xcp_d.interfaces.censoring.RemoveDummyVolumes

class xcp_d.interfaces.censoring.RemoveDummyVolumes(from_file=None, resource_monitor=None, **inputs)[source]

Removes initial volumes from a nifti or cifti file.

A bold file and its corresponding confounds TSV (fmriprep format) are adjusted to remove the first n seconds of data.

Mandatory Inputs:

  • bold_file (a pathlike object or string representing an existing file) – Either cifti or nifti .

  • confounds_file (a pathlike object or string representing an existing file) – TSV file with selected confounds for denoising.

  • dummy_scans (an integer or ‘auto’) – Number of volumes to drop from the beginning, calculated in an earlier workflow from dummy_scans.

  • fmriprep_confounds_file (a pathlike object or string representing an existing file) – FMRIPrep confounds tsv. Used for motion-based censoring.

  • motion_file (a pathlike object or string representing an existing file) – Confounds file containing only filtered motion parameters.

  • temporal_mask (a pathlike object or string representing an existing file) – Temporal mask file.

Outputs:

  • bold_file_dropped_TR (a pathlike object or string representing an existing file) – Bold or cifti with volumes dropped.

  • confounds_file_dropped_TR (a pathlike object or string representing an existing file) – TSV file with selected confounds for denoising, after removing TRs.

  • dummy_scans (an integer) – Number of volumes dropped.

  • fmriprep_confounds_file_dropped_TR (a pathlike object or string representing an existing file) – FMRIPrep confounds tsv after removing TRs. Used for motion-based censoring.

  • motion_file_dropped_TR (a pathlike object or string representing an existing file) – Confounds file containing only filtered motion parameters.

  • temporal_mask_dropped_TR (a pathlike object or string representing an existing file) – Temporal mask file.

__init__(from_file=None, resource_monitor=None, **inputs)[source]

Subclasses must implement __init__

Methods

__init__([from_file, resource_monitor])

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.

resource_monitor

version

interfaces should implement a version property