fMRI: OpenfMRI.org data, FSL¶
A growing number of datasets are available on OpenfMRI. This script demonstrates how to use nipype to analyze a data set:
python fmri_openfmri.py --datasetdir ds107
from __future__ import division
from builtins import range
from glob import glob
import os
import nipype.pipeline.engine as pe
import nipype.algorithms.modelgen as model
import nipype.algorithms.rapidart as ra
import nipype.interfaces.fsl as fsl
import nipype.interfaces.io as nio
import nipype.interfaces.utility as niu
from nipype.external.six import string_types
from nipype.workflows.fmri.fsl import (create_featreg_preproc,
create_modelfit_workflow,
create_fixed_effects_flow,
create_reg_workflow)
fsl.FSLCommand.set_default_output_type('NIFTI_GZ')
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]])
files = glob(os.path.join(base_dir,
subject_id,
'BOLD',
'task%03d_run*' % (idx + 1)))
run_ids.insert(idx, list(range(1, len(files) + 1)))
TR = np.genfromtxt(os.path.join(base_dir, 'scan_key.txt'))[1]
return run_ids[task_id - 1], conds[task_id - 1], TR
def analyze_openfmri_dataset(data_dir, subject=None, model_id=None,
task_id=None, output_dir=None, subj_prefix='*'):
"""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()
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
datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run_id',
'task_id', 'model_id'],
outfields=['anat', 'bold', 'behav',
'contrasts']),
name='datasource')
datasource.inputs.base_directory = data_dir
datasource.inputs.template = '*'
datasource.inputs.field_template = {'anat': '%s/anatomy/highres001.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']]}
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
contrast_def = np.genfromtxt(contrast_file, dtype=object)
if len(contrast_def.shape) == 1:
contrast_def = contrast_def[None, :]
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):
out_behav = []
if isinstance(behav, string_types):
behav = [behav]
for val in behav:
if not isinstance(val, list):
out_behav.append([val])
else:
out_behav.append(val)
return out_behav
wf.connect(subjinfo, 'TR', modelspec, 'time_repetition')
wf.connect(datasource, ('behav', check_behav_list), modelspec, 'event_files')
wf.connect(subjinfo, 'TR', modelfit, 'inputspec.interscan_interval')
wf.connect(subjinfo, 'conds', contrastgen, 'conds')
wf.connect(datasource, 'contrasts', contrastgen, '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')])
])
Reorder the copes so that now it combines across runs
def sort_copes(files):
numelements = len(files[0])
outfiles = []
for i in range(numelements):
outfiles.insert(i, [])
for j, elements in enumerate(files):
outfiles[i].append(elements[i])
return outfiles
def num_copes(files):
return len(files)
pickfirst = lambda x: x[0]
wf.connect([(preproc, fixed_fx, [(('outputspec.mask', pickfirst),
'flameo.mask_file')]),
(modelfit, fixed_fx, [(('outputspec.copes', sort_copes),
'inputspec.copes'),
('outputspec.dof_file',
'inputspec.dof_files'),
(('outputspec.varcopes',
sort_copes),
'inputspec.varcopes'),
(('outputspec.copes', num_copes),
'l2model.num_copes'),
])
])
wf.connect(preproc, 'outputspec.mean', registration, 'inputspec.mean_image')
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')
Connect to a datasink
def get_subs(subject_id, conds, 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)))
return subs
subsgen = pe.Node(niu.Function(input_names=['subject_id', 'conds',
'model_id', 'task_id'],
output_names=['substitutions'],
function=get_subs),
name='subsgen')
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([(splitfunc, datasink,
[('copes', 'copes.mni'),
('varcopes', 'varcopes.mni'),
('zstats', 'zstats.mni'),
])])
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
hpcutoff = 120.
preproc.inputs.inputspec.fwhm = 6.0
gethighpass.inputs.hpcutoff = hpcutoff
modelspec.inputs.high_pass_filter_cutoff = hpcutoff
modelfit.inputs.inputspec.bases = {'dgamma': {'derivs': True}}
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("-o", "--output_dir", dest="outdir",
help="Output directory base")
parser.add_argument("-w", "--work_dir", dest="work_dir",
help="Working 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")
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))
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)
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.