xcp_d.interfaces.nilearn.DenoiseNifti

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

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.

__init__(check_import=True, *args, **kwargs)[source]

Subclasses must implement __init__

Methods

__init__([check_import])

Subclasses must implement __init__

aggregate_outputs([runtime, needed_outputs])

Collate expected outputs and apply output traits validation.

help([returnhelp])

Prints class help

load_inputs_from_json(json_file[, overwrite])

A convenient way to load pre-set inputs from a JSON file.

run([cwd, ignore_exception])

Execute this interface.

save_inputs_to_json(json_file)

A convenient way to save current inputs to a JSON file.

Attributes

always_run

Should the interface be always run even if the inputs were not changed? Only applies to interfaces being run within a workflow context.

can_resume

Defines if the interface can reuse partial results after interruption.

imports

resource_monitor

version

interfaces should implement a version property