The pyAFQ configuration file

This file should be a toml file. At minimum, the file should contain the BIDS path:

[files]
bids_path = "path/to/study"

But additional configuration options can be provided. See an example configuration file below:

title = "My AFQ analysis"

# Initialize a GroupAFQ object from a BIDS dataset.

# Use '' to indicate None
# Wrap dictionaries in quotes
# Wrap definition object instantiations in quotes

[BIDS_PARAMS]

# The path to preprocessed diffusion data organized in a BIDS
# dataset. This should contain a BIDS derivative dataset with
# preprocessed dwi/bvals/bvecs.
bids_path = ""

# Filter to pass to bids_layout.get when finding DWI files.
# Default: {"suffix": "dwi"}
bids_filters = "{'suffix': 'dwi'}"

# The name of the pipeline used to preprocess the DWI data.
# Default: "all".
preproc_pipeline = "all"

# List of participant labels (subject IDs) to perform
# processing on. If None, all subjects are used.
# Default: None
participant_labels = ""

# Path to output directory. If None, outputs are put
# in a AFQ pipeline folder in the derivatives folder of
# the BIDS directory. pyAFQ will use existing derivatives
# from the output directory if they exist, instead of recalculating
# them (this means you need to clear the output folder if you want
# to recalculate a derivative).
# Default: None
output_dir = ""

# Parameters to pass to paramap in AFQ.utils.parallel,
# to parallelize computations across subjects and sessions.
# Set "n_jobs" to -1 to automatically parallelize as
# the number of cpus. Here is an example for how to do
# multiprocessing with 4 cpus:
# {"n_jobs": 4, "engine": "joblib", "backend": "loky"}
# Default: {"engine": "serial"}
parallel_params = "{'engine': 'serial'}"

# Additional arguments to give to BIDSLayout from pybids.
# For large datasets, try:
# {"validate": False, "index_metadata": False}
# Default: {}
bids_layout_kwargs = "{}"

[TRACTOGRAPHY_PARAMS]

# How tracking directions are determined.
# One of: {"det" | "prob"}
# Default: "prob"
directions = "prob"

# The maximum turning angle in each step. Default: 30
max_angle = 30.0

# The discretization of direction getting. default:
# dipy.data.default_sphere.
sphere = ""

# Float or binary mask describing the ROI within which we seed for
# tracking.
# Default to the entire volume (all ones).
seed_mask = ""

# A value of the seed_mask above which tracking is seeded.
# Default to 0.
seed_threshold = 0

# Interpret seed_threshold and stop_threshold as percentages of the
# total non-nan voxels in the seed and stop mask to include
# (between 0 and 100), instead of as a threshold on the
# values themselves.
# Default: False
thresholds_as_percentages = false

# The seeding density: if this is an int, it is is how many seeds in each
# voxel on each dimension (for example, 2 => [2, 2, 2]). If this is a 2D
# array, these are the coordinates of the seeds. Unless random_seeds is
# set to True, in which case this is the total number of random seeds
# to generate within the mask. Default: 1
n_seeds = 1

# Whether to generate a total of n_seeds random seeds in the mask.
# Default: False.
random_seeds = false

# random seed used to generate random seeds if random_seeds is
# set to True. Default: None
rng_seed = ""

# If array: A float or binary mask that determines a stopping criterion
# (e.g. FA).
# If tuple: it contains a sequence that is interpreted as:
# (pve_wm, pve_gm, pve_csf), each item of which is either a string
# (full path) or a nibabel img to be used in particle filtering
# tractography.
# A tuple is required if tracker is set to "pft".
# Defaults to no stopping (all ones).
stop_mask = ""

# If float, this a value of the stop_mask below which tracking is
# terminated (and stop_mask has to be an array).
# If str, "CMC" for Continuous Map Criterion [Girard2014]_.
# "ACT" for Anatomically-constrained tractography [Smith2012]_.
# A string is required if the tracker is set to "pft".
# Defaults to 0 (this means that if no stop_mask is passed,
# we will stop only at the edge of the image).
stop_threshold = 0

# The size of a step (in mm) of tractography. Default: 0.5
step_size = 0.5

# The miminal length (mm) in a streamline. Default: 20
minlen = 50

# The miminal length (mm) in a streamline. Default: 250
maxlen = 250

# One of {"DTI", "CSD", "DKI"}. Defaults to use "DTI"
odf_model = "CSD"

# Which strategy to use in tracking. This can be the standard local
# tracking ("local") or Particle Filtering Tracking ([Girard2014]_).
# One of {"local", "pft"}. Default: "local"
tracker = "local"

# Whether to return the streamlines compatible with input to TRX file
# (i.e., as a LazyTractogram class instance).
# Default: False
trx = false

[SEGMENTATION_PARAMS]

# Resample streamlines to nb_points number of points.
# If False, no resampling is done. Default: False
nb_points = false

# Subsample streamlines to nb_streamlines.
# If False, no subsampling is don. Default: False
nb_streamlines = false

# Whether to clip the streamlines to be only in between the ROIs.
# Default: False
clip_edges = false

# How to parallelize segmentation across processes when performing
# waypoint ROI segmentation. Set to {"engine": "serial"} to not
# perform parallelization. Some engines may cause errors, depending
# on the system. See ``dipy.utils.parallel.paramap`` for
# details.
# Default: {"engine": "serial"}
parallel_segmentation = "{'engine': 'serial'}"

# RecoBundles parameters for the recognize function.
# Default: dict(model_clust_thr=1.25, reduction_thr=25, pruning_thr=12)
rb_recognize_params = "{'model_clust_thr': 1.25, 'reduction_thr': 25, 'pruning_thr': 12}"

# Whether to refine the RecoBundles segmentation.
# Default: False
refine_reco = false

# Using AFQ Algorithm.
# Initial cleaning of fiber groups is done using probability maps
# from [Hua2008]_. Here, we choose an average probability that
# needs to be exceeded for an individual streamline to be retained.
# Default: 0.
prob_threshold = 0

# The distance that a streamline node has to be from the waypoint
# ROI in order to be included or excluded.
# If set to None (default), will be calculated as the
# center-to-corner distance of the voxel in the diffusion data.
# If a bundle has inc_addtol or exc_addtol in its bundle_dict, that
# tolerance will be added to this distance.
# For example, if you wanted to increase tolerance for the right
# arcuate waypoint ROIs by 3 each, you could make the following
# modification to your bundle_dict:
# bundle_dict["Right Arcuate"]["inc_addtol"] = [3, 3]
# Additional tolerances can also be negative.
# Default: None.
dist_to_waypoint = ""

# If None, creates RandomState.
# If int, creates RandomState with seed rng.
# Used in RecoBundles Algorithm.
# Default: None.
rng = ""

# Whether to return the indices in the original streamlines as part
# of the output of segmentation.
# Default: False.
return_idx = false

# Whether to filter the bundles based on their endpoints.
# Default: True.
filter_by_endpoints = true

# If filter_by_endpoints is True, this is the required distance
# from the endpoints to the atlas ROIs.
# Default: 4
dist_to_atlas = 4

# The full path to a folder into which intermediate products
# are saved. Default: None, means no saving of intermediates.
save_intermediates = ""

# Cleaning params to pass to seg.clean_bundle. This will
# override the default parameters of that method. However, this
# can be overriden by setting the cleaning parameters in the
# bundle_dict. Default: {}.
cleaning_params = "{}"

[CLEANING_PARAMS]

# Number of points to resample streamlines to.
# Default: 100
n_points = 100

# Number of rounds of cleaning based on the Mahalanobis distance from
# the mean of extracted bundles. Default: 5
clean_rounds = 5

# Threshold of cleaning based on the Mahalanobis distance (the units are
# standard deviations). Default: 3.
distance_threshold = 3

# Threshold for cleaning based on length (in standard deviations). Length
# of any streamline should not be *more* than this number of stdevs from
# the mean length.
length_threshold = 4

# Number of streamlines in a bundle under which we will
# not bother with cleaning outliers. Default: 20.
min_sl = 20

# The statistic of each node relative to which the Mahalanobis is
# calculated. Default: `np.mean` (but can also use median, etc.)
stat = "mean"

# Whether to return indices in the original streamlines.
# Default: False.
return_idx = false

[DATA]

# Minimum b value you want to use
# from the dataset (other than b0), inclusive.
# If None, there is no minimum limit. Default: None
min_bval = ""

# Maximum b value you want to use
# from the dataset (other than b0), inclusive.
# If None, there is no maximum limit. Default: None
max_bval = ""

# Whether to filter the DWI data based on min or max bvals.
# Default: True
filter_b = true

# The value of b under which
# it is considered to be b0. Default: 50.
b0_threshold = 50

# Whether to use robust_tensor_fitting when
# doing dti. Only applies to dti.
# Default: False
robust_tensor_fitting = false

# The response function to be used by CSD, as a tuple with two elements.
# The first is the eigen-values as an (3,) ndarray and the second is
# the signal value for the response function without diffusion-weighting
# (i.e. S0). If not provided, auto_response will be used to calculate
# these values.
# Default: None
csd_response = ""

# default: infer the number of parameters from the number of data
# volumes, but no larger than 8.
# Default: None
csd_sh_order = ""

# weight given to the constrained-positivity regularization part of
# the deconvolution equation. Default: 1
csd_lambda_ = 1

# threshold controlling the amplitude below which the corresponding
# fODF is assumed to be zero.  Ideally, tau should be set to
# zero. However, to improve the stability of the algorithm, tau is
# set to tau*100 percent of the mean fODF amplitude (here, 10 percent
# by default)
# (see [1]_). Default: 0.1
csd_tau = 0.1

# The threshold on the FA used to calculate the single shell auto
# response. Can be useful to reduce for baby subjects. Default: 0.7
csd_fa_thr = 0.7

# Diffusion sampling length.
# Default: 1.2
gq_sampling_length = 1.2

# Able to take response[0] from auto_response_ssst.
# default: array([0.0017, 0.0002, 0.0002])
rumba_wm_response = "[0.0017, 0.0002, 0.0002]"

# Mean diffusivity for GM compartment.
# If None, then grey matter volume fraction is not computed.
# Default: 0.8e-3
rumba_gm_response = 0.0008

# Mean diffusivity for CSF compartment.
# If None, then CSF volume fraction is not computed.
# Default: 3.0e-3
rumba_csf_response = 0.003

# Number of iterations for fODF estimation.
# Must be a positive int.
# Default: 600
rumba_n_iter = 600

# Spherical harmonics order for OPDT model. Must be even.
# Default: 8
opdt_sh_order = 8

# Spherical harmonics order for CSA model. Must be even.
# Default: 8
csa_sh_order = 8

# The sphere providing sample directions for the initial
# search of the maximal value of kurtosis.
# Default: 'repulsion100'
sphere = "repulsion100"

# This input is to refine kurtosis maxima under the precision of
# the directions sampled on the sphere class instance.
# The gradient of the convergence procedure must be less than gtol
# before successful termination.
# If gtol is None, fiber direction is directly taken from the initial
# sampled directions of the given sphere object.
# Default: 1e-2
gtol = 0.01

# This will be used to create
# the brain mask, which gets applied before registration to a
# template.
# If you want no brain mask to be applied, use FullImage.
# If None, use B0Image()
# Default: None
brain_mask_definition = ""

# A dictionary or BundleDict for use in segmentation.
# See `Defining Custom Bundle Dictionaries`
# in the `usage` section of pyAFQ's documentation for details.
# If None, will get all appropriate bundles for the chosen
# segmentation algorithm.
# Default: None
bundle_info = ""

# The target image data for registration.
# Can either be a Nifti1Image, a path to a Nifti1Image, or
# if "mni_T2", "dti_fa_template", "hcp_atlas", or "mni_T1",
# image data will be loaded automatically.
# If "hcp_atlas" is used, slr registration will be used
# and reg_subject should be "subject_sls".
# Default: "mni_T1"
reg_template_spec = "mni_T1"

[MAPPING]

# This defines how to either create a mapping from
# each subject space to template space or load a mapping from
# another software. If creating a map, will register reg_subject and
# reg_template.
# If None, use SynMap()
# Default: None
mapping_definition = ""

# The source image data to be registered.
# Can either be a Nifti1Image, an ImageFile, or str.
# if "b0", "dti_fa_subject", "subject_sls", or "power_map,"
# image data will be loaded automatically.
# If "subject_sls" is used, slr registration will be used
# and reg_template should be "hcp_atlas".
# Default: "power_map"
reg_subject_spec = "power_map"

[SEGMENTATION]

# How to weight each streamline (1D) or each node (2D)
# when calculating the tract-profiles. If callable, this is a
# function that calculates weights. If None, no weighting will
# be applied. If "gauss", gaussian weights will be used.
# If "median", the median of values at each node will be used
# instead of a mean or weighted mean.
# Default: "gauss"
profile_weights = "gauss"

# Number of points to resample each streamline to before
# calculating the tract-profiles.
# Default: 100
n_points_profile = 100

# List of scalars to use.
# Can be any of: "dti_fa", "dti_md", "dki_fa", "dki_md", "dki_awf",
# "dki_mk". Can also be a scalar from AFQ.definitions.image.
# Default: ["dti_fa", "dti_md"]
scalars = "['dti_fa', 'dti_md']"

[TRACTOGRAPHY]

# BIDS filters for inputing a user made tractography file,
# or a path to the tractography file. If None, DIPY is used
# to generate the tractography.
# Default: None
import_tract = ""

# Number of GPUs to use in tractography. If non-0,
# this algorithm is used for tractography,
# https://github.com/dipy/GPUStreamlines
# Default: 0
tractography_ngpus = 0

# Chunk size for GPU tracking.
# Default: 100000
chunk_size = 100000

[VIZ]

# Of the form (lower bound, upper bound). Shading based on
# shade_by_volume will only differentiate values within these bounds.
# If lower bound is None, will default to 0.
# If upper bound is None, will default to the maximum value in
# shade_by_volume.
# Default: [None, None]
sbv_lims_bundles = "[None, None]"

# Opacity of volume slices.
# Default: 0.3
volume_opacity_bundles = 0.3

# n_points to resample streamlines to before plotting. If None, no
# resampling is done.
# Default: 40
n_points_bundles = 40

# Of the form (lower bound, upper bound). Shading based on
# shade_by_volume will only differentiate values within these bounds.
# If lower bound is None, will default to 0.
# If upper bound is None, will default to the maximum value in
# shade_by_volume.
# Default: [None, None]
sbv_lims_indiv = "[None, None]"

# Opacity of volume slices.
# Default: 0.3
volume_opacity_indiv = 0.3

# n_points to resample streamlines to before plotting. If None, no
# resampling is done.
# Default: 40
n_points_indiv = 40

# Which visualization backend to use.
# See Visualization Backends page in documentation for details:
# https://tractometry.org/pyAFQ/usage/viz_backend.html
# One of {"fury", "plotly", "plotly_no_gif"}.
# Default: "plotly_no_gif"
viz_backend_spec = "plotly_no_gif"

# Whether to use a virtual fram buffer. This is neccessary if
# generating GIFs in a headless environment. Default: False
virtual_frame_buffer = false

pyAFQ will store a copy of the configuration file alongside the computed results. Note that the title variable and [metadata] section are both for users to enter any title/metadata they would like and pyAFQ will generally ignore them.