fMRI: OpenfMRI.org data, FSL, ANTS, c3daffine¶
A growing number of datasets are available on OpenfMRI. This script demonstrates how to use nipype to analyze a data set:
python fmri_ants_openfmri.py --datasetdir ds107
from __future__ import division, unicode_literals
from builtins import open, range, str, bytes
from glob import glob
import os
from nipype import config
from nipype import LooseVersion
from nipype import Workflow, Node, MapNode
from nipype.utils.filemanip import filename_to_list
import nipype.pipeline.engine as pe
import nipype.algorithms.modelgen as model
import nipype.algorithms.rapidart as ra
from nipype.algorithms.misc import TSNR
from nipype.interfaces.c3 import C3dAffineTool
from nipype.interfaces import fsl, Function, ants, freesurfer as fs
import nipype.interfaces.io as nio
from nipype.interfaces.io import FreeSurferSource
import nipype.interfaces.utility as niu
from nipype.interfaces.utility import Merge, IdentityInterface
from nipype.workflows.fmri.fsl import (create_featreg_preproc,
create_modelfit_workflow,
create_fixed_effects_flow)
from nipype.utils import NUMPY_MMAP
config.enable_provenance()
version = 0
if (fsl.Info.version() and LooseVersion(fsl.Info.version()) > LooseVersion('5.0.6')):
version = 507
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
imports = [
'import os',
'import nibabel as nb',
'import numpy as np',
'import scipy as sp',
'from nipype.utils.filemanip import filename_to_list, list_to_filename, split_filename',
'from scipy.special import legendre'
]
def median(in_files):
"""Computes an average of the median of each realigned timeseries
Parameters
----------
in_files: one or more realigned Nifti 4D time series
Returns
-------
out_file: a 3D Nifti file
"""
average = None
for idx, filename in enumerate(filename_to_list(in_files)):
img = nb.load(filename, mmap=NUMPY_MMAP)
data = np.median(img.get_data(), axis=3)
if average is None:
average = data
else:
average = average + data
median_img = nb.Nifti1Image(average / float(idx + 1), img.affine,
img.header)
filename = os.path.join(os.getcwd(), 'median.nii.gz')
median_img.to_filename(filename)
return filename
def create_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
name : name of workflow (default: 'registration')
Inputs:
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.anatomical_image : anatomical image to coregister to
inputspec.target_image : registration target
Outputs:
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
"""
register = pe.Workflow(name=name)
inputnode = pe.Node(interface=niu.IdentityInterface(fields=['source_files',
'mean_image',
'anatomical_image',
'target_image',
'target_image_brain',
'config_file']),
name='inputspec')
outputnode = pe.Node(interface=niu.IdentityInterface(fields=['func2anat_transform',
'anat2target_transform',
'transformed_files',
'transformed_mean',
'anat2target',
'mean2anat_mask'
]),
name='outputspec')
Estimate the tissue classes from the anatomical image. But use spm’s segment as FSL appears to be breaking.
stripper = pe.Node(fsl.BET(), name='stripper')
register.connect(inputnode, 'anatomical_image', stripper, 'in_file')
fast = pe.Node(fsl.FAST(), name='fast')
register.connect(stripper, 'out_file', fast, 'in_files')
Binarize the segmentation
binarize = pe.Node(fsl.ImageMaths(op_string='-nan -thr 0.5 -bin'),
name='binarize')
pickindex = lambda x, i: x[i]
register.connect(fast, ('partial_volume_files', pickindex, 2),
binarize, 'in_file')
Calculate rigid transform from mean image to anatomical image
mean2anat = pe.Node(fsl.FLIRT(), name='mean2anat')
mean2anat.inputs.dof = 6
register.connect(inputnode, 'mean_image', mean2anat, 'in_file')
register.connect(stripper, 'out_file', mean2anat, 'reference')
Now use bbr cost function to improve the transform
mean2anatbbr = pe.Node(fsl.FLIRT(), name='mean2anatbbr')
mean2anatbbr.inputs.dof = 6
mean2anatbbr.inputs.cost = 'bbr'
mean2anatbbr.inputs.schedule = os.path.join(os.getenv('FSLDIR'),
'etc/flirtsch/bbr.sch')
register.connect(inputnode, 'mean_image', mean2anatbbr, 'in_file')
register.connect(binarize, 'out_file', mean2anatbbr, 'wm_seg')
register.connect(inputnode, 'anatomical_image', mean2anatbbr, 'reference')
register.connect(mean2anat, 'out_matrix_file',
mean2anatbbr, 'in_matrix_file')
Create a mask of the median image coregistered to the anatomical image
mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
register.connect(mean2anatbbr, 'out_file', mean2anat_mask, 'in_file')
Convert the BBRegister transformation to ANTS ITK format
convert2itk = pe.Node(C3dAffineTool(),
name='convert2itk')
convert2itk.inputs.fsl2ras = True
convert2itk.inputs.itk_transform = True
register.connect(mean2anatbbr, 'out_matrix_file', convert2itk, 'transform_file')
register.connect(inputnode, 'mean_image', convert2itk, 'source_file')
register.connect(stripper, 'out_file', convert2itk, 'reference_file')
Compute registration between the subject’s structural and MNI template This is currently set to perform a very quick registration. However, the registration can be made significantly more accurate for cortical structures by increasing the number of iterations All parameters are set using the example from: #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
reg = pe.Node(ants.Registration(), name='antsRegister')
reg.inputs.output_transform_prefix = "output_"
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[100, 30, 20]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.initial_moving_transform_com = True
reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
reg.inputs.convergence_window_size = [20] * 2 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 3
reg.inputs.use_histogram_matching = [False] * 2 + [True]
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.args = '--float'
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.num_threads = 4
reg.plugin_args = {'qsub_args': '-pe orte 4',
'sbatch_args': '--mem=6G -c 4'}
register.connect(stripper, 'out_file', reg, 'moving_image')
register.connect(inputnode, 'target_image_brain', reg, 'fixed_image')
Concatenate the affine and ants transforms into a list
pickfirst = lambda x: x[0]
merge = pe.Node(niu.Merge(2), iterfield=['in2'], name='mergexfm')
register.connect(convert2itk, 'itk_transform', merge, 'in2')
register.connect(reg, 'composite_transform', merge, 'in1')
Transform the mean image. First to anatomical and then to target
warpmean = pe.Node(ants.ApplyTransforms(),
name='warpmean')
warpmean.inputs.input_image_type = 0
warpmean.inputs.interpolation = 'Linear'
warpmean.inputs.invert_transform_flags = [False, False]
warpmean.inputs.terminal_output = 'file'
register.connect(inputnode, 'target_image_brain', warpmean, 'reference_image')
register.connect(inputnode, 'mean_image', warpmean, 'input_image')
register.connect(merge, 'out', warpmean, 'transforms')
Transform the remaining images. First to anatomical and then to target
warpall = pe.MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 0
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.inputs.terminal_output = 'file'
register.connect(inputnode, 'target_image_brain', warpall, 'reference_image')
register.connect(inputnode, 'source_files', warpall, 'input_image')
register.connect(merge, 'out', warpall, 'transforms')
Assign all the output files
register.connect(reg, 'warped_image', outputnode, 'anat2target')
register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
register.connect(warpall, 'output_image', outputnode, 'transformed_files')
register.connect(mean2anatbbr, 'out_matrix_file',
outputnode, 'func2anat_transform')
register.connect(mean2anat_mask, 'mask_file',
outputnode, 'mean2anat_mask')
register.connect(reg, 'composite_transform',
outputnode, 'anat2target_transform')
return register
def get_aparc_aseg(files):
"""Return the aparc+aseg.mgz file"""
for name in files:
if 'aparc+aseg.mgz' in name:
return name
raise ValueError('aparc+aseg.mgz not found')
def create_fs_reg_workflow(name='registration'):
"""Create a FEAT preprocessing workflow together with freesurfer
Parameters
----------
name : name of workflow (default: 'registration')
Inputs::
inputspec.source_files : files (filename or list of filenames to register)
inputspec.mean_image : reference image to use
inputspec.target_image : registration target
Outputs::
outputspec.func2anat_transform : FLIRT transform
outputspec.anat2target_transform : FLIRT+FNIRT transform
outputspec.transformed_files : transformed files in target space
outputspec.transformed_mean : mean image in target space
"""
register = Workflow(name=name)
inputnode = Node(interface=IdentityInterface(fields=['source_files',
'mean_image',
'subject_id',
'subjects_dir',
'target_image']),
name='inputspec')
outputnode = Node(interface=IdentityInterface(fields=['func2anat_transform',
'out_reg_file',
'anat2target_transform',
'transforms',
'transformed_mean',
'transformed_files',
'min_cost_file',
'anat2target',
'aparc',
'mean2anat_mask'
]),
name='outputspec')
# Get the subject's freesurfer source directory
fssource = Node(FreeSurferSource(),
name='fssource')
fssource.run_without_submitting = True
register.connect(inputnode, 'subject_id', fssource, 'subject_id')
register.connect(inputnode, 'subjects_dir', fssource, 'subjects_dir')
convert = Node(freesurfer.MRIConvert(out_type='nii'),
name="convert")
register.connect(fssource, 'T1', convert, 'in_file')
# Coregister the median to the surface
bbregister = Node(freesurfer.BBRegister(registered_file=True),
name='bbregister')
bbregister.inputs.init = 'fsl'
bbregister.inputs.contrast_type = 't2'
bbregister.inputs.out_fsl_file = True
bbregister.inputs.epi_mask = True
register.connect(inputnode, 'subject_id', bbregister, 'subject_id')
register.connect(inputnode, 'mean_image', bbregister, 'source_file')
register.connect(inputnode, 'subjects_dir', bbregister, 'subjects_dir')
# Create a mask of the median coregistered to the anatomical image
mean2anat_mask = Node(fsl.BET(mask=True), name='mean2anat_mask')
register.connect(bbregister, 'registered_file', mean2anat_mask, 'in_file')
use aparc+aseg’s brain mask
binarize = Node(fs.Binarize(min=0.5, out_type="nii.gz", dilate=1), name="binarize_aparc")
register.connect(fssource, ("aparc_aseg", get_aparc_aseg), binarize, "in_file")
stripper = Node(fsl.ApplyMask(), name='stripper')
register.connect(binarize, "binary_file", stripper, "mask_file")
register.connect(convert, 'out_file', stripper, 'in_file')
Apply inverse transform to aparc file
aparcxfm = Node(freesurfer.ApplyVolTransform(inverse=True,
interp='nearest'),
name='aparc_inverse_transform')
register.connect(inputnode, 'subjects_dir', aparcxfm, 'subjects_dir')
register.connect(bbregister, 'out_reg_file', aparcxfm, 'reg_file')
register.connect(fssource, ('aparc_aseg', get_aparc_aseg),
aparcxfm, 'target_file')
register.connect(inputnode, 'mean_image', aparcxfm, 'source_file')
Convert the BBRegister transformation to ANTS ITK format
convert2itk = Node(C3dAffineTool(), name='convert2itk')
convert2itk.inputs.fsl2ras = True
convert2itk.inputs.itk_transform = True
register.connect(bbregister, 'out_fsl_file', convert2itk, 'transform_file')
register.connect(inputnode, 'mean_image', convert2itk, 'source_file')
register.connect(stripper, 'out_file', convert2itk, 'reference_file')
Compute registration between the subject’s structural and MNI template This is currently set to perform a very quick registration. However, the registration can be made significantly more accurate for cortical structures by increasing the number of iterations All parameters are set using the example from: #https://github.com/stnava/ANTs/blob/master/Scripts/newAntsExample.sh
reg = Node(ants.Registration(), name='antsRegister')
reg.inputs.output_transform_prefix = "output_"
reg.inputs.transforms = ['Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1,), (0.1,), (0.2, 3.0, 0.0)]
reg.inputs.number_of_iterations = [[10000, 11110, 11110]] * 2 + [[100, 30, 20]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = True
reg.inputs.initial_moving_transform_com = True
reg.inputs.metric = ['Mattes'] * 2 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 2 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 2 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 2 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 2 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 2 + [-0.01]
reg.inputs.convergence_window_size = [20] * 2 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 2 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 3
reg.inputs.shrink_factors = [[3, 2, 1]] * 2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 3
reg.inputs.use_histogram_matching = [False] * 2 + [True]
reg.inputs.winsorize_lower_quantile = 0.005
reg.inputs.winsorize_upper_quantile = 0.995
reg.inputs.float = True
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.num_threads = 4
reg.plugin_args = {'qsub_args': '-pe orte 4',
'sbatch_args': '--mem=6G -c 4'}
register.connect(stripper, 'out_file', reg, 'moving_image')
register.connect(inputnode, 'target_image', reg, 'fixed_image')
Concatenate the affine and ants transforms into a list
pickfirst = lambda x: x[0]
merge = Node(Merge(2), iterfield=['in2'], name='mergexfm')
register.connect(convert2itk, 'itk_transform', merge, 'in2')
register.connect(reg, 'composite_transform', merge, 'in1')
Transform the mean image. First to anatomical and then to target
warpmean = Node(ants.ApplyTransforms(), name='warpmean')
warpmean.inputs.input_image_type = 0
warpmean.inputs.interpolation = 'Linear'
warpmean.inputs.invert_transform_flags = [False, False]
warpmean.inputs.terminal_output = 'file'
warpmean.inputs.args = '--float'
# warpmean.inputs.num_threads = 4
# warpmean.plugin_args = {'sbatch_args': '--mem=4G -c 4'}
Transform the remaining images. First to anatomical and then to target
warpall = pe.MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 0
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.inputs.terminal_output = 'file'
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 2
warpall.plugin_args = {'sbatch_args': '--mem=6G -c 2'}
Assign all the output files
register.connect(warpmean, 'output_image', outputnode, 'transformed_mean')
register.connect(warpall, 'output_image', outputnode, 'transformed_files')
register.connect(inputnode, 'target_image', warpmean, 'reference_image')
register.connect(inputnode, 'mean_image', warpmean, 'input_image')
register.connect(merge, 'out', warpmean, 'transforms')
register.connect(inputnode, 'target_image', warpall, 'reference_image')
register.connect(inputnode, 'source_files', warpall, 'input_image')
register.connect(merge, 'out', warpall, 'transforms')
Assign all the output files
register.connect(reg, 'warped_image', outputnode, 'anat2target')
register.connect(aparcxfm, 'transformed_file',
outputnode, 'aparc')
register.connect(bbregister, 'out_fsl_file',
outputnode, 'func2anat_transform')
register.connect(bbregister, 'out_reg_file',
outputnode, 'out_reg_file')
register.connect(bbregister, 'min_cost_file',
outputnode, 'min_cost_file')
register.connect(mean2anat_mask, 'mask_file',
outputnode, 'mean2anat_mask')
register.connect(reg, 'composite_transform',
outputnode, 'anat2target_transform')
register.connect(merge, 'out', outputnode, 'transforms')
return register
Get info for a given subject
def get_subjectinfo(subject_id, base_dir, task_id, model_id):
"""Get info for a given subject
Parameters
----------
subject_id : string
Subject identifier (e.g., sub001)
base_dir : string
Path to base directory of the dataset
task_id : int
Which task to process
model_id : int
Which model to process
Returns
-------
run_ids : list of ints
Run numbers
conds : list of str
Condition names
TR : float
Repetition time
"""
from glob import glob
import os
import numpy as np
condition_info = []
cond_file = os.path.join(base_dir, 'models', 'model%03d' % model_id,
'condition_key.txt')
with open(cond_file, 'rt') as fp:
for line in fp:
info = line.strip().split()
condition_info.append([info[0], info[1], ' '.join(info[2:])])
if len(condition_info) == 0:
raise ValueError('No condition info found in %s' % cond_file)
taskinfo = np.array(condition_info)
n_tasks = len(np.unique(taskinfo[:, 0]))
conds = []
run_ids = []
if task_id > n_tasks:
raise ValueError('Task id %d does not exist' % task_id)
for idx in range(n_tasks):
taskidx = np.where(taskinfo[:, 0] == 'task%03d' % (idx + 1))
conds.append([condition.replace(' ', '_') for condition
in taskinfo[taskidx[0], 2]]) # if 'junk' not in condition])
files = sorted(glob(os.path.join(base_dir,
subject_id,
'BOLD',
'task%03d_run*' % (idx + 1))))
runs = [int(val[-3:]) for val in files]
run_ids.insert(idx, runs)
json_info = os.path.join(base_dir, subject_id, 'BOLD',
'task%03d_run%03d' % (task_id, run_ids[task_id - 1][0]),
'bold_scaninfo.json')
if os.path.exists(json_info):
import json
with open(json_info, 'rt') as fp:
data = json.load(fp)
TR = data['global']['const']['RepetitionTime'] / 1000.
else:
task_scan_key = os.path.join(base_dir, subject_id, 'BOLD',
'task%03d_run%03d' % (task_id, run_ids[task_id - 1][0]),
'scan_key.txt')
if os.path.exists(task_scan_key):
TR = np.genfromtxt(task_scan_key)[1]
else:
TR = np.genfromtxt(os.path.join(base_dir, 'scan_key.txt'))[1]
return run_ids[task_id - 1], conds[task_id - 1], TR
Analyzes an open fmri dataset
def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
task_id=None, output_dir=None, subj_prefix='*',
hpcutoff=120., use_derivatives=True,
fwhm=6.0, subjects_dir=None, target=None):
"""Analyzes an open fmri dataset
Parameters
----------
data_dir : str
Path to the base data directory
work_dir : str
Nipype working directory (defaults to cwd)
"""
Load nipype workflows
preproc = create_featreg_preproc(whichvol='first')
modelfit = create_modelfit_workflow()
fixed_fx = create_fixed_effects_flow()
if subjects_dir:
registration = create_fs_reg_workflow()
else:
registration = create_reg_workflow()
Remove the plotting connection so that plot iterables don’t propagate to the model stage
preproc.disconnect(preproc.get_node('plot_motion'), 'out_file',
preproc.get_node('outputspec'), 'motion_plots')
Set up openfmri data specific components
subjects = sorted([path.split(os.path.sep)[-1] for path in
glob(os.path.join(data_dir, subj_prefix))])
infosource = pe.Node(niu.IdentityInterface(fields=['subject_id',
'model_id',
'task_id']),
name='infosource')
if len(subject) == 0:
infosource.iterables = [('subject_id', subjects),
('model_id', [model_id]),
('task_id', task_id)]
else:
infosource.iterables = [('subject_id',
[subjects[subjects.index(subj)] for subj in subject]),
('model_id', [model_id]),
('task_id', task_id)]
subjinfo = pe.Node(niu.Function(input_names=['subject_id', 'base_dir',
'task_id', 'model_id'],
output_names=['run_id', 'conds', 'TR'],
function=get_subjectinfo),
name='subjectinfo')
subjinfo.inputs.base_dir = data_dir
Return data components as anat, bold and behav
contrast_file = os.path.join(data_dir, 'models', 'model%03d' % model_id,
'task_contrasts.txt')
has_contrast = os.path.exists(contrast_file)
if has_contrast:
datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
'task_id', 'model_id'],
outfields=['anat', 'bold', 'behav',
'contrasts']),
name='datasource')
else:
datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
'task_id', 'model_id'],
outfields=['anat', 'bold', 'behav']),
name='datasource')
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '*'
if has_contrast:
datasource.inputs.field_template = {'anat': '%s/anatomy/T1_001.nii.gz',
'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
'behav': ('%s/model/model%03d/onsets/task%03d_'
'run%03d/cond*.txt'),
'contrasts': ('models/model%03d/'
'task_contrasts.txt')}
datasource.inputs.template_args = {'anat': [['subject_id']],
'bold': [['subject_id', 'task_id']],
'behav': [['subject_id', 'model_id',
'task_id', 'run_id']],
'contrasts': [['model_id']]}
else:
datasource.inputs.field_template = {'anat': '%s/anatomy/T1_001.nii.gz',
'bold': '%s/BOLD/task%03d_r*/bold.nii.gz',
'behav': ('%s/model/model%03d/onsets/task%03d_'
'run%03d/cond*.txt')}
datasource.inputs.template_args = {'anat': [['subject_id']],
'bold': [['subject_id', 'task_id']],
'behav': [['subject_id', 'model_id',
'task_id', 'run_id']]}
datasource.inputs.sort_filelist = True
Create meta workflow
wf = pe.Workflow(name='openfmri')
wf.connect(infosource, 'subject_id', subjinfo, 'subject_id')
wf.connect(infosource, 'model_id', subjinfo, 'model_id')
wf.connect(infosource, 'task_id', subjinfo, 'task_id')
wf.connect(infosource, 'subject_id', datasource, 'subject_id')
wf.connect(infosource, 'model_id', datasource, 'model_id')
wf.connect(infosource, 'task_id', datasource, 'task_id')
wf.connect(subjinfo, 'run_id', datasource, 'run_id')
wf.connect([(datasource, preproc, [('bold', 'inputspec.func')]),
])
def get_highpass(TR, hpcutoff):
return hpcutoff / (2. * TR)
gethighpass = pe.Node(niu.Function(input_names=['TR', 'hpcutoff'],
output_names=['highpass'],
function=get_highpass),
name='gethighpass')
wf.connect(subjinfo, 'TR', gethighpass, 'TR')
wf.connect(gethighpass, 'highpass', preproc, 'inputspec.highpass')
Setup a basic set of contrasts, a t-test per condition
def get_contrasts(contrast_file, task_id, conds):
import numpy as np
import os
contrast_def = []
if os.path.exists(contrast_file):
with open(contrast_file, 'rt') as fp:
contrast_def.extend([np.array(row.split()) for row in fp.readlines() if row.strip()])
contrasts = []
for row in contrast_def:
if row[0] != 'task%03d' % task_id:
continue
con = [row[1], 'T', ['cond%03d' % (i + 1) for i in range(len(conds))],
row[2:].astype(float).tolist()]
contrasts.append(con)
# add auto contrasts for each column
for i, cond in enumerate(conds):
con = [cond, 'T', ['cond%03d' % (i + 1)], [1]]
contrasts.append(con)
return contrasts
contrastgen = pe.Node(niu.Function(input_names=['contrast_file',
'task_id', 'conds'],
output_names=['contrasts'],
function=get_contrasts),
name='contrastgen')
art = pe.MapNode(interface=ra.ArtifactDetect(use_differences=[True, False],
use_norm=True,
norm_threshold=1,
zintensity_threshold=3,
parameter_source='FSL',
mask_type='file'),
iterfield=['realigned_files', 'realignment_parameters',
'mask_file'],
name="art")
modelspec = pe.Node(interface=model.SpecifyModel(),
name="modelspec")
modelspec.inputs.input_units = 'secs'
def check_behav_list(behav, run_id, conds):
import numpy as np
num_conds = len(conds)
if isinstance(behav, (str, bytes)):
behav = [behav]
behav_array = np.array(behav).flatten()
num_elements = behav_array.shape[0]
return behav_array.reshape(int(num_elements / num_conds),
num_conds).tolist()
reshape_behav = pe.Node(niu.Function(input_names=['behav', 'run_id', 'conds'],
output_names=['behav'],
function=check_behav_list),
name='reshape_behav')
wf.connect(subjinfo, 'TR', modelspec, 'time_repetition')
wf.connect(datasource, 'behav', reshape_behav, 'behav')
wf.connect(subjinfo, 'run_id', reshape_behav, 'run_id')
wf.connect(subjinfo, 'conds', reshape_behav, 'conds')
wf.connect(reshape_behav, 'behav', modelspec, 'event_files')
wf.connect(subjinfo, 'TR', modelfit, 'inputspec.interscan_interval')
wf.connect(subjinfo, 'conds', contrastgen, 'conds')
if has_contrast:
wf.connect(datasource, 'contrasts', contrastgen, 'contrast_file')
else:
contrastgen.inputs.contrast_file = ''
wf.connect(infosource, 'task_id', contrastgen, 'task_id')
wf.connect(contrastgen, 'contrasts', modelfit, 'inputspec.contrasts')
wf.connect([(preproc, art, [('outputspec.motion_parameters',
'realignment_parameters'),
('outputspec.realigned_files',
'realigned_files'),
('outputspec.mask', 'mask_file')]),
(preproc, modelspec, [('outputspec.highpassed_files',
'functional_runs'),
('outputspec.motion_parameters',
'realignment_parameters')]),
(art, modelspec, [('outlier_files', 'outlier_files')]),
(modelspec, modelfit, [('session_info',
'inputspec.session_info')]),
(preproc, modelfit, [('outputspec.highpassed_files',
'inputspec.functional_data')])
])
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(preproc, "outputspec.realigned_files", tsnr, "in_file")
# Compute the median image across runs
calc_median = Node(Function(input_names=['in_files'],
output_names=['median_file'],
function=median,
imports=imports),
name='median')
wf.connect(tsnr, 'detrended_file', calc_median, 'in_files')
Reorder the copes so that now it combines across runs
def sort_copes(copes, varcopes, contrasts):
import numpy as np
if not isinstance(copes, list):
copes = [copes]
varcopes = [varcopes]
num_copes = len(contrasts)
n_runs = len(copes)
all_copes = np.array(copes).flatten()
all_varcopes = np.array(varcopes).flatten()
outcopes = all_copes.reshape(int(len(all_copes) / num_copes),
num_copes).T.tolist()
outvarcopes = all_varcopes.reshape(int(len(all_varcopes) / num_copes),
num_copes).T.tolist()
return outcopes, outvarcopes, n_runs
cope_sorter = pe.Node(niu.Function(input_names=['copes', 'varcopes',
'contrasts'],
output_names=['copes', 'varcopes',
'n_runs'],
function=sort_copes),
name='cope_sorter')
pickfirst = lambda x: x[0]
wf.connect(contrastgen, 'contrasts', cope_sorter, 'contrasts')
wf.connect([(preproc, fixed_fx, [(('outputspec.mask', pickfirst),
'flameo.mask_file')]),
(modelfit, cope_sorter, [('outputspec.copes', 'copes')]),
(modelfit, cope_sorter, [('outputspec.varcopes', 'varcopes')]),
(cope_sorter, fixed_fx, [('copes', 'inputspec.copes'),
('varcopes', 'inputspec.varcopes'),
('n_runs', 'l2model.num_copes')]),
(modelfit, fixed_fx, [('outputspec.dof_file',
'inputspec.dof_files'),
])
])
wf.connect(calc_median, 'median_file', registration, 'inputspec.mean_image')
if subjects_dir:
wf.connect(infosource, 'subject_id', registration, 'inputspec.subject_id')
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
if target:
registration.inputs.inputspec.target_image = target
else:
wf.connect(datasource, 'anat', registration, 'inputspec.anatomical_image')
registration.inputs.inputspec.target_image = fsl.Info.standard_image('MNI152_T1_2mm.nii.gz')
registration.inputs.inputspec.target_image_brain = fsl.Info.standard_image('MNI152_T1_2mm_brain.nii.gz')
registration.inputs.inputspec.config_file = 'T1_2_MNI152_2mm'
def merge_files(copes, varcopes, zstats):
out_files = []
splits = []
out_files.extend(copes)
splits.append(len(copes))
out_files.extend(varcopes)
splits.append(len(varcopes))
out_files.extend(zstats)
splits.append(len(zstats))
return out_files, splits
mergefunc = pe.Node(niu.Function(input_names=['copes', 'varcopes',
'zstats'],
output_names=['out_files', 'splits'],
function=merge_files),
name='merge_files')
wf.connect([(fixed_fx.get_node('outputspec'), mergefunc,
[('copes', 'copes'),
('varcopes', 'varcopes'),
('zstats', 'zstats'),
])])
wf.connect(mergefunc, 'out_files', registration, 'inputspec.source_files')
def split_files(in_files, splits):
copes = in_files[:splits[0]]
varcopes = in_files[splits[0]:(splits[0] + splits[1])]
zstats = in_files[(splits[0] + splits[1]):]
return copes, varcopes, zstats
splitfunc = pe.Node(niu.Function(input_names=['in_files', 'splits'],
output_names=['copes', 'varcopes',
'zstats'],
function=split_files),
name='split_files')
wf.connect(mergefunc, 'splits', splitfunc, 'splits')
wf.connect(registration, 'outputspec.transformed_files',
splitfunc, 'in_files')
if subjects_dir:
get_roi_mean = pe.MapNode(fs.SegStats(default_color_table=True),
iterfield=['in_file'], name='get_aparc_means')
get_roi_mean.inputs.avgwf_txt_file = True
wf.connect(fixed_fx.get_node('outputspec'), 'copes', get_roi_mean, 'in_file')
wf.connect(registration, 'outputspec.aparc', get_roi_mean, 'segmentation_file')
get_roi_tsnr = pe.MapNode(fs.SegStats(default_color_table=True),
iterfield=['in_file'], name='get_aparc_tsnr')
get_roi_tsnr.inputs.avgwf_txt_file = True
wf.connect(tsnr, 'tsnr_file', get_roi_tsnr, 'in_file')
wf.connect(registration, 'outputspec.aparc', get_roi_tsnr, 'segmentation_file')
Connect to a datasink
def get_subs(subject_id, conds, run_id, model_id, task_id):
subs = [('_subject_id_%s_' % subject_id, '')]
subs.append(('_model_id_%d' % model_id, 'model%03d' % model_id))
subs.append(('task_id_%d/' % task_id, '/task%03d_' % task_id))
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_warp',
'mean'))
subs.append(('bold_dtype_mcf_mask_smooth_mask_gms_tempfilt_mean_flirt',
'affine'))
for i in range(len(conds)):
subs.append(('_flameo%d/cope1.' % i, 'cope%02d.' % (i + 1)))
subs.append(('_flameo%d/varcope1.' % i, 'varcope%02d.' % (i + 1)))
subs.append(('_flameo%d/zstat1.' % i, 'zstat%02d.' % (i + 1)))
subs.append(('_flameo%d/tstat1.' % i, 'tstat%02d.' % (i + 1)))
subs.append(('_flameo%d/res4d.' % i, 'res4d%02d.' % (i + 1)))
subs.append(('_warpall%d/cope1_warp.' % i,
'cope%02d.' % (i + 1)))
subs.append(('_warpall%d/varcope1_warp.' % (len(conds) + i),
'varcope%02d.' % (i + 1)))
subs.append(('_warpall%d/zstat1_warp.' % (2 * len(conds) + i),
'zstat%02d.' % (i + 1)))
subs.append(('_warpall%d/cope1_trans.' % i,
'cope%02d.' % (i + 1)))
subs.append(('_warpall%d/varcope1_trans.' % (len(conds) + i),
'varcope%02d.' % (i + 1)))
subs.append(('_warpall%d/zstat1_trans.' % (2 * len(conds) + i),
'zstat%02d.' % (i + 1)))
subs.append(('__get_aparc_means%d/' % i, '/cope%02d_' % (i + 1)))
for i, run_num in enumerate(run_id):
subs.append(('__get_aparc_tsnr%d/' % i, '/run%02d_' % run_num))
subs.append(('__art%d/' % i, '/run%02d_' % run_num))
subs.append(('__dilatemask%d/' % i, '/run%02d_' % run_num))
subs.append(('__realign%d/' % i, '/run%02d_' % run_num))
subs.append(('__modelgen%d/' % i, '/run%02d_' % run_num))
subs.append(('/model%03d/task%03d/' % (model_id, task_id), '/'))
subs.append(('/model%03d/task%03d_' % (model_id, task_id), '/'))
subs.append(('_bold_dtype_mcf_bet_thresh_dil', '_mask'))
subs.append(('_output_warped_image', '_anat2target'))
subs.append(('median_flirt_brain_mask', 'median_brain_mask'))
subs.append(('median_bbreg_brain_mask', 'median_brain_mask'))
return subs
subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds', 'run_id',
'model_id', 'task_id'],
output_names=['substitutions'],
function=get_subs),
name='subsgen')
wf.connect(subjinfo, 'run_id', subsgen, 'run_id')
datasink = pe.Node(interface=nio.DataSink(),
name="datasink")
wf.connect(infosource, 'subject_id', datasink, 'container')
wf.connect(infosource, 'subject_id', subsgen, 'subject_id')
wf.connect(infosource, 'model_id', subsgen, 'model_id')
wf.connect(infosource, 'task_id', subsgen, 'task_id')
wf.connect(contrastgen, 'contrasts', subsgen, 'conds')
wf.connect(subsgen, 'substitutions', datasink, 'substitutions')
wf.connect([(fixed_fx.get_node('outputspec'), datasink,
[('res4d', 'res4d'),
('copes', 'copes'),
('varcopes', 'varcopes'),
('zstats', 'zstats'),
('tstats', 'tstats')])
])
wf.connect([(modelfit.get_node('modelgen'), datasink,
[('design_cov', 'qa.model'),
('design_image', 'qa.model.@matrix_image'),
('design_file', 'qa.model.@matrix'),
])])
wf.connect([(preproc, datasink, [('outputspec.motion_parameters',
'qa.motion'),
('outputspec.motion_plots',
'qa.motion.plots'),
('outputspec.mask', 'qa.mask')])])
wf.connect(registration, 'outputspec.mean2anat_mask', datasink, 'qa.mask.mean2anat')
wf.connect(art, 'norm_files', datasink, 'qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'qa.art.@outlier_files')
wf.connect(registration, 'outputspec.anat2target', datasink, 'qa.anat2target')
wf.connect(tsnr, 'tsnr_file', datasink, 'qa.tsnr.@map')
if subjects_dir:
wf.connect(registration, 'outputspec.min_cost_file', datasink, 'qa.mincost')
wf.connect([(get_roi_tsnr, datasink, [('avgwf_txt_file', 'qa.tsnr'),
('summary_file', 'qa.tsnr.@summary')])])
wf.connect([(get_roi_mean, datasink, [('avgwf_txt_file', 'copes.roi'),
('summary_file', 'copes.roi.@summary')])])
wf.connect([(splitfunc, datasink,
[('copes', 'copes.mni'),
('varcopes', 'varcopes.mni'),
('zstats', 'zstats.mni'),
])])
wf.connect(calc_median, 'median_file', datasink, 'mean')
wf.connect(registration, 'outputspec.transformed_mean', datasink, 'mean.mni')
wf.connect(registration, 'outputspec.func2anat_transform', datasink, 'xfm.mean2anat')
wf.connect(registration, 'outputspec.anat2target_transform', datasink, 'xfm.anat2target')
Set processing parameters
preproc.inputs.inputspec.fwhm = fwhm
gethighpass.inputs.hpcutoff = hpcutoff
modelspec.inputs.high_pass_filter_cutoff = hpcutoff
modelfit.inputs.inputspec.bases = {'dgamma': {'derivs': use_derivatives}}
modelfit.inputs.inputspec.model_serial_correlations = True
modelfit.inputs.inputspec.film_threshold = 1000
datasink.inputs.base_directory = output_dir
return wf
The following functions run the whole workflow.
if __name__ == '__main__':
import argparse
defstr = ' (default %(default)s)'
parser = argparse.ArgumentParser(prog='fmri_openfmri.py',
description=__doc__)
parser.add_argument('-d', '--datasetdir', required=True)
parser.add_argument('-s', '--subject', default=[],
nargs='+', type=str,
help="Subject name (e.g. 'sub001')")
parser.add_argument('-m', '--model', default=1,
help="Model index" + defstr)
parser.add_argument('-x', '--subjectprefix', default='sub*',
help="Subject prefix" + defstr)
parser.add_argument('-t', '--task', default=1, # nargs='+',
type=int, help="Task index" + defstr)
parser.add_argument('--hpfilter', default=120.,
type=float, help="High pass filter cutoff (in secs)" + defstr)
parser.add_argument('--fwhm', default=6.,
type=float, help="Spatial FWHM" + defstr)
parser.add_argument('--derivatives', action="store_true",
help="Use derivatives" + defstr)
parser.add_argument("-o", "--output_dir", dest="outdir",
help="Output directory base")
parser.add_argument("-w", "--work_dir", dest="work_dir",
help="Output directory base")
parser.add_argument("-p", "--plugin", dest="plugin",
default='Linear',
help="Plugin to use")
parser.add_argument("--plugin_args", dest="plugin_args",
help="Plugin arguments")
parser.add_argument("--sd", dest="subjects_dir",
help="FreeSurfer subjects directory (if available)")
parser.add_argument("--target", dest="target_file",
help=("Target in MNI space. Best to use the MindBoggle "
"template - only used with FreeSurfer"
"OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz"))
args = parser.parse_args()
outdir = args.outdir
work_dir = os.getcwd()
if args.work_dir:
work_dir = os.path.abspath(args.work_dir)
if outdir:
outdir = os.path.abspath(outdir)
else:
outdir = os.path.join(work_dir, 'output')
outdir = os.path.join(outdir, 'model%02d' % int(args.model),
'task%03d' % int(args.task))
derivatives = args.derivatives
if derivatives is None:
derivatives = False
wf = analyze_openfmri_dataset(data_dir=os.path.abspath(args.datasetdir),
subject=args.subject,
model_id=int(args.model),
task_id=[int(args.task)],
subj_prefix=args.subjectprefix,
output_dir=outdir,
hpcutoff=args.hpfilter,
use_derivatives=derivatives,
fwhm=args.fwhm,
subjects_dir=args.subjects_dir,
target=args.target_file)
# wf.config['execution']['remove_unnecessary_outputs'] = False
wf.base_dir = work_dir
if args.plugin_args:
wf.run(args.plugin, plugin_args=eval(args.plugin_args))
else:
wf.run(args.plugin)
Example source code
You can download the full source code of this example.
This same script is also included in the Nipype source distribution under the
examples directory.