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confounds.py
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confounds.py
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# -*- coding: utf-8 -*-
# emacs: -*- mode: python; py-indent-offset: 4; indent-tabs-mode: nil -*-
# vi: set ft=python sts=4 ts=4 sw=4 et:
"""
Algorithms to compute confounds in :abbr:`fMRI (functional MRI)`
"""
import os
import os.path as op
from collections import OrderedDict
from itertools import chain
import nibabel as nb
import numpy as np
from numpy.polynomial import Legendre
from .. import config, logging
from ..external.due import BibTeX
from ..interfaces.base import (
traits,
TraitedSpec,
BaseInterface,
BaseInterfaceInputSpec,
File,
isdefined,
InputMultiPath,
OutputMultiPath,
SimpleInterface,
)
from ..utils.misc import normalize_mc_params
IFLOGGER = logging.getLogger("nipype.interface")
def fallback_svd(a, full_matrices=True, compute_uv=True):
try:
return np.linalg.svd(a, full_matrices=full_matrices, compute_uv=compute_uv)
except np.linalg.LinAlgError:
pass
from scipy.linalg import svd
return svd(
a, full_matrices=full_matrices, compute_uv=compute_uv, lapack_driver="gesvd"
)
class ComputeDVARSInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="functional data, after HMC")
in_mask = File(exists=True, mandatory=True, desc="a brain mask")
remove_zerovariance = traits.Bool(
True, usedefault=True, desc="remove voxels with zero variance"
)
variance_tol = traits.Float(
1e-7,
usedefault=True,
desc="maximum variance to consider \"close to\" zero for the purposes of removal",
)
save_std = traits.Bool(True, usedefault=True, desc="save standardized DVARS")
save_nstd = traits.Bool(False, usedefault=True, desc="save non-standardized DVARS")
save_vxstd = traits.Bool(
False, usedefault=True, desc="save voxel-wise standardized DVARS"
)
save_all = traits.Bool(False, usedefault=True, desc="output all DVARS")
series_tr = traits.Float(desc="repetition time in sec.")
save_plot = traits.Bool(False, usedefault=True, desc="write DVARS plot")
figdpi = traits.Int(100, usedefault=True, desc="output dpi for the plot")
figsize = traits.Tuple(
traits.Float(11.7),
traits.Float(2.3),
usedefault=True,
desc="output figure size",
)
figformat = traits.Enum(
"png", "pdf", "svg", usedefault=True, desc="output format for figures"
)
intensity_normalization = traits.Float(
1000.0,
usedefault=True,
desc="Divide value in each voxel at each timepoint "
"by the median calculated across all voxels"
"and timepoints within the mask (if specified)"
"and then multiply by the value specified by"
"this parameter. By using the default (1000)"
"output DVARS will be expressed in "
"x10 % BOLD units compatible with Power et al."
"2012. Set this to 0 to disable intensity"
"normalization altogether.",
)
class ComputeDVARSOutputSpec(TraitedSpec):
out_std = File(exists=True, desc="output text file")
out_nstd = File(exists=True, desc="output text file")
out_vxstd = File(exists=True, desc="output text file")
out_all = File(exists=True, desc="output text file")
avg_std = traits.Float()
avg_nstd = traits.Float()
avg_vxstd = traits.Float()
fig_std = File(exists=True, desc="output DVARS plot")
fig_nstd = File(exists=True, desc="output DVARS plot")
fig_vxstd = File(exists=True, desc="output DVARS plot")
class ComputeDVARS(BaseInterface):
"""
Computes the DVARS.
"""
input_spec = ComputeDVARSInputSpec
output_spec = ComputeDVARSOutputSpec
_references = [
{
"entry": BibTeX(
"""\
@techreport{nichols_notes_2013,
address = {Coventry, UK},
title = {Notes on {Creating} a {Standardized} {Version} of {DVARS}},
url = {http://www2.warwick.ac.uk/fac/sci/statistics/staff/academic-\
research/nichols/scripts/fsl/standardizeddvars.pdf},
urldate = {2016-08-16},
institution = {University of Warwick},
author = {Nichols, Thomas},
year = {2013}
}"""
),
"tags": ["method"],
},
{
"entry": BibTeX(
"""\
@article{power_spurious_2012,
title = {Spurious but systematic correlations in functional connectivity {MRI} networks \
arise from subject motion},
volume = {59},
doi = {10.1016/j.neuroimage.2011.10.018},
number = {3},
urldate = {2016-08-16},
journal = {NeuroImage},
author = {Power, Jonathan D. and Barnes, Kelly A. and Snyder, Abraham Z. and Schlaggar, \
Bradley L. and Petersen, Steven E.},
year = {2012},
pages = {2142--2154},
}
"""
),
"tags": ["method"],
},
]
def __init__(self, **inputs):
self._results = {}
super(ComputeDVARS, self).__init__(**inputs)
def _gen_fname(self, suffix, ext=None):
fname, in_ext = op.splitext(op.basename(self.inputs.in_file))
if in_ext == ".gz":
fname, in_ext2 = op.splitext(fname)
in_ext = in_ext2 + in_ext
if ext is None:
ext = in_ext
if ext.startswith("."):
ext = ext[1:]
return op.abspath("{}_{}.{}".format(fname, suffix, ext))
def _run_interface(self, runtime):
dvars = compute_dvars(
self.inputs.in_file,
self.inputs.in_mask,
remove_zerovariance=self.inputs.remove_zerovariance,
variance_tol=self.inputs.variance_tol,
intensity_normalization=self.inputs.intensity_normalization,
)
(
self._results["avg_std"],
self._results["avg_nstd"],
self._results["avg_vxstd"],
) = np.mean(dvars, axis=1).astype(float)
tr = None
if isdefined(self.inputs.series_tr):
tr = self.inputs.series_tr
if self.inputs.save_std:
out_file = self._gen_fname("dvars_std", ext="tsv")
np.savetxt(out_file, dvars[0], fmt=b"%0.6f")
self._results["out_std"] = out_file
if self.inputs.save_plot:
self._results["fig_std"] = self._gen_fname(
"dvars_std", ext=self.inputs.figformat
)
fig = plot_confound(
dvars[0], self.inputs.figsize, "Standardized DVARS", series_tr=tr
)
fig.savefig(
self._results["fig_std"],
dpi=float(self.inputs.figdpi),
format=self.inputs.figformat,
bbox_inches="tight",
)
fig.clf()
if self.inputs.save_nstd:
out_file = self._gen_fname("dvars_nstd", ext="tsv")
np.savetxt(out_file, dvars[1], fmt=b"%0.6f")
self._results["out_nstd"] = out_file
if self.inputs.save_plot:
self._results["fig_nstd"] = self._gen_fname(
"dvars_nstd", ext=self.inputs.figformat
)
fig = plot_confound(
dvars[1], self.inputs.figsize, "DVARS", series_tr=tr
)
fig.savefig(
self._results["fig_nstd"],
dpi=float(self.inputs.figdpi),
format=self.inputs.figformat,
bbox_inches="tight",
)
fig.clf()
if self.inputs.save_vxstd:
out_file = self._gen_fname("dvars_vxstd", ext="tsv")
np.savetxt(out_file, dvars[2], fmt=b"%0.6f")
self._results["out_vxstd"] = out_file
if self.inputs.save_plot:
self._results["fig_vxstd"] = self._gen_fname(
"dvars_vxstd", ext=self.inputs.figformat
)
fig = plot_confound(
dvars[2], self.inputs.figsize, "Voxelwise std DVARS", series_tr=tr
)
fig.savefig(
self._results["fig_vxstd"],
dpi=float(self.inputs.figdpi),
format=self.inputs.figformat,
bbox_inches="tight",
)
fig.clf()
if self.inputs.save_all:
out_file = self._gen_fname("dvars", ext="tsv")
np.savetxt(
out_file,
np.vstack(dvars).T,
fmt=b"%0.8f",
delimiter=b"\t",
header="std DVARS\tnon-std DVARS\tvx-wise std DVARS",
comments="",
)
self._results["out_all"] = out_file
return runtime
def _list_outputs(self):
return self._results
class FramewiseDisplacementInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="motion parameters")
parameter_source = traits.Enum(
"FSL",
"AFNI",
"SPM",
"FSFAST",
"NIPY",
desc="Source of movement parameters",
mandatory=True,
)
radius = traits.Float(
50,
usedefault=True,
desc="radius in mm to calculate angular FDs, 50mm is the "
"default since it is used in Power et al. 2012",
)
out_file = File("fd_power_2012.txt", usedefault=True, desc="output file name")
out_figure = File("fd_power_2012.pdf", usedefault=True, desc="output figure name")
series_tr = traits.Float(desc="repetition time in sec.")
save_plot = traits.Bool(False, usedefault=True, desc="write FD plot")
normalize = traits.Bool(False, usedefault=True, desc="calculate FD in mm/s")
figdpi = traits.Int(100, usedefault=True, desc="output dpi for the FD plot")
figsize = traits.Tuple(
traits.Float(11.7),
traits.Float(2.3),
usedefault=True,
desc="output figure size",
)
class FramewiseDisplacementOutputSpec(TraitedSpec):
out_file = File(desc="calculated FD per timestep")
out_figure = File(desc="output image file")
fd_average = traits.Float(desc="average FD")
class FramewiseDisplacement(BaseInterface):
"""
Calculate the :abbr:`FD (framewise displacement)` as in [Power2012]_.
This implementation reproduces the calculation in fsl_motion_outliers
.. [Power2012] Power et al., Spurious but systematic correlations in functional
connectivity MRI networks arise from subject motion, NeuroImage 59(3),
2012. doi:`10.1016/j.neuroimage.2011.10.018
<https://doi.org/10.1016/j.neuroimage.2011.10.018>`_.
"""
input_spec = FramewiseDisplacementInputSpec
output_spec = FramewiseDisplacementOutputSpec
_references = [
{
"entry": BibTeX(
"""\
@article{power_spurious_2012,
title = {Spurious but systematic correlations in functional connectivity {MRI} networks \
arise from subject motion},
volume = {59},
doi = {10.1016/j.neuroimage.2011.10.018},
number = {3},
urldate = {2016-08-16},
journal = {NeuroImage},
author = {Power, Jonathan D. and Barnes, Kelly A. and Snyder, Abraham Z. and Schlaggar, \
Bradley L. and Petersen, Steven E.},
year = {2012},
pages = {2142--2154},
}
"""
),
"tags": ["method"],
}
]
def _run_interface(self, runtime):
mpars = np.loadtxt(self.inputs.in_file) # mpars is N_t x 6
mpars = np.apply_along_axis(
func1d=normalize_mc_params,
axis=1,
arr=mpars,
source=self.inputs.parameter_source,
)
diff = mpars[:-1, :6] - mpars[1:, :6]
diff[:, 3:6] *= self.inputs.radius
fd_res = np.abs(diff).sum(axis=1)
self._results = {
"out_file": op.abspath(self.inputs.out_file),
"fd_average": float(fd_res.mean()),
}
np.savetxt(
self.inputs.out_file, fd_res, header="FramewiseDisplacement", comments=""
)
if self.inputs.save_plot:
tr = None
if isdefined(self.inputs.series_tr):
tr = self.inputs.series_tr
if self.inputs.normalize and tr is None:
IFLOGGER.warning("FD plot cannot be normalized if TR is not set")
self._results["out_figure"] = op.abspath(self.inputs.out_figure)
fig = plot_confound(
fd_res,
self.inputs.figsize,
"FD",
units="mm",
series_tr=tr,
normalize=self.inputs.normalize,
)
fig.savefig(
self._results["out_figure"],
dpi=float(self.inputs.figdpi),
format=self.inputs.out_figure[-3:],
bbox_inches="tight",
)
fig.clf()
return runtime
def _list_outputs(self):
return self._results
class CompCorInputSpec(BaseInterfaceInputSpec):
realigned_file = File(
exists=True, mandatory=True, desc="already realigned brain image (4D)"
)
mask_files = InputMultiPath(
File(exists=True),
desc=(
"One or more mask files that determines "
"ROI (3D). When more that one file is "
"provided ``merge_method`` or "
"``merge_index`` must be provided"
),
)
merge_method = traits.Enum(
"union",
"intersect",
"none",
xor=["mask_index"],
requires=["mask_files"],
desc=(
"Merge method if multiple masks are "
"present - ``union`` uses voxels included in"
" at least one input mask, ``intersect`` "
"uses only voxels present in all input "
"masks, ``none`` performs CompCor on "
"each mask individually"
),
)
mask_index = traits.Range(
low=0,
xor=["merge_method"],
requires=["mask_files"],
desc="Position of mask in ``mask_files`` to use - first is the default.",
)
mask_names = traits.List(
traits.Str,
desc="Names for provided masks (for printing into metadata). "
"If provided, it must be as long as the final mask list "
"(after any merge and indexing operations).",
)
components_file = traits.Str(
"components_file.txt",
usedefault=True,
desc="Filename to store physiological components",
)
num_components = traits.Either(
"all",
traits.Range(low=1),
xor=["variance_threshold"],
desc="Number of components to return from the decomposition. If "
"``num_components`` is ``all``, then all components will be "
"retained.",
)
# 6 for BOLD, 4 for ASL
# automatically instantiated to 6 in CompCor below if neither
# ``num_components`` nor ``variance_threshold`` is defined (for
# backward compatibility)
variance_threshold = traits.Range(
low=0.0,
high=1.0,
exclude_low=True,
exclude_high=True,
xor=["num_components"],
desc="Select the number of components to be returned automatically "
"based on their ability to explain variance in the dataset. "
"``variance_threshold`` is a fractional value between 0 and 1; "
"the number of components retained will be equal to the minimum "
"number of components necessary to explain the provided "
"fraction of variance in the masked time series.",
)
pre_filter = traits.Enum(
"polynomial",
"cosine",
False,
usedefault=True,
desc="Detrend time series prior to component " "extraction",
)
use_regress_poly = traits.Bool(
deprecated="0.15.0",
new_name="pre_filter",
desc=("use polynomial regression " "pre-component extraction"),
)
regress_poly_degree = traits.Range(
low=1, value=1, usedefault=True, desc="the degree polynomial to use"
)
header_prefix = traits.Str(
desc=(
"the desired header for the output tsv "
"file (one column). If undefined, will "
'default to "CompCor"'
)
)
high_pass_cutoff = traits.Float(
128, usedefault=True, desc='Cutoff (in seconds) for "cosine" pre-filter'
)
repetition_time = traits.Float(
desc="Repetition time (TR) of series - derived from image header if "
"unspecified"
)
save_pre_filter = traits.Either(
traits.Bool,
File,
default=False,
usedefault=True,
desc="Save pre-filter basis as text file",
)
save_metadata = traits.Either(
traits.Bool,
File,
default=False,
usedefault=True,
desc="Save component metadata as text file",
)
ignore_initial_volumes = traits.Range(
low=0, usedefault=True, desc="Number of volumes at start of series to ignore"
)
failure_mode = traits.Enum(
"error",
"NaN",
usedefault=True,
desc="When no components are found or convergence fails, raise an error "
"or silently return columns of NaNs.",
)
class CompCorOutputSpec(TraitedSpec):
components_file = File(
exists=True, desc="text file containing the noise components"
)
pre_filter_file = File(desc="text file containing high-pass filter basis")
metadata_file = File(desc="text file containing component metadata")
class CompCor(SimpleInterface):
"""
Interface with core CompCor computation, used in aCompCor and tCompCor.
CompCor provides three pre-filter options, all of which include per-voxel
mean removal:
- ``'polynomial'``: Legendre polynomial basis
- ``'cosine'``: Discrete cosine basis
- ``False``: mean-removal only
In the case of ``polynomial`` and ``cosine`` filters, a pre-filter file may
be saved with a row for each volume/timepoint, and a column for each
non-constant regressor.
If no non-constant (mean-removal) columns are used, this file may be empty.
If ``ignore_initial_volumes`` is set, then the specified number of initial
volumes are excluded both from pre-filtering and CompCor component
extraction.
Each column in the components and pre-filter files are prefixe with zeros
for each excluded volume so that the number of rows continues to match the
number of volumes in the input file.
In addition, for each excluded volume, a column is added to the pre-filter
file with a 1 in the corresponding row.
Example
-------
>>> ccinterface = CompCor()
>>> ccinterface.inputs.realigned_file = 'functional.nii'
>>> ccinterface.inputs.mask_files = 'mask.nii'
>>> ccinterface.inputs.num_components = 1
>>> ccinterface.inputs.pre_filter = 'polynomial'
>>> ccinterface.inputs.regress_poly_degree = 2
"""
input_spec = CompCorInputSpec
output_spec = CompCorOutputSpec
_references = [
{
"tags": ["method", "implementation"],
"entry": BibTeX(
"""\
@article{compcor_2007,
title = {A component based noise correction method (CompCor) for BOLD and perfusion based},
volume = {37},
number = {1},
doi = {10.1016/j.neuroimage.2007.04.042},
urldate = {2016-08-13},
journal = {NeuroImage},
author = {Behzadi, Yashar and Restom, Khaled and Liau, Joy and Liu, Thomas T.},
year = {2007},
pages = {90-101}
}"""
),
}
]
def __init__(self, *args, **kwargs):
"""exactly the same as compcor except the header"""
super(CompCor, self).__init__(*args, **kwargs)
self._header = "CompCor"
def _run_interface(self, runtime):
mask_images = []
if isdefined(self.inputs.mask_files):
mask_images = combine_mask_files(
self.inputs.mask_files, self.inputs.merge_method, self.inputs.mask_index
)
if self.inputs.use_regress_poly:
self.inputs.pre_filter = "polynomial"
# Degree 0 == remove mean; see compute_noise_components
degree = (
self.inputs.regress_poly_degree
if self.inputs.pre_filter == "polynomial"
else 0
)
imgseries = nb.load(self.inputs.realigned_file)
if len(imgseries.shape) != 4:
raise ValueError(
"{} expected a 4-D nifti file. Input {} has "
"{} dimensions (shape {})".format(
self._header,
self.inputs.realigned_file,
len(imgseries.shape),
imgseries.shape,
)
)
if len(mask_images) == 0:
img = nb.Nifti1Image(
np.ones(imgseries.shape[:3], dtype=bool),
affine=imgseries.affine,
header=imgseries.header,
)
mask_images = [img]
skip_vols = self.inputs.ignore_initial_volumes
if skip_vols:
imgseries = imgseries.__class__(
imgseries.dataobj[..., skip_vols:], imgseries.affine, imgseries.header
)
mask_images = self._process_masks(mask_images, imgseries.dataobj)
TR = 0
if self.inputs.pre_filter == "cosine":
if isdefined(self.inputs.repetition_time):
TR = self.inputs.repetition_time
else:
# Derive TR from NIfTI header, if possible
try:
TR = imgseries.header.get_zooms()[3]
if imgseries.header.get_xyzt_units()[1] == "msec":
TR /= 1000
except (AttributeError, IndexError):
TR = 0
if TR == 0:
raise ValueError(
"{} cannot detect repetition time from image - "
"Set the repetition_time input".format(self._header)
)
if isdefined(self.inputs.variance_threshold):
components_criterion = self.inputs.variance_threshold
elif isdefined(self.inputs.num_components):
components_criterion = self.inputs.num_components
else:
components_criterion = 6
IFLOGGER.warning(
"`num_components` and `variance_threshold` are "
"not defined. Setting number of components to 6 "
"for backward compatibility. Please set either "
"`num_components` or `variance_threshold`, as "
"this feature may be deprecated in the future."
)
components, filter_basis, metadata = compute_noise_components(
imgseries.get_fdata(dtype=np.float32),
mask_images,
components_criterion,
self.inputs.pre_filter,
degree,
self.inputs.high_pass_cutoff,
TR,
self.inputs.failure_mode,
self.inputs.mask_names,
)
if skip_vols:
old_comp = components
nrows = skip_vols + components.shape[0]
components = np.zeros((nrows, components.shape[1]), dtype=components.dtype)
components[skip_vols:] = old_comp
components_file = os.path.join(os.getcwd(), self.inputs.components_file)
components_header = self._make_headers(components.shape[1])
np.savetxt(
components_file,
components,
fmt=b"%.10f",
delimiter="\t",
header="\t".join(components_header),
comments="",
)
self._results["components_file"] = os.path.join(
runtime.cwd, self.inputs.components_file
)
save_pre_filter = False
if self.inputs.pre_filter in ["polynomial", "cosine"]:
save_pre_filter = self.inputs.save_pre_filter
if save_pre_filter:
self._results["pre_filter_file"] = save_pre_filter
if save_pre_filter is True:
self._results["pre_filter_file"] = os.path.join(
runtime.cwd, "pre_filter.tsv"
)
ftype = {"polynomial": "Legendre", "cosine": "Cosine"}[
self.inputs.pre_filter
]
ncols = filter_basis.shape[1] if filter_basis.size > 0 else 0
header = ["{}{:02d}".format(ftype, i) for i in range(ncols)]
if skip_vols:
old_basis = filter_basis
# nrows defined above
filter_basis = np.zeros(
(nrows, ncols + skip_vols), dtype=filter_basis.dtype
)
if old_basis.size > 0:
filter_basis[skip_vols:, :ncols] = old_basis
filter_basis[:skip_vols, -skip_vols:] = np.eye(skip_vols)
header.extend(
["NonSteadyStateOutlier{:02d}".format(i) for i in range(skip_vols)]
)
np.savetxt(
self._results["pre_filter_file"],
filter_basis,
fmt=b"%.10f",
delimiter="\t",
header="\t".join(header),
comments="",
)
metadata_file = self.inputs.save_metadata
if metadata_file:
self._results["metadata_file"] = metadata_file
if metadata_file is True:
self._results["metadata_file"] = os.path.join(
runtime.cwd, "component_metadata.tsv"
)
components_names = np.empty(len(metadata["mask"]), dtype="object_")
retained = np.where(metadata["retained"])
not_retained = np.where(np.logical_not(metadata["retained"]))
components_names[retained] = components_header
components_names[not_retained] = [
"dropped{}".format(i) for i in range(len(not_retained[0]))
]
with open(self._results["metadata_file"], "w") as f:
f.write("\t".join(["component"] + list(metadata.keys())) + "\n")
for i in zip(components_names, *metadata.values()):
f.write(
"{0[0]}\t{0[1]}\t{0[2]:.10f}\t"
"{0[3]:.10f}\t{0[4]:.10f}\t{0[5]}\n".format(i)
)
return runtime
def _process_masks(self, mask_images, timeseries=None):
return mask_images
def _make_headers(self, num_col):
header = (
self.inputs.header_prefix
if isdefined(self.inputs.header_prefix)
else self._header
)
headers = ["{}{:02d}".format(header, i) for i in range(num_col)]
return headers
class ACompCor(CompCor):
"""
Anatomical compcor: for inputs and outputs, see CompCor.
When the mask provided is an anatomical mask, then CompCor
is equivalent to ACompCor.
"""
def __init__(self, *args, **kwargs):
"""exactly the same as compcor except the header"""
super(ACompCor, self).__init__(*args, **kwargs)
self._header = "aCompCor"
class TCompCorInputSpec(CompCorInputSpec):
# and all the fields in CompCorInputSpec
percentile_threshold = traits.Range(
low=0.0,
high=1.0,
value=0.02,
exclude_low=True,
exclude_high=True,
usedefault=True,
desc="the percentile "
"used to select highest-variance "
"voxels, represented by a number "
"between 0 and 1, exclusive. By "
"default, this value is set to .02. "
"That is, the 2% of voxels "
"with the highest variance are used.",
)
class TCompCorOutputSpec(CompCorOutputSpec):
# and all the fields in CompCorOutputSpec
high_variance_masks = OutputMultiPath(
File(exists=True), desc=(("voxels exceeding the variance" " threshold"))
)
class TCompCor(CompCor):
"""
Interface for tCompCor. Computes a ROI mask based on variance of voxels.
Example
-------
>>> ccinterface = TCompCor()
>>> ccinterface.inputs.realigned_file = 'functional.nii'
>>> ccinterface.inputs.mask_files = 'mask.nii'
>>> ccinterface.inputs.num_components = 1
>>> ccinterface.inputs.pre_filter = 'polynomial'
>>> ccinterface.inputs.regress_poly_degree = 2
>>> ccinterface.inputs.percentile_threshold = .03
"""
input_spec = TCompCorInputSpec
output_spec = TCompCorOutputSpec
def __init__(self, *args, **kwargs):
"""exactly the same as compcor except the header"""
super(TCompCor, self).__init__(*args, **kwargs)
self._header = "tCompCor"
self._mask_files = []
def _process_masks(self, mask_images, timeseries=None):
out_images = []
self._mask_files = []
timeseries = np.asanyarray(timeseries)
for i, img in enumerate(mask_images):
mask = np.asanyarray(img.dataobj).astype(bool)
imgseries = timeseries[mask, :]
imgseries = regress_poly(2, imgseries)[0]
tSTD = _compute_tSTD(imgseries, 0, axis=-1)
threshold_std = np.percentile(
tSTD,
np.round(100.0 * (1.0 - self.inputs.percentile_threshold)).astype(int),
)
mask_data = np.zeros_like(mask)
mask_data[mask != 0] = tSTD >= threshold_std
out_image = nb.Nifti1Image(mask_data, affine=img.affine, header=img.header)
# save mask
mask_file = os.path.abspath("mask_{:03d}.nii.gz".format(i))
out_image.to_filename(mask_file)
IFLOGGER.debug(
"tCompcor computed and saved mask of shape %s to " "mask_file %s",
str(mask.shape),
mask_file,
)
self._mask_files.append(mask_file)
out_images.append(out_image)
return out_images
def _list_outputs(self):
outputs = super(TCompCor, self)._list_outputs()
outputs["high_variance_masks"] = self._mask_files
return outputs
class TSNRInputSpec(BaseInterfaceInputSpec):
in_file = InputMultiPath(
File(exists=True),
mandatory=True,
desc="realigned 4D file or a list of 3D files",
)
regress_poly = traits.Range(low=1, desc="Remove polynomials")
tsnr_file = File(
"tsnr.nii.gz", usedefault=True, hash_files=False, desc="output tSNR file"
)
mean_file = File(
"mean.nii.gz", usedefault=True, hash_files=False, desc="output mean file"
)
stddev_file = File(
"stdev.nii.gz", usedefault=True, hash_files=False, desc="output tSNR file"
)
detrended_file = File(
"detrend.nii.gz",
usedefault=True,
hash_files=False,
desc="input file after detrending",
)
class TSNROutputSpec(TraitedSpec):
tsnr_file = File(exists=True, desc="tsnr image file")
mean_file = File(exists=True, desc="mean image file")
stddev_file = File(exists=True, desc="std dev image file")
detrended_file = File(desc="detrended input file")
class TSNR(BaseInterface):
"""
Computes the time-course SNR for a time series
Typically you want to run this on a realigned time-series.
Example
-------
>>> tsnr = TSNR()
>>> tsnr.inputs.in_file = 'functional.nii'
>>> res = tsnr.run() # doctest: +SKIP
"""
input_spec = TSNRInputSpec
output_spec = TSNROutputSpec
def _run_interface(self, runtime):
img = nb.load(self.inputs.in_file[0])
header = img.header.copy()
vollist = [nb.load(filename) for filename in self.inputs.in_file]
data = np.concatenate(
[
vol.get_fdata(dtype=np.float32).reshape(vol.shape[:3] + (-1,))
for vol in vollist
],
axis=3,
)
data = np.nan_to_num(data)
if data.dtype.kind == "i":
header.set_data_dtype(np.float32)
data = data.astype(np.float32)
if isdefined(self.inputs.regress_poly):
data = regress_poly(self.inputs.regress_poly, data, remove_mean=False)[0]
img = nb.Nifti1Image(data, img.affine, header)
nb.save(img, op.abspath(self.inputs.detrended_file))
meanimg = np.mean(data, axis=3)
stddevimg = np.std(data, axis=3)
tsnr = np.zeros_like(meanimg)
stddevimg_nonzero = stddevimg > 1.0e-3
tsnr[stddevimg_nonzero] = (
meanimg[stddevimg_nonzero] / stddevimg[stddevimg_nonzero]
)
img = nb.Nifti1Image(tsnr, img.affine, header)
nb.save(img, op.abspath(self.inputs.tsnr_file))
img = nb.Nifti1Image(meanimg, img.affine, header)
nb.save(img, op.abspath(self.inputs.mean_file))
img = nb.Nifti1Image(stddevimg, img.affine, header)
nb.save(img, op.abspath(self.inputs.stddev_file))
return runtime
def _list_outputs(self):
outputs = self._outputs().get()
for k in ["tsnr_file", "mean_file", "stddev_file"]:
outputs[k] = op.abspath(getattr(self.inputs, k))
if isdefined(self.inputs.regress_poly):
outputs["detrended_file"] = op.abspath(self.inputs.detrended_file)
return outputs
class NonSteadyStateDetectorInputSpec(BaseInterfaceInputSpec):
in_file = File(exists=True, mandatory=True, desc="4D NIFTI EPI file")
class NonSteadyStateDetectorOutputSpec(TraitedSpec):
n_volumes_to_discard = traits.Int(
desc="Number of non-steady state volumes"
"detected in the beginning of the scan."
)
class NonSteadyStateDetector(BaseInterface):
"""
Returns the number of non-steady state volumes detected at the beginning
of the scan.
"""
input_spec = NonSteadyStateDetectorInputSpec
output_spec = NonSteadyStateDetectorOutputSpec
def _run_interface(self, runtime):
in_nii = nb.load(self.inputs.in_file)
global_signal = (
in_nii.dataobj[:, :, :, :50].mean(axis=0).mean(axis=0).mean(axis=0)
)
self._results = {"n_volumes_to_discard": is_outlier(global_signal)}
return runtime
def _list_outputs(self):
return self._results