Nuisance Signal Removal

CPAC.nuisance.bandpass_voxels(realigned_file, regressor_file, bandpass_freqs, sample_period=None)

Performs ideal bandpass filtering on each voxel time-series.

Parameters:

realigned_filestring

Path of a realigned nifti file.

bandpass_freqstuple

Tuple containing the bandpass frequencies. (LowCutoff_HighPass HighCutoff_LowPass)

sample_periodfloat, optional

Length of sampling period in seconds. If not specified, this value is read from the nifti file provided.

Returns:

bandpassed_filestring

Path of filtered output (nifti file).

CPAC.nuisance.cosine_filter(input_image_path, timestep, period_cut=128, remove_mean=True, axis=- 1, failure_mode='error')

input_image_path: string

Bold image to be filtered.

timestep: float

‘Repetition time (TR) of series (in sec) - derived from image header if unspecified’

period_cut: float

Minimum period (in sec) for DCT high-pass filter, nipype default value: 128

CPAC.nuisance.create_regressor_workflow(nuisance_selectors, use_ants, ventricle_mask_exist, csf_mask_exist, all_bold=False, name='nuisance_regressors')

Workflow for the removal of various signals considered to be noise from resting state fMRI data. The residual signals for linear regression denoising is performed in a single model. Therefore the residual time-series will be orthogonal to all signals.

Parameters:

:param nuisance_selectors: dictionary describing nuisance regression to be performed

:param use_ants: flag indicating whether FNIRT or ANTS is used

:param name: Name of the workflow, defaults to ‘nuisance’

:return: nuisancenipype.pipeline.engine.Workflow

Nuisance workflow.

CPAC.nuisance.find_offending_time_points(fd_j_file_path=None, fd_p_file_path=None, dvars_file_path=None, fd_j_threshold=None, fd_p_threshold=None, dvars_threshold=None, number_of_previous_trs_to_censor=0, number_of_subsequent_trs_to_censor=0)

Applies criterion in method to find time points whose FD or DVARS (or both) are above threshold.

Parameters:

• fd_j_file_path – path to TSV containing framewise displacement as a single column. If not specified, it will not be used.

• fd_p_file_path – path to TSV containing framewise displacement as a single column. If not specified, it will not be used.

• dvars_file_path – path to TSV containing DVARS as a single column. If not specified, it will not be used.

• fd_j_threshold – threshold to apply to framewise displacement (Jenkinson), it can be a value such as 0.2 or a floating point multiple of the standard deviation specified as, e.g. ‘1.5SD’.

• fd_p_threshold – threshold to apply to framewise displacement (Power), it can be a value such as 0.2 or a floating point multiple of the standard deviation specified as, e.g. ‘1.5SD’.

• dvars_threshold – threshold to apply to DVARS, can be a value such as 0.5 or a floating point multiple of the standard deviation specified as, e.g. ‘1.5SD’.

• number_of_previous_trs_to_censor – extent of censorship window before the censor.

• number_of_subsequent_trs_to_censor – extent of censorship window after the censor.

Returns:

File path to TSV file containing the volumes to be censored.

CPAC.nuisance.generate_summarize_tissue_mask(nuisance_wf, pipeline_resource_pool, regressor_descriptor, regressor_selector, csf_mask_exist, use_ants=True, ventricle_mask_exist=True, all_bold=False)

Add tissue mask generation into pipeline according to the selector.

Parameters:

• nuisance_wf – Nuisance regressor workflow.

• pipeline_resource_pool – dictionary of available resources.

• regressor_descriptor – dictionary of steps to build, including keys: ‘tissue’, ‘resolution’, ‘erosion’

• regressor_selector – dictionary with the original selector

Returns:

the full path of the 3D nifti file containing the mask created by this operation.