Skip to content

FIX: Add tolerance parameter to ComputeDVARS #3489

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 4 commits into from
Jul 12, 2022
Merged

FIX: Add tolerance parameter to ComputeDVARS #3489

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
4 commits
Select commit Hold shift + click to select a range
64e176b
FIX: Add variance tolerance parameter to ComputeDVARS
effigies Jul 12, 2022
75a78e0
RF: Modernize compute_dvars
effigies Jul 12, 2022
53afcd4
TEST: make specs
effigies Jul 12, 2022
693bff7
Update nipype/algorithms/confounds.py
effigies Jul 12, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
40 changes: 28 additions & 12 deletions nipype/algorithms/confounds.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -50,6 +50,11 @@ class ComputeDVARSInputSpec(BaseInterfaceInputSpec):
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(
Expand Down Expand Up @@ -167,6 +172,7 @@ def _run_interface(self, runtime):
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,
)

Expand Down Expand Up @@ -994,8 +1000,19 @@ def _list_outputs(self):
return self._results


def _AR_est_YW(x, order, rxx=None):
"""Retrieve AR coefficients while dropping the sig_sq return value"""
from nitime.algorithms import AR_est_YW

return AR_est_YW(x, order, rxx=rxx)[0]


def compute_dvars(
in_file, in_mask, remove_zerovariance=False, intensity_normalization=1000
in_file,
in_mask,
remove_zerovariance=False,
intensity_normalization=1000,
variance_tol=0.0,
):
"""
Compute the :abbr:`DVARS (D referring to temporal
Expand Down Expand Up @@ -1027,36 +1044,35 @@ def compute_dvars(
"""
import numpy as np
import nibabel as nb
from nitime.algorithms import AR_est_YW
import warnings

func = nb.load(in_file).get_fdata(dtype=np.float32)
mask = np.asanyarray(nb.load(in_mask).dataobj).astype(np.uint8)
func = np.float32(nb.load(in_file).dataobj)
mask = np.bool_(nb.load(in_mask).dataobj)

if len(func.shape) != 4:
raise RuntimeError("Input fMRI dataset should be 4-dimensional")

idx = np.where(mask > 0)
mfunc = func[idx[0], idx[1], idx[2], :]
mfunc = func[mask]

if intensity_normalization != 0:
mfunc = (mfunc / np.median(mfunc)) * intensity_normalization

# Robust standard deviation (we are using "lower" interpolation
# because this is what FSL is doing
func_sd = (
np.percentile(mfunc, 75, axis=1, interpolation="lower")
- np.percentile(mfunc, 25, axis=1, interpolation="lower")
np.percentile(mfunc, 75, axis=1, method="lower")
- np.percentile(mfunc, 25, axis=1, method="lower")
) / 1.349

if remove_zerovariance:
mfunc = mfunc[func_sd != 0, :]
func_sd = func_sd[func_sd != 0]
zero_variance_voxels = func_sd > variance_tol
mfunc = mfunc[zero_variance_voxels, :]
func_sd = func_sd[zero_variance_voxels]

# Compute (non-robust) estimate of lag-1 autocorrelation
ar1 = np.apply_along_axis(
AR_est_YW, 1, regress_poly(0, mfunc, remove_mean=True)[0].astype(np.float32), 1
)[:, 0]
_AR_est_YW, 1, regress_poly(0, mfunc, remove_mean=True)[0].astype(np.float32), 1
)

# Compute (predicted) standard deviation of temporal difference time series
diff_sdhat = np.squeeze(np.sqrt(((1 - ar1) * 2).tolist())) * func_sd
Expand Down
3 changes: 3 additions & 0 deletions nipype/algorithms/tests/test_auto_ComputeDVARS.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -43,6 +43,9 @@ def test_ComputeDVARS_inputs():
usedefault=True,
),
series_tr=dict(),
variance_tol=dict(
usedefault=True,
),
)
inputs = ComputeDVARS.input_spec()

Expand Down