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.

For more information about the exact steps, please see denoise_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 or None) – 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:

denoised_interpolated_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.

class xcp_d.interfaces.nilearn.DenoiseNifti(check_import=True, *args, **kwargs)[source]

Bases: NilearnBaseInterface, SimpleInterface

Denoise a NIfTI BOLD file with Nilearn.

For more information about the exact steps, please see denoise_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 or None) – 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:

denoised_interpolated_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.

class xcp_d.interfaces.nilearn.IndexImage(check_import=True, *args, **kwargs)[source]

Bases: NilearnBaseInterface, SimpleInterface

Select a specific volume from a 4D image.

Mandatory Inputs:

in_file (a pathlike object or string representing an existing file) – A 4D image to index.

Optional Inputs:

  • index (an integer) – Volume index to select from in_file. (Nipype default value: 0)

  • out_file (a pathlike object or string representing a file) – The name of the indexed file. (Nipype default value: img_3d.nii.gz)

Outputs:

out_file (a pathlike object or string representing an existing file) – Concatenated output file.

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.