interfaces.freesurfer.model¶
Binarize¶
Wraps the executable command mri_binarize.
Use FreeSurfer mri_binarize to threshold an input volume
Examples¶
>>> binvol = Binarize(in_file='structural.nii', min=10, binary_file='foo_out.nii')
>>> binvol.cmdline
'mri_binarize --o foo_out.nii --i structural.nii --min 10.000000'
Inputs:
[Mandatory]
in_file: (an existing file name)
input volume
argument: ``--i %s``
[Optional]
min: (a float)
min thresh
argument: ``--min %f``
mutually_exclusive: wm_ven_csf
max: (a float)
max thresh
argument: ``--max %f``
mutually_exclusive: wm_ven_csf
rmin: (a float)
compute min based on rmin*globalmean
argument: ``--rmin %f``
rmax: (a float)
compute max based on rmax*globalmean
argument: ``--rmax %f``
match: (a list of items which are an integer (int or long))
match instead of threshold
argument: ``--match %d...``
wm: (a boolean)
set match vals to 2 and 41 (aseg for cerebral WM)
argument: ``--wm``
ventricles: (a boolean)
set match vals those for aseg ventricles+choroid (not 4th)
argument: ``--ventricles``
wm_ven_csf: (a boolean)
WM and ventricular CSF, including choroid (not 4th)
argument: ``--wm+vcsf``
mutually_exclusive: min, max
binary_file: (a file name)
binary output volume
argument: ``--o %s``
out_type: ('nii' or 'nii.gz' or 'mgz')
output file type
count_file: (a boolean or a file name)
save number of hits in ascii file (hits, ntotvox, pct)
argument: ``--count %s``
bin_val: (an integer (int or long))
set vox within thresh to val (default is 1)
argument: ``--binval %d``
bin_val_not: (an integer (int or long))
set vox outside range to val (default is 0)
argument: ``--binvalnot %d``
invert: (a boolean)
set binval=0, binvalnot=1
argument: ``--inv``
frame_no: (an integer (int or long))
use 0-based frame of input (default is 0)
argument: ``--frame %s``
merge_file: (an existing file name)
merge with mergevol
argument: ``--merge %s``
mask_file: (an existing file name)
must be within mask
argument: ``--mask maskvol``
mask_thresh: (a float)
set thresh for mask
argument: ``--mask-thresh %f``
abs: (a boolean)
take abs of invol first (ie, make unsigned)
argument: ``--abs``
bin_col_num: (a boolean)
set binarized voxel value to its column number
argument: ``--bincol``
zero_edges: (a boolean)
zero the edge voxels
argument: ``--zero-edges``
zero_slice_edge: (a boolean)
zero the edge slice voxels
argument: ``--zero-slice-edges``
dilate: (an integer (int or long))
niters: dilate binarization in 3D
argument: ``--dilate %d``
erode: (an integer (int or long))
nerode: erode binarization in 3D (after any dilation)
argument: ``--erode %d``
erode2d: (an integer (int or long))
nerode2d: erode binarization in 2D (after any 3D erosion)
argument: ``--erode2d %d``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
binary_file: (an existing file name)
binarized output volume
count_file: (a file name)
ascii file containing number of hits
Concatenate¶
Wraps the executable command mri_concat.
Use Freesurfer mri_concat to combine several input volumes into one output volume. Can concatenate by frames, or compute a variety of statistics on the input volumes.
Examples¶
Combine two input volumes into one volume with two frames
>>> concat = Concatenate()
>>> concat.inputs.in_files = ['cont1.nii', 'cont2.nii']
>>> concat.inputs.concatenated_file = 'bar.nii'
>>> concat.cmdline
'mri_concat --o bar.nii --i cont1.nii --i cont2.nii'
Inputs:
[Mandatory]
in_files: (a list of items which are an existing file name)
Individual volumes to be concatenated
argument: ``--i %s...``
[Optional]
concatenated_file: (a file name)
Output volume
argument: ``--o %s``
sign: ('abs' or 'pos' or 'neg')
Take only pos or neg voxles from input, or take abs
argument: ``--%s``
stats: ('sum' or 'var' or 'std' or 'max' or 'min' or 'mean')
Compute the sum, var, std, max, min or mean of the input volumes
argument: ``--%s``
paired_stats: ('sum' or 'avg' or 'diff' or 'diff-norm' or 'diff-
norm1' or 'diff-norm2')
Compute paired sum, avg, or diff
argument: ``--paired-%s``
gmean: (an integer (int or long))
create matrix to average Ng groups, Nper=Ntot/Ng
argument: ``--gmean %d``
mean_div_n: (a boolean)
compute mean/nframes (good for var)
argument: ``--mean-div-n``
multiply_by: (a float)
Multiply input volume by some amount
argument: ``--mul %f``
add_val: (a float)
Add some amount to the input volume
argument: ``--add %f``
multiply_matrix_file: (an existing file name)
Multiply input by an ascii matrix in file
argument: ``--mtx %s``
combine: (a boolean)
Combine non-zero values into single frame volume
argument: ``--combine``
keep_dtype: (a boolean)
Keep voxelwise precision type (default is float
argument: ``--keep-datatype``
max_bonfcor: (a boolean)
Compute max and bonferroni correct (assumes -log10(ps))
argument: ``--max-bonfcor``
max_index: (a boolean)
Compute the index of max voxel in concatenated volumes
argument: ``--max-index``
mask_file: (an existing file name)
Mask input with a volume
argument: ``--mask %s``
vote: (a boolean)
Most frequent value at each voxel and fraction of occurances
argument: ``--vote``
sort: (a boolean)
Sort each voxel by ascending frame value
argument: ``--sort``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
concatenated_file: (an existing file name)
Path/name of the output volume
GLMFit¶
Wraps the executable command mri_glmfit.
Use FreeSurfer’s mri_glmfit to specify and estimate a general linear model.
Examples¶
>>> glmfit = GLMFit()
>>> glmfit.inputs.in_file = 'functional.nii'
>>> glmfit.inputs.one_sample = True
>>> glmfit.cmdline == 'mri_glmfit --glmdir %s --y functional.nii --osgm'%os.getcwd()
True
Inputs:
[Mandatory]
in_file: (a file name)
input 4D file
argument: ``--y %s``
[Optional]
glm_dir: (a unicode string)
save outputs to dir
argument: ``--glmdir %s``
fsgd: (a tuple of the form: (an existing file name, 'doss' or
'dods'))
freesurfer descriptor file
argument: ``--fsgd %s %s``
mutually_exclusive: fsgd, design, one_sample
design: (an existing file name)
design matrix file
argument: ``--X %s``
mutually_exclusive: fsgd, design, one_sample
contrast: (a list of items which are an existing file name)
contrast file
argument: ``--C %s...``
one_sample: (a boolean)
construct X and C as a one-sample group mean
argument: ``--osgm``
mutually_exclusive: one_sample, fsgd, design, contrast
no_contrast_ok: (a boolean)
do not fail if no contrasts specified
argument: ``--no-contrasts-ok``
per_voxel_reg: (a list of items which are an existing file name)
per-voxel regressors
argument: ``--pvr %s...``
self_reg: (a tuple of the form: (an integer (int or long), an integer
(int or long), an integer (int or long)))
self-regressor from index col row slice
argument: ``--selfreg %d %d %d``
weighted_ls: (an existing file name)
weighted least squares
argument: ``--wls %s``
mutually_exclusive: weight_file, weight_inv, weight_sqrt
fixed_fx_var: (an existing file name)
for fixed effects analysis
argument: ``--yffxvar %s``
fixed_fx_dof: (an integer (int or long))
dof for fixed effects analysis
argument: ``--ffxdof %d``
mutually_exclusive: fixed_fx_dof_file
fixed_fx_dof_file: (a file name)
text file with dof for fixed effects analysis
argument: ``--ffxdofdat %d``
mutually_exclusive: fixed_fx_dof
weight_file: (an existing file name)
weight for each input at each voxel
mutually_exclusive: weighted_ls
weight_inv: (a boolean)
invert weights
argument: ``--w-inv``
mutually_exclusive: weighted_ls
weight_sqrt: (a boolean)
sqrt of weights
argument: ``--w-sqrt``
mutually_exclusive: weighted_ls
fwhm: (a floating point number >= 0.0)
smooth input by fwhm
argument: ``--fwhm %f``
var_fwhm: (a floating point number >= 0.0)
smooth variance by fwhm
argument: ``--var-fwhm %f``
no_mask_smooth: (a boolean)
do not mask when smoothing
argument: ``--no-mask-smooth``
no_est_fwhm: (a boolean)
turn off FWHM output estimation
argument: ``--no-est-fwhm``
mask_file: (an existing file name)
binary mask
argument: ``--mask %s``
label_file: (an existing file name)
use label as mask, surfaces only
argument: ``--label %s``
mutually_exclusive: cortex
cortex: (a boolean)
use subjects ?h.cortex.label as label
argument: ``--cortex``
mutually_exclusive: label_file
invert_mask: (a boolean)
invert mask
argument: ``--mask-inv``
prune: (a boolean)
remove voxels that do not have a non-zero value at each frame (def)
argument: ``--prune``
no_prune: (a boolean)
do not prune
argument: ``--no-prune``
mutually_exclusive: prunethresh
prune_thresh: (a float)
prune threshold. Default is FLT_MIN
argument: ``--prune_thr %f``
mutually_exclusive: noprune
compute_log_y: (a boolean)
compute natural log of y prior to analysis
argument: ``--logy``
save_estimate: (a boolean)
save signal estimate (yhat)
argument: ``--yhat-save``
save_residual: (a boolean)
save residual error (eres)
argument: ``--eres-save``
save_res_corr_mtx: (a boolean)
save residual error spatial correlation matrix (eres.scm). Big!
argument: ``--eres-scm``
surf: (a boolean)
analysis is on a surface mesh
argument: ``--surf %s %s %s``
requires: subject_id, hemi
subject_id: (a unicode string)
subject id for surface geometry
hemi: ('lh' or 'rh')
surface hemisphere
surf_geo: (a unicode string, nipype default value: white)
surface geometry name (e.g. white, pial)
simulation: (a tuple of the form: ('perm' or 'mc-full' or 'mc-z', an
integer (int or long), a float, a unicode string))
nulltype nsim thresh csdbasename
argument: ``--sim %s %d %f %s``
sim_sign: ('abs' or 'pos' or 'neg')
abs, pos, or neg
argument: ``--sim-sign %s``
uniform: (a tuple of the form: (a float, a float))
use uniform distribution instead of gaussian
argument: ``--uniform %f %f``
pca: (a boolean)
perform pca/svd analysis on residual
argument: ``--pca``
calc_AR1: (a boolean)
compute and save temporal AR1 of residual
argument: ``--tar1``
save_cond: (a boolean)
flag to save design matrix condition at each voxel
argument: ``--save-cond``
vox_dump: (a tuple of the form: (an integer (int or long), an integer
(int or long), an integer (int or long)))
dump voxel GLM and exit
argument: ``--voxdump %d %d %d``
seed: (an integer (int or long))
used for synthesizing noise
argument: ``--seed %d``
synth: (a boolean)
replace input with gaussian
argument: ``--synth``
resynth_test: (an integer (int or long))
test GLM by resynthsis
argument: ``--resynthtest %d``
profile: (an integer (int or long))
niters : test speed
argument: ``--profile %d``
force_perm: (a boolean)
force perumtation test, even when design matrix is not orthog
argument: ``--perm-force``
diag: (an integer (int or long))
Gdiag_no : set diagnositc level
argument: ``--diag %d``
diag_cluster: (a boolean)
save sig volume and exit from first sim loop
argument: ``--diag-cluster``
debug: (a boolean)
turn on debugging
argument: ``--debug``
check_opts: (a boolean)
don't run anything, just check options and exit
argument: ``--checkopts``
allow_repeated_subjects: (a boolean)
allow subject names to repeat in the fsgd file (must appear before
--fsgd
argument: ``--allowsubjrep``
allow_ill_cond: (a boolean)
allow ill-conditioned design matrices
argument: ``--illcond``
sim_done_file: (a file name)
create file when simulation finished
argument: ``--sim-done %s``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
glm_dir: (an existing directory name)
output directory
beta_file: (an existing file name)
map of regression coefficients
error_file: (a file name)
map of residual error
error_var_file: (a file name)
map of residual error variance
error_stddev_file: (a file name)
map of residual error standard deviation
estimate_file: (a file name)
map of the estimated Y values
mask_file: (a file name)
map of the mask used in the analysis
fwhm_file: (a file name)
text file with estimated smoothness
dof_file: (a file name)
text file with effective degrees-of-freedom for the analysis
gamma_file: (a list of items which are any value)
map of contrast of regression coefficients
gamma_var_file: (a list of items which are any value)
map of regression contrast variance
sig_file: (a list of items which are any value)
map of F-test significance (in -log10p)
ftest_file: (a list of items which are any value)
map of test statistic values
spatial_eigenvectors: (a file name)
map of spatial eigenvectors from residual PCA
frame_eigenvectors: (a file name)
matrix of frame eigenvectors from residual PCA
singular_values: (a file name)
matrix singular values from residual PCA
svd_stats_file: (a file name)
text file summarizing the residual PCA
Label2Annot¶
Wraps the executable command mris_label2annot.
Converts a set of surface labels to an annotation file
Examples¶
>>> from nipype.interfaces.freesurfer import Label2Annot
>>> l2a = Label2Annot()
>>> l2a.inputs.hemisphere = 'lh'
>>> l2a.inputs.subject_id = '10335'
>>> l2a.inputs.in_labels = ['lh.aparc.label']
>>> l2a.inputs.orig = 'lh.pial'
>>> l2a.inputs.out_annot = 'test'
>>> l2a.cmdline
'mris_label2annot --hemi lh --l lh.aparc.label --a test --s 10335'
Inputs:
[Mandatory]
hemisphere: ('lh' or 'rh')
Input hemisphere
argument: ``--hemi %s``
subject_id: (a string, nipype default value: subject_id)
Subject name/ID
argument: ``--s %s``
in_labels: (a list of items which are any value)
List of input label files
argument: ``--l %s...``
out_annot: (a string)
Name of the annotation to create
argument: ``--a %s``
orig: (an existing file name)
implicit {hemisphere}.orig
[Optional]
keep_max: (a boolean)
Keep label with highest 'stat' value
argument: ``--maxstatwinner``
verbose_off: (a boolean)
Turn off overlap and stat override messages
argument: ``--noverbose``
color_table: (an existing file name)
File that defines the structure names, their indices, and their
color
argument: ``--ctab %s``
copy_inputs: (a boolean)
copy implicit inputs and create a temp subjects_dir
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_file: (an existing file name)
Output annotation file
Label2Label¶
Wraps the executable command mri_label2label.
Converts a label in one subject’s space to a label in another subject’s space using either talairach or spherical as an intermediate registration space.
If a source mask is used, then the input label must have been created from a surface (ie, the vertex numbers are valid). The format can be anything supported by mri_convert or curv or paint. Vertices in the source label that do not meet threshold in the mask will be removed from the label.
Examples¶
>>> from nipype.interfaces.freesurfer import Label2Label
>>> l2l = Label2Label()
>>> l2l.inputs.hemisphere = 'lh'
>>> l2l.inputs.subject_id = '10335'
>>> l2l.inputs.sphere_reg = 'lh.pial'
>>> l2l.inputs.white = 'lh.pial'
>>> l2l.inputs.source_subject = 'fsaverage'
>>> l2l.inputs.source_label = 'lh-pial.stl'
>>> l2l.inputs.source_white = 'lh.pial'
>>> l2l.inputs.source_sphere_reg = 'lh.pial'
>>> l2l.cmdline
'mri_label2label --hemi lh --trglabel lh-pial_converted.stl --regmethod surface --srclabel lh-pial.stl --srcsubject fsaverage --trgsubject 10335'
Inputs:
[Mandatory]
hemisphere: ('lh' or 'rh')
Input hemisphere
argument: ``--hemi %s``
subject_id: (a string, nipype default value: subject_id)
Target subject
argument: ``--trgsubject %s``
sphere_reg: (an existing file name)
Implicit input <hemisphere>.sphere.reg
white: (an existing file name)
Implicit input <hemisphere>.white
source_sphere_reg: (an existing file name)
Implicit input <hemisphere>.sphere.reg
source_white: (an existing file name)
Implicit input <hemisphere>.white
source_label: (an existing file name)
Source label
argument: ``--srclabel %s``
source_subject: (a string)
Source subject name
argument: ``--srcsubject %s``
[Optional]
out_file: (a file name)
Target label
argument: ``--trglabel %s``
registration_method: ('surface' or 'volume', nipype default value:
surface)
Registration method
argument: ``--regmethod %s``
copy_inputs: (a boolean)
If running as a node, set this to True.This will copy the input
files to the node directory.
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_file: (an existing file name)
Output label
Label2Vol¶
Wraps the executable command mri_label2vol.
Make a binary volume from a Freesurfer label
Examples¶
>>> binvol = Label2Vol(label_file='cortex.label', template_file='structural.nii', reg_file='register.dat', fill_thresh=0.5, vol_label_file='foo_out.nii')
>>> binvol.cmdline
'mri_label2vol --fillthresh 0.5 --label cortex.label --reg register.dat --temp structural.nii --o foo_out.nii'
Inputs:
[Mandatory]
label_file: (a list of items which are an existing file name)
list of label files
argument: ``--label %s...``
mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
annot_file: (an existing file name)
surface annotation file
argument: ``--annot %s``
mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
requires: subject_id, hemi
seg_file: (an existing file name)
segmentation file
argument: ``--seg %s``
mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
aparc_aseg: (a boolean)
use aparc+aseg.mgz in subjectdir as seg
argument: ``--aparc+aseg``
mutually_exclusive: label_file, annot_file, seg_file, aparc_aseg
template_file: (an existing file name)
output template volume
argument: ``--temp %s``
[Optional]
reg_file: (an existing file name)
tkregister style matrix VolXYZ = R*LabelXYZ
argument: ``--reg %s``
mutually_exclusive: reg_file, reg_header, identity
reg_header: (an existing file name)
label template volume
argument: ``--regheader %s``
mutually_exclusive: reg_file, reg_header, identity
identity: (a boolean)
set R=I
argument: ``--identity``
mutually_exclusive: reg_file, reg_header, identity
invert_mtx: (a boolean)
Invert the registration matrix
argument: ``--invertmtx``
fill_thresh: (0.0 <= a floating point number <= 1.0)
thresh : between 0 and 1
argument: ``--fillthresh %g``
label_voxel_volume: (a float)
volume of each label point (def 1mm3)
argument: ``--labvoxvol %f``
proj: (a tuple of the form: ('abs' or 'frac', a float, a float, a
float))
project along surface normal
argument: ``--proj %s %f %f %f``
requires: subject_id, hemi
subject_id: (a unicode string)
subject id
argument: ``--subject %s``
hemi: ('lh' or 'rh')
hemisphere to use lh or rh
argument: ``--hemi %s``
surface: (a unicode string)
use surface instead of white
argument: ``--surf %s``
vol_label_file: (a file name)
output volume
argument: ``--o %s``
label_hit_file: (a file name)
file with each frame is nhits for a label
argument: ``--hits %s``
map_label_stat: (a file name)
map the label stats field into the vol
argument: ``--label-stat %s``
native_vox2ras: (a boolean)
use native vox2ras xform instead of tkregister-style
argument: ``--native-vox2ras``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
vol_label_file: (an existing file name)
output volume
MRISPreproc¶
Wraps the executable command mris_preproc.
Use FreeSurfer mris_preproc to prepare a group of contrasts for a second level analysis
Examples¶
>>> preproc = MRISPreproc()
>>> preproc.inputs.target = 'fsaverage'
>>> preproc.inputs.hemi = 'lh'
>>> preproc.inputs.vol_measure_file = [('cont1.nii', 'register.dat'), ('cont1a.nii', 'register.dat')]
>>> preproc.inputs.out_file = 'concatenated_file.mgz'
>>> preproc.cmdline
'mris_preproc --hemi lh --out concatenated_file.mgz --target fsaverage --iv cont1.nii register.dat --iv cont1a.nii register.dat'
Inputs:
[Mandatory]
target: (a unicode string)
target subject name
argument: ``--target %s``
hemi: ('lh' or 'rh')
hemisphere for source and target
argument: ``--hemi %s``
[Optional]
out_file: (a file name)
output filename
argument: ``--out %s``
surf_measure: (a unicode string)
Use subject/surf/hemi.surf_measure as input
argument: ``--meas %s``
mutually_exclusive: surf_measure, surf_measure_file, surf_area
surf_area: (a unicode string)
Extract vertex area from subject/surf/hemi.surfname to use as input.
argument: ``--area %s``
mutually_exclusive: surf_measure, surf_measure_file, surf_area
subjects: (a list of items which are any value)
subjects from who measures are calculated
argument: ``--s %s...``
mutually_exclusive: subjects, fsgd_file, subject_file
fsgd_file: (an existing file name)
specify subjects using fsgd file
argument: ``--fsgd %s``
mutually_exclusive: subjects, fsgd_file, subject_file
subject_file: (an existing file name)
file specifying subjects separated by white space
argument: ``--f %s``
mutually_exclusive: subjects, fsgd_file, subject_file
surf_measure_file: (a list of items which are an existing file name)
file alternative to surfmeas, still requires list of subjects
argument: ``--is %s...``
mutually_exclusive: surf_measure, surf_measure_file, surf_area
source_format: (a unicode string)
source format
argument: ``--srcfmt %s``
surf_dir: (a unicode string)
alternative directory (instead of surf)
argument: ``--surfdir %s``
vol_measure_file: (a list of items which are a tuple of the form: (an
existing file name, an existing file name))
list of volume measure and reg file tuples
argument: ``--iv %s %s...``
proj_frac: (a float)
projection fraction for vol2surf
argument: ``--projfrac %s``
fwhm: (a float)
smooth by fwhm mm on the target surface
argument: ``--fwhm %f``
mutually_exclusive: num_iters
num_iters: (an integer (int or long))
niters : smooth by niters on the target surface
argument: ``--niters %d``
mutually_exclusive: fwhm
fwhm_source: (a float)
smooth by fwhm mm on the source surface
argument: ``--fwhm-src %f``
mutually_exclusive: num_iters_source
num_iters_source: (an integer (int or long))
niters : smooth by niters on the source surface
argument: ``--niterssrc %d``
mutually_exclusive: fwhm_source
smooth_cortex_only: (a boolean)
only smooth cortex (ie, exclude medial wall)
argument: ``--smooth-cortex-only``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_file: (a file name)
preprocessed output file
MRISPreprocReconAll¶
Wraps the executable command mris_preproc.
Extends MRISPreproc to allow it to be used in a recon-all workflow
Examples¶
>>> preproc = MRISPreprocReconAll()
>>> preproc.inputs.target = 'fsaverage'
>>> preproc.inputs.hemi = 'lh'
>>> preproc.inputs.vol_measure_file = [('cont1.nii', 'register.dat'), ('cont1a.nii', 'register.dat')]
>>> preproc.inputs.out_file = 'concatenated_file.mgz'
>>> preproc.cmdline
'mris_preproc --hemi lh --out concatenated_file.mgz --s subject_id --target fsaverage --iv cont1.nii register.dat --iv cont1a.nii register.dat'
Inputs:
[Mandatory]
target: (a unicode string)
target subject name
argument: ``--target %s``
hemi: ('lh' or 'rh')
hemisphere for source and target
argument: ``--hemi %s``
[Optional]
surf_measure_file: (an existing file name)
file necessary for surfmeas
argument: ``--meas %s``
mutually_exclusive: surf_measure, surf_measure_file, surf_area
surfreg_files: (a list of items which are an existing file name)
lh and rh input surface registration files
argument: ``--surfreg %s``
requires: lh_surfreg_target, rh_surfreg_target
lh_surfreg_target: (a file name)
Implicit target surface registration file
requires: surfreg_files
rh_surfreg_target: (a file name)
Implicit target surface registration file
requires: surfreg_files
subject_id: (a string, nipype default value: subject_id)
subject from whom measures are calculated
argument: ``--s %s``
mutually_exclusive: subjects, fsgd_file, subject_file, subject_id
copy_inputs: (a boolean)
If running as a node, set this to True this will copy some implicit
inputs to the node directory.
out_file: (a file name)
output filename
argument: ``--out %s``
surf_measure: (a unicode string)
Use subject/surf/hemi.surf_measure as input
argument: ``--meas %s``
mutually_exclusive: surf_measure, surf_measure_file, surf_area
surf_area: (a unicode string)
Extract vertex area from subject/surf/hemi.surfname to use as input.
argument: ``--area %s``
mutually_exclusive: surf_measure, surf_measure_file, surf_area
subjects: (a list of items which are any value)
subjects from who measures are calculated
argument: ``--s %s...``
mutually_exclusive: subjects, fsgd_file, subject_file
fsgd_file: (an existing file name)
specify subjects using fsgd file
argument: ``--fsgd %s``
mutually_exclusive: subjects, fsgd_file, subject_file
subject_file: (an existing file name)
file specifying subjects separated by white space
argument: ``--f %s``
mutually_exclusive: subjects, fsgd_file, subject_file
source_format: (a unicode string)
source format
argument: ``--srcfmt %s``
surf_dir: (a unicode string)
alternative directory (instead of surf)
argument: ``--surfdir %s``
vol_measure_file: (a list of items which are a tuple of the form: (an
existing file name, an existing file name))
list of volume measure and reg file tuples
argument: ``--iv %s %s...``
proj_frac: (a float)
projection fraction for vol2surf
argument: ``--projfrac %s``
fwhm: (a float)
smooth by fwhm mm on the target surface
argument: ``--fwhm %f``
mutually_exclusive: num_iters
num_iters: (an integer (int or long))
niters : smooth by niters on the target surface
argument: ``--niters %d``
mutually_exclusive: fwhm
fwhm_source: (a float)
smooth by fwhm mm on the source surface
argument: ``--fwhm-src %f``
mutually_exclusive: num_iters_source
num_iters_source: (an integer (int or long))
niters : smooth by niters on the source surface
argument: ``--niterssrc %d``
mutually_exclusive: fwhm_source
smooth_cortex_only: (a boolean)
only smooth cortex (ie, exclude medial wall)
argument: ``--smooth-cortex-only``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_file: (a file name)
preprocessed output file
MS_LDA¶
Wraps the executable command mri_ms_LDA.
Perform LDA reduction on the intensity space of an arbitrary # of FLASH images
Examples¶
>>> grey_label = 2
>>> white_label = 3
>>> zero_value = 1
>>> optimalWeights = MS_LDA(lda_labels=[grey_label, white_label], label_file='label.mgz', weight_file='weights.txt', shift=zero_value, vol_synth_file='synth_out.mgz', conform=True, use_weights=True, images=['FLASH1.mgz', 'FLASH2.mgz', 'FLASH3.mgz'])
>>> optimalWeights.cmdline
'mri_ms_LDA -conform -label label.mgz -lda 2 3 -shift 1 -W -synth synth_out.mgz -weight weights.txt FLASH1.mgz FLASH2.mgz FLASH3.mgz'
Inputs:
[Mandatory]
lda_labels: (a list of from 2 to 2 items which are an integer (int or
long))
pair of class labels to optimize
argument: ``-lda %s``
weight_file: (a file name)
filename for the LDA weights (input or output)
argument: ``-weight %s``
vol_synth_file: (a file name)
filename for the synthesized output volume
argument: ``-synth %s``
images: (a list of items which are an existing file name)
list of input FLASH images
argument: ``%s``, position: -1
[Optional]
label_file: (a file name)
filename of the label volume
argument: ``-label %s``
mask_file: (a file name)
filename of the brain mask volume
argument: ``-mask %s``
shift: (an integer (int or long))
shift all values equal to the given value to zero
argument: ``-shift %d``
conform: (a boolean)
Conform the input volumes (brain mask typically already conformed)
argument: ``-conform``
use_weights: (a boolean)
Use the weights from a previously generated weight file
argument: ``-W``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
weight_file: (an existing file name)
vol_synth_file: (an existing file name)
OneSampleTTest¶
Wraps the executable command mri_glmfit.
Inputs:
[Mandatory]
in_file: (a file name)
input 4D file
argument: ``--y %s``
[Optional]
glm_dir: (a unicode string)
save outputs to dir
argument: ``--glmdir %s``
fsgd: (a tuple of the form: (an existing file name, 'doss' or
'dods'))
freesurfer descriptor file
argument: ``--fsgd %s %s``
mutually_exclusive: fsgd, design, one_sample
design: (an existing file name)
design matrix file
argument: ``--X %s``
mutually_exclusive: fsgd, design, one_sample
contrast: (a list of items which are an existing file name)
contrast file
argument: ``--C %s...``
one_sample: (a boolean)
construct X and C as a one-sample group mean
argument: ``--osgm``
mutually_exclusive: one_sample, fsgd, design, contrast
no_contrast_ok: (a boolean)
do not fail if no contrasts specified
argument: ``--no-contrasts-ok``
per_voxel_reg: (a list of items which are an existing file name)
per-voxel regressors
argument: ``--pvr %s...``
self_reg: (a tuple of the form: (an integer (int or long), an integer
(int or long), an integer (int or long)))
self-regressor from index col row slice
argument: ``--selfreg %d %d %d``
weighted_ls: (an existing file name)
weighted least squares
argument: ``--wls %s``
mutually_exclusive: weight_file, weight_inv, weight_sqrt
fixed_fx_var: (an existing file name)
for fixed effects analysis
argument: ``--yffxvar %s``
fixed_fx_dof: (an integer (int or long))
dof for fixed effects analysis
argument: ``--ffxdof %d``
mutually_exclusive: fixed_fx_dof_file
fixed_fx_dof_file: (a file name)
text file with dof for fixed effects analysis
argument: ``--ffxdofdat %d``
mutually_exclusive: fixed_fx_dof
weight_file: (an existing file name)
weight for each input at each voxel
mutually_exclusive: weighted_ls
weight_inv: (a boolean)
invert weights
argument: ``--w-inv``
mutually_exclusive: weighted_ls
weight_sqrt: (a boolean)
sqrt of weights
argument: ``--w-sqrt``
mutually_exclusive: weighted_ls
fwhm: (a floating point number >= 0.0)
smooth input by fwhm
argument: ``--fwhm %f``
var_fwhm: (a floating point number >= 0.0)
smooth variance by fwhm
argument: ``--var-fwhm %f``
no_mask_smooth: (a boolean)
do not mask when smoothing
argument: ``--no-mask-smooth``
no_est_fwhm: (a boolean)
turn off FWHM output estimation
argument: ``--no-est-fwhm``
mask_file: (an existing file name)
binary mask
argument: ``--mask %s``
label_file: (an existing file name)
use label as mask, surfaces only
argument: ``--label %s``
mutually_exclusive: cortex
cortex: (a boolean)
use subjects ?h.cortex.label as label
argument: ``--cortex``
mutually_exclusive: label_file
invert_mask: (a boolean)
invert mask
argument: ``--mask-inv``
prune: (a boolean)
remove voxels that do not have a non-zero value at each frame (def)
argument: ``--prune``
no_prune: (a boolean)
do not prune
argument: ``--no-prune``
mutually_exclusive: prunethresh
prune_thresh: (a float)
prune threshold. Default is FLT_MIN
argument: ``--prune_thr %f``
mutually_exclusive: noprune
compute_log_y: (a boolean)
compute natural log of y prior to analysis
argument: ``--logy``
save_estimate: (a boolean)
save signal estimate (yhat)
argument: ``--yhat-save``
save_residual: (a boolean)
save residual error (eres)
argument: ``--eres-save``
save_res_corr_mtx: (a boolean)
save residual error spatial correlation matrix (eres.scm). Big!
argument: ``--eres-scm``
surf: (a boolean)
analysis is on a surface mesh
argument: ``--surf %s %s %s``
requires: subject_id, hemi
subject_id: (a unicode string)
subject id for surface geometry
hemi: ('lh' or 'rh')
surface hemisphere
surf_geo: (a unicode string, nipype default value: white)
surface geometry name (e.g. white, pial)
simulation: (a tuple of the form: ('perm' or 'mc-full' or 'mc-z', an
integer (int or long), a float, a unicode string))
nulltype nsim thresh csdbasename
argument: ``--sim %s %d %f %s``
sim_sign: ('abs' or 'pos' or 'neg')
abs, pos, or neg
argument: ``--sim-sign %s``
uniform: (a tuple of the form: (a float, a float))
use uniform distribution instead of gaussian
argument: ``--uniform %f %f``
pca: (a boolean)
perform pca/svd analysis on residual
argument: ``--pca``
calc_AR1: (a boolean)
compute and save temporal AR1 of residual
argument: ``--tar1``
save_cond: (a boolean)
flag to save design matrix condition at each voxel
argument: ``--save-cond``
vox_dump: (a tuple of the form: (an integer (int or long), an integer
(int or long), an integer (int or long)))
dump voxel GLM and exit
argument: ``--voxdump %d %d %d``
seed: (an integer (int or long))
used for synthesizing noise
argument: ``--seed %d``
synth: (a boolean)
replace input with gaussian
argument: ``--synth``
resynth_test: (an integer (int or long))
test GLM by resynthsis
argument: ``--resynthtest %d``
profile: (an integer (int or long))
niters : test speed
argument: ``--profile %d``
force_perm: (a boolean)
force perumtation test, even when design matrix is not orthog
argument: ``--perm-force``
diag: (an integer (int or long))
Gdiag_no : set diagnositc level
argument: ``--diag %d``
diag_cluster: (a boolean)
save sig volume and exit from first sim loop
argument: ``--diag-cluster``
debug: (a boolean)
turn on debugging
argument: ``--debug``
check_opts: (a boolean)
don't run anything, just check options and exit
argument: ``--checkopts``
allow_repeated_subjects: (a boolean)
allow subject names to repeat in the fsgd file (must appear before
--fsgd
argument: ``--allowsubjrep``
allow_ill_cond: (a boolean)
allow ill-conditioned design matrices
argument: ``--illcond``
sim_done_file: (a file name)
create file when simulation finished
argument: ``--sim-done %s``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
glm_dir: (an existing directory name)
output directory
beta_file: (an existing file name)
map of regression coefficients
error_file: (a file name)
map of residual error
error_var_file: (a file name)
map of residual error variance
error_stddev_file: (a file name)
map of residual error standard deviation
estimate_file: (a file name)
map of the estimated Y values
mask_file: (a file name)
map of the mask used in the analysis
fwhm_file: (a file name)
text file with estimated smoothness
dof_file: (a file name)
text file with effective degrees-of-freedom for the analysis
gamma_file: (a list of items which are any value)
map of contrast of regression coefficients
gamma_var_file: (a list of items which are any value)
map of regression contrast variance
sig_file: (a list of items which are any value)
map of F-test significance (in -log10p)
ftest_file: (a list of items which are any value)
map of test statistic values
spatial_eigenvectors: (a file name)
map of spatial eigenvectors from residual PCA
frame_eigenvectors: (a file name)
matrix of frame eigenvectors from residual PCA
singular_values: (a file name)
matrix singular values from residual PCA
svd_stats_file: (a file name)
text file summarizing the residual PCA
SegStats¶
Wraps the executable command mri_segstats.
Use FreeSurfer mri_segstats for ROI analysis
Examples¶
>>> import nipype.interfaces.freesurfer as fs
>>> ss = fs.SegStats()
>>> ss.inputs.annot = ('PWS04', 'lh', 'aparc')
>>> ss.inputs.in_file = 'functional.nii'
>>> ss.inputs.subjects_dir = '.'
>>> ss.inputs.avgwf_txt_file = 'avgwf.txt'
>>> ss.inputs.summary_file = 'summary.stats'
>>> ss.cmdline
'mri_segstats --annot PWS04 lh aparc --avgwf ./avgwf.txt --i functional.nii --sum ./summary.stats'
Inputs:
[Mandatory]
segmentation_file: (an existing file name)
segmentation volume path
argument: ``--seg %s``
mutually_exclusive: segmentation_file, annot, surf_label
annot: (a tuple of the form: (a unicode string, 'lh' or 'rh', a
unicode string))
subject hemi parc : use surface parcellation
argument: ``--annot %s %s %s``
mutually_exclusive: segmentation_file, annot, surf_label
surf_label: (a tuple of the form: (a unicode string, 'lh' or 'rh', a
unicode string))
subject hemi label : use surface label
argument: ``--slabel %s %s %s``
mutually_exclusive: segmentation_file, annot, surf_label
[Optional]
summary_file: (a file name)
Segmentation stats summary table file
argument: ``--sum %s``, position: -1
partial_volume_file: (an existing file name)
Compensate for partial voluming
argument: ``--pv %s``
in_file: (an existing file name)
Use the segmentation to report stats on this volume
argument: ``--i %s``
frame: (an integer (int or long))
Report stats on nth frame of input volume
argument: ``--frame %d``
multiply: (a float)
multiply input by val
argument: ``--mul %f``
calc_snr: (a boolean)
save mean/std as extra column in output table
argument: ``--snr``
calc_power: ('sqr' or 'sqrt')
Compute either the sqr or the sqrt of the input
argument: ``--%s``
color_table_file: (an existing file name)
color table file with seg id names
argument: ``--ctab %s``
mutually_exclusive: color_table_file, default_color_table,
gca_color_table
default_color_table: (a boolean)
use $FREESURFER_HOME/FreeSurferColorLUT.txt
argument: ``--ctab-default``
mutually_exclusive: color_table_file, default_color_table,
gca_color_table
gca_color_table: (an existing file name)
get color table from GCA (CMA)
argument: ``--ctab-gca %s``
mutually_exclusive: color_table_file, default_color_table,
gca_color_table
segment_id: (a list of items which are any value)
Manually specify segmentation ids
argument: ``--id %s...``
exclude_id: (an integer (int or long))
Exclude seg id from report
argument: ``--excludeid %d``
exclude_ctx_gm_wm: (a boolean)
exclude cortical gray and white matter
argument: ``--excl-ctxgmwm``
wm_vol_from_surf: (a boolean)
Compute wm volume from surf
argument: ``--surf-wm-vol``
cortex_vol_from_surf: (a boolean)
Compute cortex volume from surf
argument: ``--surf-ctx-vol``
non_empty_only: (a boolean)
Only report nonempty segmentations
argument: ``--nonempty``
empty: (a boolean)
Report on segmentations listed in the color table
argument: ``--empty``
mask_file: (an existing file name)
Mask volume (same size as seg
argument: ``--mask %s``
mask_thresh: (a float)
binarize mask with this threshold <0.5>
argument: ``--maskthresh %f``
mask_sign: ('abs' or 'pos' or 'neg' or '--masksign %s')
Sign for mask threshold: pos, neg, or abs
mask_frame: (an integer (int or long))
Mask with this (0 based) frame of the mask volume
requires: mask_file
mask_invert: (a boolean)
Invert binarized mask volume
argument: ``--maskinvert``
mask_erode: (an integer (int or long))
Erode mask by some amount
argument: ``--maskerode %d``
brain_vol: ('brain-vol-from-seg' or 'brainmask')
Compute brain volume either with ``brainmask`` or ``brain-vol-from-
seg``
argument: ``--%s``
brainmask_file: (an existing file name)
Load brain mask and compute the volume of the brain as the non-zero
voxels in this volume
argument: ``--brainmask %s``
etiv: (a boolean)
Compute ICV from talairach transform
argument: ``--etiv``
etiv_only: ('etiv' or 'old-etiv' or '--%s-only')
Compute etiv and exit. Use ``etiv`` or ``old-etiv``
avgwf_txt_file: (a boolean or a file name)
Save average waveform into file (bool or filename)
argument: ``--avgwf %s``
avgwf_file: (a boolean or a file name)
Save as binary volume (bool or filename)
argument: ``--avgwfvol %s``
sf_avg_file: (a boolean or a file name)
Save mean across space and time
argument: ``--sfavg %s``
vox: (a list of items which are an integer (int or long))
Replace seg with all 0s except at C R S (three int inputs)
argument: ``--vox %s``
supratent: (a boolean)
Undocumented input flag
argument: ``--supratent``
subcort_gm: (a boolean)
Compute volume of subcortical gray matter
argument: ``--subcortgray``
total_gray: (a boolean)
Compute volume of total gray matter
argument: ``--totalgray``
euler: (a boolean)
Write out number of defect holes in orig.nofix based on the euler
number
argument: ``--euler``
in_intensity: (a file name)
Undocumented input norm.mgz file
argument: ``--in %s --in-intensity-name %s``
intensity_units: ('MR')
Intensity units
argument: ``--in-intensity-units %s``
requires: in_intensity
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
summary_file: (an existing file name)
Segmentation summary statistics table
avgwf_txt_file: (a file name)
Text file with functional statistics averaged over segs
avgwf_file: (a file name)
Volume with functional statistics averaged over segs
sf_avg_file: (a file name)
Text file with func statistics averaged over segs and framss
SegStatsReconAll¶
Wraps the executable command mri_segstats.
This class inherits SegStats and modifies it for use in a recon-all workflow. This implementation mandates implicit inputs that SegStats. To ensure backwards compatability of SegStats, this class was created.
Examples¶
>>> from nipype.interfaces.freesurfer import SegStatsReconAll
>>> segstatsreconall = SegStatsReconAll()
>>> segstatsreconall.inputs.annot = ('PWS04', 'lh', 'aparc')
>>> segstatsreconall.inputs.avgwf_txt_file = 'avgwf.txt'
>>> segstatsreconall.inputs.summary_file = 'summary.stats'
>>> segstatsreconall.inputs.subject_id = '10335'
>>> segstatsreconall.inputs.ribbon = 'wm.mgz'
>>> segstatsreconall.inputs.transform = 'trans.mat'
>>> segstatsreconall.inputs.presurf_seg = 'wm.mgz'
>>> segstatsreconall.inputs.lh_orig_nofix = 'lh.pial'
>>> segstatsreconall.inputs.rh_orig_nofix = 'lh.pial'
>>> segstatsreconall.inputs.lh_pial = 'lh.pial'
>>> segstatsreconall.inputs.rh_pial = 'lh.pial'
>>> segstatsreconall.inputs.lh_white = 'lh.pial'
>>> segstatsreconall.inputs.rh_white = 'lh.pial'
>>> segstatsreconall.inputs.empty = True
>>> segstatsreconall.inputs.brain_vol = 'brain-vol-from-seg'
>>> segstatsreconall.inputs.exclude_ctx_gm_wm = True
>>> segstatsreconall.inputs.supratent = True
>>> segstatsreconall.inputs.subcort_gm = True
>>> segstatsreconall.inputs.etiv = True
>>> segstatsreconall.inputs.wm_vol_from_surf = True
>>> segstatsreconall.inputs.cortex_vol_from_surf = True
>>> segstatsreconall.inputs.total_gray = True
>>> segstatsreconall.inputs.euler = True
>>> segstatsreconall.inputs.exclude_id = 0
>>> segstatsreconall.cmdline
'mri_segstats --annot PWS04 lh aparc --avgwf ./avgwf.txt --brain-vol-from-seg --surf-ctx-vol --empty --etiv --euler --excl-ctxgmwm --excludeid 0 --subcortgray --subject 10335 --supratent --totalgray --surf-wm-vol --sum ./summary.stats'
Inputs:
[Mandatory]
subject_id: (a string, nipype default value: subject_id)
Subject id being processed
argument: ``--subject %s``
ribbon: (a file name)
Input file mri/ribbon.mgz
transform: (an existing file name)
Input transform file
lh_orig_nofix: (an existing file name)
Input lh.orig.nofix
rh_orig_nofix: (an existing file name)
Input rh.orig.nofix
lh_white: (an existing file name)
Input file must be <subject_id>/surf/lh.white
rh_white: (an existing file name)
Input file must be <subject_id>/surf/rh.white
lh_pial: (an existing file name)
Input file must be <subject_id>/surf/lh.pial
rh_pial: (an existing file name)
Input file must be <subject_id>/surf/rh.pial
segmentation_file: (an existing file name)
segmentation volume path
argument: ``--seg %s``
mutually_exclusive: segmentation_file, annot, surf_label
annot: (a tuple of the form: (a unicode string, 'lh' or 'rh', a
unicode string))
subject hemi parc : use surface parcellation
argument: ``--annot %s %s %s``
mutually_exclusive: segmentation_file, annot, surf_label
surf_label: (a tuple of the form: (a unicode string, 'lh' or 'rh', a
unicode string))
subject hemi label : use surface label
argument: ``--slabel %s %s %s``
mutually_exclusive: segmentation_file, annot, surf_label
[Optional]
presurf_seg: (an existing file name)
Input segmentation volume
aseg: (an existing file name)
Mandatory implicit input in 5.3
copy_inputs: (a boolean)
If running as a node, set this to True otherwise, this will copy the
implicit inputs to the node directory.
summary_file: (a file name)
Segmentation stats summary table file
argument: ``--sum %s``, position: -1
partial_volume_file: (an existing file name)
Compensate for partial voluming
argument: ``--pv %s``
in_file: (an existing file name)
Use the segmentation to report stats on this volume
argument: ``--i %s``
frame: (an integer (int or long))
Report stats on nth frame of input volume
argument: ``--frame %d``
multiply: (a float)
multiply input by val
argument: ``--mul %f``
calc_snr: (a boolean)
save mean/std as extra column in output table
argument: ``--snr``
calc_power: ('sqr' or 'sqrt')
Compute either the sqr or the sqrt of the input
argument: ``--%s``
color_table_file: (an existing file name)
color table file with seg id names
argument: ``--ctab %s``
mutually_exclusive: color_table_file, default_color_table,
gca_color_table
default_color_table: (a boolean)
use $FREESURFER_HOME/FreeSurferColorLUT.txt
argument: ``--ctab-default``
mutually_exclusive: color_table_file, default_color_table,
gca_color_table
gca_color_table: (an existing file name)
get color table from GCA (CMA)
argument: ``--ctab-gca %s``
mutually_exclusive: color_table_file, default_color_table,
gca_color_table
segment_id: (a list of items which are any value)
Manually specify segmentation ids
argument: ``--id %s...``
exclude_id: (an integer (int or long))
Exclude seg id from report
argument: ``--excludeid %d``
exclude_ctx_gm_wm: (a boolean)
exclude cortical gray and white matter
argument: ``--excl-ctxgmwm``
wm_vol_from_surf: (a boolean)
Compute wm volume from surf
argument: ``--surf-wm-vol``
cortex_vol_from_surf: (a boolean)
Compute cortex volume from surf
argument: ``--surf-ctx-vol``
non_empty_only: (a boolean)
Only report nonempty segmentations
argument: ``--nonempty``
empty: (a boolean)
Report on segmentations listed in the color table
argument: ``--empty``
mask_file: (an existing file name)
Mask volume (same size as seg
argument: ``--mask %s``
mask_thresh: (a float)
binarize mask with this threshold <0.5>
argument: ``--maskthresh %f``
mask_sign: ('abs' or 'pos' or 'neg' or '--masksign %s')
Sign for mask threshold: pos, neg, or abs
mask_frame: (an integer (int or long))
Mask with this (0 based) frame of the mask volume
requires: mask_file
mask_invert: (a boolean)
Invert binarized mask volume
argument: ``--maskinvert``
mask_erode: (an integer (int or long))
Erode mask by some amount
argument: ``--maskerode %d``
brain_vol: ('brain-vol-from-seg' or 'brainmask')
Compute brain volume either with ``brainmask`` or ``brain-vol-from-
seg``
argument: ``--%s``
brainmask_file: (an existing file name)
Load brain mask and compute the volume of the brain as the non-zero
voxels in this volume
argument: ``--brainmask %s``
etiv: (a boolean)
Compute ICV from talairach transform
argument: ``--etiv``
etiv_only: ('etiv' or 'old-etiv' or '--%s-only')
Compute etiv and exit. Use ``etiv`` or ``old-etiv``
avgwf_txt_file: (a boolean or a file name)
Save average waveform into file (bool or filename)
argument: ``--avgwf %s``
avgwf_file: (a boolean or a file name)
Save as binary volume (bool or filename)
argument: ``--avgwfvol %s``
sf_avg_file: (a boolean or a file name)
Save mean across space and time
argument: ``--sfavg %s``
vox: (a list of items which are an integer (int or long))
Replace seg with all 0s except at C R S (three int inputs)
argument: ``--vox %s``
supratent: (a boolean)
Undocumented input flag
argument: ``--supratent``
subcort_gm: (a boolean)
Compute volume of subcortical gray matter
argument: ``--subcortgray``
total_gray: (a boolean)
Compute volume of total gray matter
argument: ``--totalgray``
euler: (a boolean)
Write out number of defect holes in orig.nofix based on the euler
number
argument: ``--euler``
in_intensity: (a file name)
Undocumented input norm.mgz file
argument: ``--in %s --in-intensity-name %s``
intensity_units: ('MR')
Intensity units
argument: ``--in-intensity-units %s``
requires: in_intensity
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
summary_file: (an existing file name)
Segmentation summary statistics table
avgwf_txt_file: (a file name)
Text file with functional statistics averaged over segs
avgwf_file: (a file name)
Volume with functional statistics averaged over segs
sf_avg_file: (a file name)
Text file with func statistics averaged over segs and framss
SphericalAverage¶
Wraps the executable command mris_spherical_average.
This program will add a template into an average surface.
Examples¶
>>> from nipype.interfaces.freesurfer import SphericalAverage
>>> sphericalavg = SphericalAverage()
>>> sphericalavg.inputs.out_file = 'test.out'
>>> sphericalavg.inputs.in_average = '.'
>>> sphericalavg.inputs.in_surf = 'lh.pial'
>>> sphericalavg.inputs.hemisphere = 'lh'
>>> sphericalavg.inputs.fname = 'lh.entorhinal'
>>> sphericalavg.inputs.which = 'label'
>>> sphericalavg.inputs.subject_id = '10335'
>>> sphericalavg.inputs.erode = 2
>>> sphericalavg.inputs.threshold = 5
>>> sphericalavg.cmdline
'mris_spherical_average -erode 2 -o 10335 -t 5.0 label lh.entorhinal lh pial . test.out'
Inputs:
[Mandatory]
in_surf: (an existing file name)
Input surface file
argument: ``%s``, position: -3
hemisphere: ('lh' or 'rh')
Input hemisphere
argument: ``%s``, position: -4
fname: (a string)
Filename from the average subject directory.
Example: to use rh.entorhinal.label as the input label
filename, set fname to 'rh.entorhinal' and which to
'label'. The program will then search for
'{in_average}/label/rh.entorhinal.label'
argument: ``%s``, position: -5
which: ('coords' or 'label' or 'vals' or 'curv' or 'area')
No documentation
argument: ``%s``, position: -6
subject_id: (a string)
Output subject id
argument: ``-o %s``
[Optional]
out_file: (a file name)
Output filename
argument: ``%s``, position: -1
in_average: (a directory name)
Average subject
argument: ``%s``, position: -2
erode: (an integer (int or long))
Undocumented
argument: ``-erode %d``
in_orig: (an existing file name)
Original surface filename
argument: ``-orig %s``
threshold: (a float)
Undocumented
argument: ``-t %.1f``
subjects_dir: (an existing directory name)
subjects directory
args: (a unicode string)
Additional parameters to the command
argument: ``%s``
environ: (a dictionary with keys which are a bytes or None or a value
of class 'str' and with values which are a bytes or None or a
value of class 'str', nipype default value: {})
Environment variables
Outputs:
out_file: (a file name)
Output label