algorithms.confounds¶
ACompCor¶
Anatomical compcor: for inputs and outputs, see CompCor. When the mask provided is an anatomical mask, then CompCor is equivalent to ACompCor.
Inputs:
[Mandatory]
realigned_file: (an existing file name)
already realigned brain image (4D)
[Optional]
mask_files: (a list of items which are an existing file name)
One or more mask files that determines ROI (3D). When more that one
file is provided `merge_method` or `merge_index` must be provided
merge_method: ('union' or 'intersect' or 'none')
Merge method if multiple masks are present - `union` uses voxels
included in at least one input mask, `intersect` uses only voxels
present in all input masks, `none` performs CompCor on each mask
individually
mutually_exclusive: mask_index
requires: mask_files
mask_index: (a long integer >= 0)
Position of mask in `mask_files` to use - first is the default.
mutually_exclusive: merge_method
requires: mask_files
components_file: (a unicode string, nipype default value:
components_file.txt)
Filename to store physiological components
num_components: (an integer (int or long), nipype default value: 6)
pre_filter: ('polynomial' or 'cosine' or False, nipype default value:
polynomial)
Detrend time series prior to component extraction
use_regress_poly: (a boolean)
use polynomial regression pre-component extraction
regress_poly_degree: (a long integer >= 1, nipype default value: 1)
the degree polynomial to use
header_prefix: (a unicode string)
the desired header for the output tsv file (one column). If
undefined, will default to "CompCor"
high_pass_cutoff: (a float, nipype default value: 128)
Cutoff (in seconds) for "cosine" pre-filter
repetition_time: (a float)
Repetition time (TR) of series - derived from image header if
unspecified
save_pre_filter: (a boolean or a file name)
Save pre-filter basis as text file
ignore_initial_volumes: (a long integer >= 0, nipype default value:
0)
Number of volumes at start of series to ignore
failure_mode: ('error' or 'NaN', nipype default value: error)
When no components are found or convergence fails, raise an error or
silently return columns of NaNs.
Outputs:
components_file: (an existing file name)
text file containing the noise components
pre_filter_file: (a file name)
text file containing high-pass filter basis
References:¶
None
CompCor¶
Interface with core CompCor computation, used in aCompCor and tCompCor
CompCor provides three pre-filter options, all of which include per-voxel mean removal:
- polynomial: Legendre polynomial basis
- cosine: Discrete cosine basis
- False: mean-removal only
In the case of polynomial and cosine filters, a pre-filter file may
be saved with a row for each volume/timepoint, and a column for each
non-constant regressor.
If no non-constant (mean-removal) columns are used, this file may be empty.
If ignore_initial_volumes is set, then the specified number of initial
volumes are excluded both from pre-filtering and CompCor component
extraction.
Each column in the components and pre-filter files are prefixe with zeros
for each excluded volume so that the number of rows continues to match the
number of volumes in the input file.
In addition, for each excluded volume, a column is added to the pre-filter
file with a 1 in the corresponding row.
Example¶
>>> ccinterface = CompCor()
>>> ccinterface.inputs.realigned_file = 'functional.nii'
>>> ccinterface.inputs.mask_files = 'mask.nii'
>>> ccinterface.inputs.num_components = 1
>>> ccinterface.inputs.pre_filter = 'polynomial'
>>> ccinterface.inputs.regress_poly_degree = 2
Inputs:
[Mandatory]
realigned_file: (an existing file name)
already realigned brain image (4D)
[Optional]
mask_files: (a list of items which are an existing file name)
One or more mask files that determines ROI (3D). When more that one
file is provided `merge_method` or `merge_index` must be provided
merge_method: ('union' or 'intersect' or 'none')
Merge method if multiple masks are present - `union` uses voxels
included in at least one input mask, `intersect` uses only voxels
present in all input masks, `none` performs CompCor on each mask
individually
mutually_exclusive: mask_index
requires: mask_files
mask_index: (a long integer >= 0)
Position of mask in `mask_files` to use - first is the default.
mutually_exclusive: merge_method
requires: mask_files
components_file: (a unicode string, nipype default value:
components_file.txt)
Filename to store physiological components
num_components: (an integer (int or long), nipype default value: 6)
pre_filter: ('polynomial' or 'cosine' or False, nipype default value:
polynomial)
Detrend time series prior to component extraction
use_regress_poly: (a boolean)
use polynomial regression pre-component extraction
regress_poly_degree: (a long integer >= 1, nipype default value: 1)
the degree polynomial to use
header_prefix: (a unicode string)
the desired header for the output tsv file (one column). If
undefined, will default to "CompCor"
high_pass_cutoff: (a float, nipype default value: 128)
Cutoff (in seconds) for "cosine" pre-filter
repetition_time: (a float)
Repetition time (TR) of series - derived from image header if
unspecified
save_pre_filter: (a boolean or a file name)
Save pre-filter basis as text file
ignore_initial_volumes: (a long integer >= 0, nipype default value:
0)
Number of volumes at start of series to ignore
failure_mode: ('error' or 'NaN', nipype default value: error)
When no components are found or convergence fails, raise an error or
silently return columns of NaNs.
Outputs:
components_file: (an existing file name)
text file containing the noise components
pre_filter_file: (a file name)
text file containing high-pass filter basis
References:¶
None
ComputeDVARS¶
Computes the DVARS.
Inputs:
[Mandatory]
in_file: (an existing file name)
functional data, after HMC
in_mask: (an existing file name)
a brain mask
[Optional]
remove_zerovariance: (a boolean, nipype default value: True)
remove voxels with zero variance
save_std: (a boolean, nipype default value: True)
save standardized DVARS
save_nstd: (a boolean, nipype default value: False)
save non-standardized DVARS
save_vxstd: (a boolean, nipype default value: False)
save voxel-wise standardized DVARS
save_all: (a boolean, nipype default value: False)
output all DVARS
series_tr: (a float)
repetition time in sec.
save_plot: (a boolean, nipype default value: False)
write DVARS plot
figdpi: (an integer (int or long), nipype default value: 100)
output dpi for the plot
figsize: (a tuple of the form: (a float, a float), nipype default
value: (11.7, 2.3))
output figure size
figformat: ('png' or 'pdf' or 'svg', nipype default value: png)
output format for figures
intensity_normalization: (a float, nipype default value: 1000.0)
Divide value in each voxel at each timepoint by the median
calculated across all voxelsand timepoints within the mask (if
specified)and then multiply by the value specified bythis parameter.
By using the default (1000)output DVARS will be expressed in x10 %
BOLD units compatible with Power et al.2012. Set this to 0 to
disable intensitynormalization altogether.
Outputs:
out_std: (an existing file name)
output text file
out_nstd: (an existing file name)
output text file
out_vxstd: (an existing file name)
output text file
out_all: (an existing file name)
output text file
avg_std: (a float)
avg_nstd: (a float)
avg_vxstd: (a float)
fig_std: (an existing file name)
output DVARS plot
fig_nstd: (an existing file name)
output DVARS plot
fig_vxstd: (an existing file name)
output DVARS plot
References:¶
None None
FramewiseDisplacement¶
Calculate the FD as in [Power2012]. This implementation reproduces the calculation in fsl_motion_outliers
| [Power2012] | (1, 2) Power et al., Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion, NeuroImage 59(3), 2012. doi:10.1016/j.neuroimage.2011.10.018. |
Inputs:
[Mandatory]
in_file: (an existing file name)
motion parameters
parameter_source: ('FSL' or 'AFNI' or 'SPM' or 'FSFAST' or 'NIPY')
Source of movement parameters
[Optional]
radius: (a float, nipype default value: 50)
radius in mm to calculate angular FDs, 50mm is the default since it
is used in Power et al. 2012
out_file: (a file name, nipype default value: fd_power_2012.txt)
output file name
out_figure: (a file name, nipype default value: fd_power_2012.pdf)
output figure name
series_tr: (a float)
repetition time in sec.
save_plot: (a boolean, nipype default value: False)
write FD plot
normalize: (a boolean, nipype default value: False)
calculate FD in mm/s
figdpi: (an integer (int or long), nipype default value: 100)
output dpi for the FD plot
figsize: (a tuple of the form: (a float, a float), nipype default
value: (11.7, 2.3))
output figure size
Outputs:
out_file: (a file name)
calculated FD per timestep
out_figure: (a file name)
output image file
fd_average: (a float)
average FD
References:¶
None
NonSteadyStateDetector¶
Returns the number of non-steady state volumes detected at the beginning of the scan.
Inputs:
[Mandatory]
in_file: (an existing file name)
4D NIFTI EPI file
Outputs:
n_volumes_to_discard: (an integer (int or long))
Number of non-steady state volumesdetected in the beginning of the
scan.
TCompCor¶
Interface for tCompCor. Computes a ROI mask based on variance of voxels.
Example¶
>>> ccinterface = TCompCor()
>>> ccinterface.inputs.realigned_file = 'functional.nii'
>>> ccinterface.inputs.mask_files = 'mask.nii'
>>> ccinterface.inputs.num_components = 1
>>> ccinterface.inputs.pre_filter = 'polynomial'
>>> ccinterface.inputs.regress_poly_degree = 2
>>> ccinterface.inputs.percentile_threshold = .03
Inputs:
[Mandatory]
realigned_file: (an existing file name)
already realigned brain image (4D)
[Optional]
percentile_threshold: (0.0 < a floating point number < 1.0, nipype
default value: 0.02)
the percentile used to select highest-variance voxels, represented
by a number between 0 and 1, exclusive. By default, this value is
set to .02. That is, the 2% of voxels with the highest variance are
used.
mask_files: (a list of items which are an existing file name)
One or more mask files that determines ROI (3D). When more that one
file is provided `merge_method` or `merge_index` must be provided
merge_method: ('union' or 'intersect' or 'none')
Merge method if multiple masks are present - `union` uses voxels
included in at least one input mask, `intersect` uses only voxels
present in all input masks, `none` performs CompCor on each mask
individually
mutually_exclusive: mask_index
requires: mask_files
mask_index: (a long integer >= 0)
Position of mask in `mask_files` to use - first is the default.
mutually_exclusive: merge_method
requires: mask_files
components_file: (a unicode string, nipype default value:
components_file.txt)
Filename to store physiological components
num_components: (an integer (int or long), nipype default value: 6)
pre_filter: ('polynomial' or 'cosine' or False, nipype default value:
polynomial)
Detrend time series prior to component extraction
use_regress_poly: (a boolean)
use polynomial regression pre-component extraction
regress_poly_degree: (a long integer >= 1, nipype default value: 1)
the degree polynomial to use
header_prefix: (a unicode string)
the desired header for the output tsv file (one column). If
undefined, will default to "CompCor"
high_pass_cutoff: (a float, nipype default value: 128)
Cutoff (in seconds) for "cosine" pre-filter
repetition_time: (a float)
Repetition time (TR) of series - derived from image header if
unspecified
save_pre_filter: (a boolean or a file name)
Save pre-filter basis as text file
ignore_initial_volumes: (a long integer >= 0, nipype default value:
0)
Number of volumes at start of series to ignore
failure_mode: ('error' or 'NaN', nipype default value: error)
When no components are found or convergence fails, raise an error or
silently return columns of NaNs.
Outputs:
high_variance_masks: (a list of items which are an existing file
name)
voxels exceeding the variance threshold
components_file: (an existing file name)
text file containing the noise components
pre_filter_file: (a file name)
text file containing high-pass filter basis
References:¶
None
TSNR¶
Computes the time-course SNR for a time series
Typically you want to run this on a realigned time-series.
Example¶
>>> tsnr = TSNR()
>>> tsnr.inputs.in_file = 'functional.nii'
>>> res = tsnr.run()
Inputs:
[Mandatory]
in_file: (a list of items which are an existing file name)
realigned 4D file or a list of 3D files
[Optional]
regress_poly: (a long integer >= 1)
Remove polynomials
tsnr_file: (a file name, nipype default value: tsnr.nii.gz)
output tSNR file
mean_file: (a file name, nipype default value: mean.nii.gz)
output mean file
stddev_file: (a file name, nipype default value: stdev.nii.gz)
output tSNR file
detrended_file: (a file name, nipype default value: detrend.nii.gz)
input file after detrending
Outputs:
tsnr_file: (an existing file name)
tsnr image file
mean_file: (an existing file name)
mean image file
stddev_file: (an existing file name)
std dev image file
detrended_file: (a file name)
detrended input file
combine_mask_files()¶
Combines input mask files into a single nibabel image
A helper function for CompCor
- mask_files: a list
- one or more binary mask files
- mask_method: enum (‘union’, ‘intersect’, ‘none’)
- determines how to combine masks
- mask_index: an integer
- determines which file to return (mutually exclusive with mask_method)
returns: a list of nibabel images
compute_dvars()¶
Compute the DVARS [Power2012].
Particularly, the standardized DVARS [Nichols2013] are computed.
| [Nichols2013] | Nichols T, Notes on creating a standardized version of DVARS, 2013. |
Note
Implementation details
Uses the implementation of the Yule-Walker equations from nitime for the AR filtering of the fMRI signal.
| param numpy.ndarray func: | |
|---|---|
| functional data, after head-motion-correction. | |
| param numpy.ndarray mask: | |
| a 3D mask of the brain | |
| param bool output_all: | |
| write out all dvars | |
| param str out_file: | |
| a path to which the standardized dvars should be saved. | |
| return: | the standardized DVARS |
compute_noise_components()¶
Compute the noise components from the imgseries for each mask
imgseries: a nibabel img mask_images: a list of nibabel images num_components: number of noise components to return filter_type: type off filter to apply to time series before computing
noise components.‘polynomial’ - Legendre polynomial basis ‘cosine’ - Discrete cosine (DCT) basis False - None (mean-removal only)
Filter options:
degree: order of polynomial used to remove trends from the timeseries period_cut: minimum period (in sec) for DCT high-pass filter repetition_time: time (in sec) between volume acquisitions
returns:
components: a numpy array basis: a numpy array containing the (non-constant) filter regressors
cosine_filter()¶
fallback_svd()¶
is_outlier()¶
Returns a boolean array with True if points are outliers and False otherwise.
| param nparray points: | |
|---|---|
| an numobservations by numdimensions numpy array of observations | |
| param float thresh: | |
| the modified z-score to use as a threshold. Observations with a modified z-score (based on the median absolute deviation) greater than this value will be classified as outliers. | |
| return: | A bolean mask, of size numobservations-length array. |
Note
References
Boris Iglewicz and David Hoaglin (1993), “Volume 16: How to Detect and Handle Outliers”, The ASQC Basic References in Quality Control: Statistical Techniques, Edward F. Mykytka, Ph.D., Editor.
regress_poly()¶
Returns data with degree polynomial regressed out.
| param bool remove_mean: | |
|---|---|
| whether or not demean data (i.e. degree 0), | |
| param int axis: | numpy array axes along which regression is performed |