xcp_d.interfaces.nilearn module
Interfaces for Nilearn code.
- class xcp_d.interfaces.nilearn.BinaryMath(check_import=True, *args, **kwargs)[source]
Bases:
NilearnBaseInterface
,SimpleInterface
Do math on an image.
- Mandatory Inputs:
expression (a string) – A mathematical expression to apply to the image. Must have ‘img’ in it.
in_file (a pathlike object or string representing an existing file) – An image to do math on.
- Optional Inputs:
out_file (a pathlike object or string representing a file) – The name of the mathified file to write out. out_img.nii.gz by default. (Nipype default value:
out_img.nii.gz
)- Outputs:
out_file (a pathlike object or string representing an existing file) – Mathified output file.
- class xcp_d.interfaces.nilearn.DenoiseCifti(check_import=True, *args, **kwargs)[source]
Bases:
NilearnBaseInterface
,SimpleInterface
Denoise a CIFTI BOLD file with Nilearn.
- Mandatory Inputs:
TR (a float) – Repetition time.
bandpass_filter (a boolean) – To apply bandpass or not.
confounds_file (a pathlike object or string representing an existing file) – A tab-delimited file containing the confounds to remove from the BOLD data.
filter_order (an integer) – Filter order.
high_pass (a float) – Highpass filter in Hz.
low_pass (a float) – Lowpass filter in Hz.
preprocessed_bold (a pathlike object or string representing an existing file) – Preprocessed BOLD data, after dummy volume removal, but without any additional censoring.
temporal_mask (a pathlike object or string representing an existing file) – The tab-delimited high-motion outliers file.
- Outputs:
interpolated_filtered_bold (a pathlike object or string representing an existing file) – The result of denoising the censored preprocessed BOLD data, followed by cubic spline interpolation and band-pass filtering.
uncensored_denoised_bold (a pathlike object or string representing an existing file) – The result of denoising the full (uncensored) preprocessed BOLD data using betas estimated using the censored BOLD data and nuisance regressors.
- class xcp_d.interfaces.nilearn.DenoiseNifti(check_import=True, *args, **kwargs)[source]
Bases:
NilearnBaseInterface
,SimpleInterface
Denoise a NIfTI BOLD file with Nilearn.
- Mandatory Inputs:
TR (a float) – Repetition time.
bandpass_filter (a boolean) – To apply bandpass or not.
confounds_file (a pathlike object or string representing an existing file) – A tab-delimited file containing the confounds to remove from the BOLD data.
filter_order (an integer) – Filter order.
high_pass (a float) – Highpass filter in Hz.
low_pass (a float) – Lowpass filter in Hz.
mask (a pathlike object or string representing an existing file) – A binary brain mask.
preprocessed_bold (a pathlike object or string representing an existing file) – Preprocessed BOLD data, after dummy volume removal, but without any additional censoring.
temporal_mask (a pathlike object or string representing an existing file) – The tab-delimited high-motion outliers file.
- Outputs:
interpolated_filtered_bold (a pathlike object or string representing an existing file) – The result of denoising the censored preprocessed BOLD data, followed by cubic spline interpolation and band-pass filtering.
uncensored_denoised_bold (a pathlike object or string representing an existing file) – The result of denoising the full (uncensored) preprocessed BOLD data using betas estimated using the censored BOLD data and nuisance regressors.
- class xcp_d.interfaces.nilearn.Merge(check_import=True, *args, **kwargs)[source]
Bases:
NilearnBaseInterface
,SimpleInterface
Merge images.
- Mandatory Inputs:
in_files (a list of items which are a pathlike object or string representing an existing file) – A list of images to concatenate.
- Optional Inputs:
out_file (a pathlike object or string representing a file) – The name of the concatenated file to write out. concat_4d.nii.gz by default. (Nipype default value:
concat_4d.nii.gz
)- Outputs:
out_file (a pathlike object or string representing an existing file) – Concatenated output file.
- class xcp_d.interfaces.nilearn.ResampleToImage(check_import=True, *args, **kwargs)[source]
Bases:
NilearnBaseInterface
,SimpleInterface
Resample a source image on a target image.
No registration is performed: the image should already be aligned.
- Mandatory Inputs:
in_file (a pathlike object or string representing an existing file) – An image to average over time.
target_file (a pathlike object or string representing an existing file)
- Optional Inputs:
out_file (a pathlike object or string representing a file) – The name of the resampled file to write out. out_img.nii.gz by default. (Nipype default value:
out_img.nii.gz
)- Outputs:
out_file (a pathlike object or string representing an existing file) – Resampled output file.
- class xcp_d.interfaces.nilearn.Smooth(check_import=True, *args, **kwargs)[source]
Bases:
NilearnBaseInterface
,SimpleInterface
Smooth image.
- Mandatory Inputs:
in_file (a pathlike object or string representing an existing file) – An image to smooth.
- Optional Inputs:
fwhm (a float or a list of from 3 to 3 items which are a float) – Full width at half maximum. Smoothing strength, as a full-width at half maximum, in millimeters.
out_file (a pathlike object or string representing a file) – The name of the smoothed file to write out. smooth_img.nii.gz by default. (Nipype default value:
smooth_img.nii.gz
)
- Outputs:
out_file (a pathlike object or string representing an existing file) – Smoothed output file.