This workflow organizes the execution of FMRIPREP, with a sub-workflow for each subject.

If FreeSurfer’s recon-all is to be run, a corresponding folder is created and populated with any needed template subjects under the derivatives folder.

Workflow Graph
_images/api-1.png

(Source code, png, svg, pdf)

fmriprep.workflows.base.init_single_subject_wf(subject_id: str)[source]

Organize the preprocessing pipeline for a single subject.

It collects and reports information about the subject, and prepares sub-workflows to perform anatomical and functional preprocessing. Anatomical preprocessing is performed in a single workflow, regardless of the number of sessions. Functional preprocessing is performed using a separate workflow for each individual BOLD series.

Workflow Graph
_images/api-2.png

(Source code, png, svg, pdf)

Parameters:

subject_id (str) – Subject label for this single-subject workflow.

Parameters:

subjects_dir (str) – FreeSurfer’s $SUBJECTS_DIR.

Pre-processing fMRI - BOLD signal workflows

Orchestrating the BOLD-preprocessing workflow

fmriprep.workflows.bold.base.init_bold_wf(*, bold_series: List[str], precomputed: dict = {}, fieldmap_id: str | None = None) Workflow[source]

This workflow controls the functional preprocessing stages of fMRIPrep.

Workflow Graph
_images/api-3.png

(Source code, png, svg, pdf)

Parameters:
  • bold_series – List of paths to NIfTI files.

  • precomputed – Dictionary containing precomputed derivatives to reuse, if possible.

  • fieldmap_id – ID of the fieldmap to use to correct this BOLD series. If None, no correction will be applied.

Parameters:
  • t1w_preproc – Bias-corrected structural template image

  • t1w_mask – Mask of the skull-stripped template image

  • t1w_dseg – Segmentation of preprocessed structural image, including gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)

  • t1w_tpms – List of tissue probability maps in T1w space

  • subjects_dir – FreeSurfer SUBJECTS_DIR

  • subject_id – FreeSurfer subject ID

  • fsnative2t1w_xfm – LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w

  • white – FreeSurfer white matter surfaces, in T1w space, collated left, then right

  • midthickness – FreeSurfer mid-thickness surfaces, in T1w space, collated left, then right

  • pial – FreeSurfer pial surfaces, in T1w space, collated left, then right

  • sphere_reg_fsLR – Registration spheres from fsnative to fsLR space, collated left, then right

  • anat_ribbon – Binary cortical ribbon mask in T1w space

  • fmap_id – Unique identifiers to select fieldmap files

  • fmap – List of estimated fieldmaps (collated with fmap_id)

  • fmap_ref – List of fieldmap reference files (collated with fmap_id)

  • fmap_coeff – List of lists of spline coefficient files (collated with fmap_id)

  • fmap_mask – List of fieldmap masks (collated with fmap_id)

  • sdc_method – List of fieldmap correction method names (collated with fmap_id)

  • anat2std_xfm – Transform from anatomical space to standard space

  • std_t1w – T1w reference image in standard space

  • std_mask – Brain (binary) mask of the standard reference image

  • std_space – Value of space entity to be used in standard space output filenames

  • std_resolution – Value of resolution entity to be used in standard space output filenames

  • std_cohort – Value of cohort entity to be used in standard space output filenames

  • anat2mni6_xfm – Transform from anatomical space to MNI152NLin6Asym space

  • mni6_mask – Brain (binary) mask of the MNI152NLin6Asym reference image

  • mni2009c2anat_xfm – Transform from MNI152NLin2009cAsym to anatomical space

  • Note that ``anat2std_xfm``, ``std_space``, ``std_resolution``,

  • ``std_cohort``, ``std_t1w`` and ``std_mask`` are treated as single

  • inputs. In order to resample to multiple target spaces, connect

  • these fields to an iterable.

See also

fmriprep.workflows.bold.base.init_bold_fit_wf(*, bold_series: List[str], precomputed: dict = {}, fieldmap_id: str | None = None, omp_nthreads: int = 1, name: str = 'bold_fit_wf') Workflow[source]

This workflow controls the minimal estimation steps for functional preprocessing.

Workflow Graph
_images/api-4.png

(Source code, png, svg, pdf)

Parameters:
  • bold_series – List of paths to NIfTI files, sorted by echo time.

  • precomputed – Dictionary containing precomputed derivatives to reuse, if possible.

  • fieldmap_id – ID of the fieldmap to use to correct this BOLD series. If None, no correction will be applied.

Parameters:
  • bold_file – BOLD series NIfTI file

  • t1w_preproc – Bias-corrected structural template image

  • t1w_mask – Mask of the skull-stripped template image

  • t1w_dseg – Segmentation of preprocessed structural image, including gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)

  • anat2std_xfm – List of transform files, collated with templates

  • subjects_dir – FreeSurfer SUBJECTS_DIR

  • subject_id – FreeSurfer subject ID

  • fsnative2t1w_xfm – LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w

  • fmap_id – Unique identifiers to select fieldmap files

  • fmap – List of estimated fieldmaps (collated with fmap_id)

  • fmap_ref – List of fieldmap reference files (collated with fmap_id)

  • fmap_coeff – List of lists of spline coefficient files (collated with fmap_id)

  • fmap_mask – List of fieldmap masks (collated with fmap_id)

  • sdc_method – List of fieldmap correction method names (collated with fmap_id)

Parameters:
  • hmc_boldref – BOLD reference image used for head motion correction. Minimally processed to ensure consistent contrast with BOLD series.

  • coreg_boldref – BOLD reference image used for coregistration. Contrast-enhanced and fieldmap-corrected for greater anatomical fidelity, and aligned with hmc_boldref.

  • bold_mask – Mask of coreg_boldref.

  • motion_xfm – Affine transforms from each BOLD volume to hmc_boldref, written as concatenated ITK affine transforms.

  • boldref2anat_xfm – Affine transform mapping from BOLD reference space to the anatomical space.

  • boldref2fmap_xfm – Affine transform mapping from BOLD reference space to the fieldmap space, if applicable.

  • movpar_file – MCFLIRT motion parameters, normalized to SPM format (X, Y, Z, Rx, Ry, Rz)

  • rmsd_file – Root mean squared deviation as measured by fsl_motion_outliers [Jenkinson2002].

  • dummy_scans – The number of dummy scans declared or detected at the beginning of the series.

See also

fmriprep.workflows.bold.base.init_bold_native_wf(*, bold_series: List[str], fieldmap_id: str | None = None, omp_nthreads: int = 1, name: str = 'bold_native_wf') Workflow[source]

Minimal resampling workflow.

This workflow performs slice-timing correction, and resamples to boldref space with head motion and susceptibility distortion correction. It also handles multi-echo processing and selects the transforms needed to perform further resampling.

Workflow Graph
_images/api-5.png

(Source code, png, svg, pdf)

Parameters:
  • bold_series – List of paths to NIfTI files.

  • fieldmap_id – ID of the fieldmap to use to correct this BOLD series. If None, no correction will be applied.

Parameters:
  • boldref – BOLD reference file

  • bold_mask – Mask of BOLD reference file

  • motion_xfm – Affine transforms from each BOLD volume to hmc_boldref, written as concatenated ITK affine transforms.

  • boldref2fmap_xfm – Affine transform mapping from BOLD reference space to the fieldmap space, if applicable.

  • fmap_id – Unique identifiers to select fieldmap files

  • fmap_ref – List of fieldmap reference files (collated with fmap_id)

  • fmap_coeff – List of lists of spline coefficient files (collated with fmap_id)

Parameters:
  • bold_minimal – BOLD series ready for further resampling. For single-echo data, only slice-timing correction (STC) may have been applied. For multi-echo data, this is identical to bold_native.

  • bold_native – BOLD series resampled into BOLD reference space. Slice-timing, head motion and susceptibility distortion correction (STC, HMC, SDC) will all be applied to each file. For multi-echo data, the echos are combined to form an optimal combination.

  • metadata – Metadata dictionary of BOLD series with the shortest echo

  • motion_xfm – Motion correction transforms for further correcting bold_minimal. For multi-echo data, motion correction has already been applied, so this will be undefined.

  • bold_echos – The individual, corrected echos, suitable for use in Tedana. (Multi-echo only.)

  • t2star_map – The T2* map estimated by Tedana when calculating the optimal combination. (Multi-echo only.)

Head-Motion Estimation and Correction (HMC) of BOLD images

fmriprep.workflows.bold.hmc.init_bold_hmc_wf(mem_gb: float, omp_nthreads: int, name: str = 'bold_hmc_wf')[source]

Build a workflow to estimate head-motion parameters.

This workflow estimates the motion parameters to perform HMC over the input BOLD image.

Workflow Graph
_images/api-6.png

(Source code, png, svg, pdf)

Parameters:
  • mem_gb (float) – Size of BOLD file in GB

  • omp_nthreads (int) – Maximum number of threads an individual process may use

  • name (str) – Name of workflow (default: bold_hmc_wf)

Parameters:
  • bold_file – BOLD series NIfTI file

  • raw_ref_image – Reference image to which BOLD series is motion corrected

Parameters:
  • xforms – ITKTransform file aligning each volume to ref_image

  • movpar_file – MCFLIRT motion parameters, normalized to SPM format (X, Y, Z, Rx, Ry, Rz)

  • rmsd_file – Root mean squared deviation as measured by fsl_motion_outliers [Jenkinson2002].

Slice-Timing Correction (STC) of BOLD images

fmriprep.workflows.bold.stc.init_bold_stc_wf(*, mem_gb: dict, metadata: dict, name='bold_stc_wf')[source]

Create a workflow for STC.

This workflow performs STC over the input BOLD image.

Workflow Graph

(Source code)

Parameters:
  • metadata (dict) – BIDS metadata for BOLD file

  • name (str) – Name of workflow (default: bold_stc_wf)

Parameters:
  • bold_file – BOLD series NIfTI file

  • skip_vols – Number of non-steady-state volumes detected at beginning of bold_file

Parameters:

stc_file – Slice-timing corrected BOLD series NIfTI file

Generate T2* map from multi-echo BOLD images

fmriprep.workflows.bold.t2s.init_bold_t2s_wf(echo_times: Sequence[float], mem_gb: float, omp_nthreads: int, name: str = 'bold_t2s_wf')[source]

Combine multiple echos of ME-EPI.

This workflow wraps the tedana T2* workflow to optimally combine multiple preprocessed echos and derive a T2 map. The following steps are performed: #. Compute the T2 map #. Create an optimally combined ME-EPI time series

Parameters:
  • echo_times (list or tuple) – list of TEs associated with each echo

  • mem_gb (float) – Size of BOLD file in GB

  • omp_nthreads (int) – Maximum number of threads an individual process may use

  • name (str) – Name of workflow (default: bold_t2s_wf)

Parameters:
  • bold_file – list of individual echo files

  • bold_mask – a binary mask to apply to the BOLD files

Parameters:
  • bold – the optimally combined time series for all supplied echos

  • t2star_map – the calculated T2 map

Registration workflows

fmriprep.workflows.bold.registration.init_bold_reg_wf(freesurfer: bool, use_bbr: bool, bold2t1w_dof: Literal[6, 9, 12], bold2t1w_init: Literal['register', 'header'], mem_gb: float, omp_nthreads: int, name: str = 'bold_reg_wf', sloppy: bool = False)[source]

Build a workflow to run same-subject, BOLD-to-T1w image-registration.

Calculates the registration between a reference BOLD image and T1w-space using a boundary-based registration (BBR) cost function. If FreeSurfer-based preprocessing is enabled, the bbregister utility is used to align the BOLD images to the reconstructed subject, and the resulting transform is adjusted to target the T1 space. If FreeSurfer-based preprocessing is disabled, FSL FLIRT is used with the BBR cost function to directly target the T1 space.

Workflow Graph
_images/api-8.png

(Source code, png, svg, pdf)

Parameters:
  • freesurfer (bool) – Enable FreeSurfer functional registration (bbregister)

  • use_bbr (bool or None) – Enable/disable boundary-based registration refinement. If None, test BBR result for distortion before accepting.

  • bold2t1w_dof (6, 9 or 12) – Degrees-of-freedom for BOLD-T1w registration

  • bold2t1w_init (str, ‘header’ or ‘register’) – If 'header', use header information for initialization of BOLD and T1 images. If 'register', align volumes by their centers.

  • mem_gb (float) – Size of BOLD file in GB

  • omp_nthreads (int) – Maximum number of threads an individual process may use

  • name (str) – Name of workflow (default: bold_reg_wf)

Parameters:
  • ref_bold_brain – Reference image to which BOLD series is aligned If fieldwarp == True, ref_bold_brain should be unwarped

  • t1w_brain – Skull-stripped t1w_preproc

  • t1w_dseg – Segmentation of preprocessed structural image, including gray-matter (GM), white-matter (WM) and cerebrospinal fluid (CSF)

  • subjects_dir – FreeSurfer SUBJECTS_DIR

  • subject_id – FreeSurfer subject ID

  • fsnative2t1w_xfm – LTA-style affine matrix translating from FreeSurfer-conformed subject space to T1w

Parameters:
  • itk_bold_to_t1 – Affine transform from ref_bold_brain to T1 space (ITK format)

  • itk_t1_to_bold – Affine transform from T1 space to BOLD space (ITK format)

  • fallback – Boolean indicating whether BBR was rejected (mri_coreg registration returned)

fmriprep.workflows.bold.registration.init_bbreg_wf(use_bbr: bool, bold2t1w_dof: Literal[6, 9, 12], bold2t1w_init: Literal['register', 'header'], omp_nthreads: int, name: str = 'bbreg_wf')[source]

Build a workflow to run FreeSurfer’s bbregister.

This workflow uses FreeSurfer’s bbregister to register a BOLD image to a T1-weighted structural image.

It is a counterpart to init_fsl_bbr_wf(), which performs the same task using FSL’s FLIRT with a BBR cost function. The use_bbr option permits a high degree of control over registration. If False, standard, affine coregistration will be performed using FreeSurfer’s mri_coreg tool. If True, bbregister will be seeded with the initial transform found by mri_coreg (equivalent to running bbregister --init-coreg). If None, after bbregister is run, the resulting affine transform will be compared to the initial transform found by mri_coreg. Excessive deviation will result in rejecting the BBR refinement and accepting the original, affine registration.

Workflow Graph
_images/api-9.png

(Source code, png, svg, pdf)

Parameters:
  • use_bbr (bool or None) – Enable/disable boundary-based registration refinement. If None, test BBR result for distortion before accepting.

  • bold2t1w_dof (6, 9 or 12) – Degrees-of-freedom for BOLD-T1w registration

  • bold2t1w_init (str, ‘header’ or ‘register’) – If 'header', use header information for initialization of BOLD and T1 images. If 'register', align volumes by their centers.

  • name (str, optional) – Workflow name (default: bbreg_wf)

Parameters:
  • in_file – Reference BOLD image to be registered

  • fsnative2t1w_xfm – FSL-style affine matrix translating from FreeSurfer T1.mgz to T1w

  • subjects_dir – FreeSurfer SUBJECTS_DIR

  • subject_id – FreeSurfer subject ID (must have folder in SUBJECTS_DIR)

  • t1w_preproc – Unused (see init_fsl_bbr_wf())

  • t1w_mask – Unused (see init_fsl_bbr_wf())

  • t1w_dseg – Unused (see init_fsl_bbr_wf())

Parameters:
  • itk_bold_to_t1 – Affine transform from ref_bold_brain to T1 space (ITK format)

  • itk_t1_to_bold – Affine transform from T1 space to BOLD space (ITK format)

  • fallback – Boolean indicating whether BBR was rejected (mri_coreg registration returned)

fmriprep.workflows.bold.registration.init_fsl_bbr_wf(use_bbr: bool, bold2t1w_dof: Literal[6, 9, 12], bold2t1w_init: Literal['register', 'header'], omp_nthreads: int, sloppy: bool = False, name: str = 'fsl_bbr_wf')[source]

Build a workflow to run FSL’s flirt.

This workflow uses FSL FLIRT to register a BOLD image to a T1-weighted structural image, using a boundary-based registration (BBR) cost function. It is a counterpart to init_bbreg_wf(), which performs the same task using FreeSurfer’s bbregister.

The use_bbr option permits a high degree of control over registration. If False, standard, rigid coregistration will be performed by FLIRT. If True, FLIRT-BBR will be seeded with the initial transform found by the rigid coregistration. If None, after FLIRT-BBR is run, the resulting affine transform will be compared to the initial transform found by FLIRT. Excessive deviation will result in rejecting the BBR refinement and accepting the original, affine registration.

Workflow Graph
_images/api-10.png

(Source code, png, svg, pdf)

Parameters:
  • use_bbr (bool or None) – Enable/disable boundary-based registration refinement. If None, test BBR result for distortion before accepting.

  • bold2t1w_dof (6, 9 or 12) – Degrees-of-freedom for BOLD-T1w registration

  • bold2t1w_init (str, ‘header’ or ‘register’) – If 'header', use header information for initialization of BOLD and T1 images. If 'register', align volumes by their centers.

  • name (str, optional) – Workflow name (default: fsl_bbr_wf)

Parameters:
  • in_file – Reference BOLD image to be registered

  • t1w_preproc – T1-weighted structural image

  • t1w_mask – Brain mask of structural image

  • t1w_dseg – FAST segmentation of masked t1w_preproc

  • fsnative2t1w_xfm – Unused (see init_bbreg_wf())

  • subjects_dir – Unused (see init_bbreg_wf())

  • subject_id – Unused (see init_bbreg_wf())

Parameters:
  • itk_bold_to_t1 – Affine transform from ref_bold_brain to T1w space (ITK format)

  • itk_t1_to_bold – Affine transform from T1 space to BOLD space (ITK format)

  • fallback – Boolean indicating whether BBR was rejected (rigid FLIRT registration returned)

Resampling workflows

fmriprep.workflows.bold.resampling.init_bold_surf_wf(*, mem_gb: float, surface_spaces: List[str], medial_surface_nan: bool, metadata: dict, output_dir: str, name: str = 'bold_surf_wf')[source]

Sample functional images to FreeSurfer surfaces.

For each vertex, the cortical ribbon is sampled at six points (spaced 20% of thickness apart) and averaged.

Outputs are in GIFTI format.

Workflow Graph
_images/api-11.png

(Source code, png, svg, pdf)

Parameters:
  • surface_spaces (list) – List of FreeSurfer surface-spaces (either fsaverage{3,4,5,6,} or fsnative) the functional images are to be resampled to. For fsnative, images will be resampled to the individual subject’s native surface.

  • medial_surface_nan (bool) – Replace medial wall values with NaNs on functional GIFTI files

Parameters:
  • source_file – Original BOLD series

  • bold_t1w – Motion-corrected BOLD series in T1 space

  • subjects_dir – FreeSurfer SUBJECTS_DIR

  • subject_id – FreeSurfer subject ID

  • fsnative2t1w_xfm – ITK-style affine matrix translating from FreeSurfer-conformed subject space to T1w

Parameters:

surfaces – BOLD series, resampled to FreeSurfer surfaces

fmriprep.workflows.bold.resampling.init_bold_fsLR_resampling_wf(grayord_density: Literal['91k', '170k'], omp_nthreads: int, mem_gb: float, name: str = 'bold_fsLR_resampling_wf')[source]

Resample BOLD time series to fsLR surface.

This workflow is derived heavily from three scripts within the DCAN-HCP pipelines scripts

Line numbers correspond to the locations of the code in the original scripts, found at: https://github.com/DCAN-Labs/DCAN-HCP/tree/9291324/

Workflow Graph
_images/api-12.png

(Source code, png, svg, pdf)

Parameters:
  • grayord_density (str) – Either "91k" or "170k", representing the total grayordinates.

  • omp_nthreads (int) – Maximum number of threads an individual process may use

  • mem_gb (float) – Size of BOLD file in GB

  • name (str) – Name of workflow (default: bold_fsLR_resampling_wf)

Parameters:
  • bold_file (str) – Path to BOLD file resampled into T1 space

  • white (list of str) – Path to left and right hemisphere white matter GIFTI surfaces.

  • pial (list of str) – Path to left and right hemisphere pial GIFTI surfaces.

  • midthickness (list of str) – Path to left and right hemisphere midthickness GIFTI surfaces.

  • midthickness_fsLR (list of str) – Path to left and right hemisphere midthickness GIFTI surfaces in fsLR space.

  • sphere_reg_fsLR (list of str) – Path to left and right hemisphere sphere.reg GIFTI surfaces, mapping from subject to fsLR

  • cortex_mask (list of str) – Path to left and right hemisphere cortical masks.

  • volume_roi (str or Undefined) – Pre-calculated goodvoxels mask. Not required.

Parameters:

bold_fsLR (list of str) – Path to BOLD series resampled as functional GIFTI files in fsLR space

fmriprep.workflows.bold.resampling.init_bold_grayords_wf(grayord_density: Literal['91k', '170k'], mem_gb: float, repetition_time: float, name: str = 'bold_grayords_wf')[source]

Sample Grayordinates files onto the fsLR atlas.

Outputs are in CIFTI2 format.

Workflow Graph
_images/api-13.png

(Source code, png, svg, pdf)

Parameters:
  • grayord_density (str) – Either "91k" or "170k", representing the total grayordinates.

  • mem_gb (float) – Size of BOLD file in GB

  • repetition_time (float) – Repetition time in seconds

  • name (str) – Unique name for the subworkflow (default: "bold_grayords_wf")

Parameters:
  • bold_fsLR (str) – List of paths to BOLD series resampled as functional GIFTI files in fsLR space

  • bold_std (str) – List of BOLD conversions to standard spaces.

  • spatial_reference (str) – List of unique identifiers corresponding to the BOLD standard-conversions.

Parameters:
  • cifti_bold (str) – BOLD CIFTI dtseries.

  • cifti_metadata (str) – BIDS metadata file corresponding to cifti_bold.

fmriprep.workflows.bold.resampling.init_goodvoxels_bold_mask_wf(mem_gb: float, name: str = 'goodvoxels_bold_mask_wf')[source]

Calculate a mask of a BOLD series excluding high variance voxels.

Workflow Graph
_images/api-14.png

(Source code, png, svg, pdf)

Parameters:
  • mem_gb (float) – Size of BOLD file in GB

  • name (str) – Name of workflow (default: goodvoxels_bold_mask_wf)

Parameters:
  • anat_ribbon – Cortical ribbon in T1w space

  • bold_file – Motion-corrected BOLD series in T1w space

Parameters:
  • masked_bold – BOLD series after masking outlier voxels with locally high COV

  • goodvoxels_ribbon – Cortical ribbon mask excluding voxels with locally high COV

Calculate BOLD confounds

fmriprep.workflows.bold.confounds.init_bold_confs_wf(mem_gb: float, metadata: dict, regressors_all_comps: bool, regressors_dvars_th: float, regressors_fd_th: float, freesurfer: bool = False, name: str = 'bold_confs_wf')[source]

Build a workflow to generate and write out confounding signals.

This workflow calculates confounds for a BOLD series, and aggregates them into a TSV file, for use as nuisance regressors in a GLM. The following confounds are calculated, with column headings in parentheses:

  1. Region-wise average signal (csf, white_matter, global_signal)

  2. DVARS - original and standardized variants (dvars, std_dvars)

  3. Framewise displacement, based on head-motion parameters (framewise_displacement)

  4. Temporal CompCor (t_comp_cor_XX)

  5. Anatomical CompCor (a_comp_cor_XX)

  6. Cosine basis set for high-pass filtering w/ 0.008 Hz cut-off (cosine_XX)

  7. Non-steady-state volumes (non_steady_state_XX)

  8. Estimated head-motion parameters, in mm and rad (trans_x, trans_y, trans_z, rot_x, rot_y, rot_z)

Prior to estimating aCompCor and tCompCor, non-steady-state volumes are censored and high-pass filtered using a DCT basis. The cosine basis, as well as one regressor per censored volume, are included for convenience.

Workflow Graph
_images/api-15.png

(Source code, png, svg, pdf)

Parameters:
  • mem_gb (float) – Size of BOLD file in GB - please note that this size should be calculated after resamplings that may extend the FoV

  • metadata (dict) – BIDS metadata for BOLD file

  • name (str) – Name of workflow (default: bold_confs_wf)

  • regressors_all_comps (bool) – Indicates whether CompCor decompositions should return all components instead of the minimal number of components necessary to explain 50 percent of the variance in the decomposition mask.

  • regressors_dvars_th (float) – Criterion for flagging DVARS outliers

  • regressors_fd_th (float) – Criterion for flagging framewise displacement outliers

Parameters:
  • bold – BOLD image, after the prescribed corrections (STC, HMC and SDC) when available.

  • bold_mask – BOLD series mask

  • movpar_file – SPM-formatted motion parameters file

  • rmsd_file – Root mean squared deviation as measured by fsl_motion_outliers [Jenkinson2002].

  • skip_vols – number of non steady state volumes

  • t1w_mask – Mask of the skull-stripped template image

  • t1w_tpms – List of tissue probability maps in T1w space

  • boldref2anat_xfm – Affine matrix that maps the BOLD reference space into alignment with the anatomical (T1w) space

Parameters:
  • confounds_file – TSV of all aggregated confounds

  • rois_report – Reportlet visualizing white-matter/CSF mask used for aCompCor, the ROI for tCompCor and the BOLD brain mask.

  • confounds_metadata – Confounds metadata dictionary.

  • crown_mask – Mask of brain edge voxels