def return_fd_tsnr_dist(population, out_dir, pipeline_name):
fd_means=[]
tsnr_files = []
mask_files =[]
missing_subjects = []
for subject in population:
subject_dir = os.path.join(out_dir, pipeline_name, subject)
mkdir_path(os.path.join(subject_dir, 'quality_control'))
qc_dir = os.path.join(subject_dir, 'quality_control')
subject_dir = os.path.join(out_dir, pipeline_name, subject)
fd1d = os.path.join(subject_dir, 'functional_motion_FDPower/FD.1D')
if os.path.isfile(fd1d):
fd_means.append(np.mean(np.genfromtxt(fd1d)))
else:
print subject,'has no fd1d'
missing_subjects.append(subject)
os.chdir(qc_dir)
pp_file = os.path.join(subject_dir, 'functional_native_brain_preproc/REST_calc_resample_corrected_volreg_maths_brain.nii.gz')
tsnr_file = os.path.join(qc_dir,'REST_calc_resample_corrected_volreg_maths_brain_tsnr.nii.gz')
mask_file = os.path.join(subject_dir, 'functional_native_brain_preproc_mask/REST_calc_resample_corrected_volreg_maths_brain_mask.nii.gz')
if os.path.isfile(tsnr_file):
tsnr_files.append(tsnr_file)
mask_files.append(mask_file)
else:
if os.path.isfile(pp_file):
tsnr = TSNR()
tsnr.inputs.in_file = pp_file
res = tsnr.run()
tsnr_files.append(res.outputs.tsnr_file)
else:
print subject,'has no functional_native_preproc'
tsnr_distributions = volumes.get_median_distribution(tsnr_files, mask_files)
population_fd_means = fd_means
np.savetxt(os.path.join(out_dir, 'GluConnectivity', 'population_fd_distributions.txt'), population_fd_means)
np.savetxt(os.path.join(out_dir, 'GluConnectivity', 'population_tsnr_distributions.txt'), tsnr_distributions)
print 'FD mean=', population_fd_means
print 'TSNR_distribution=', tsnr_distributions
print ''
def get_distributions(population, workspace_dir):
fd_means = []
tsnr_brain_medians = []
tsnr_medians_first = []
print 'Grabbing FD and TSNR population distribution'
for subject in population:
# print subject
subject_dir = os.path.join(workspace_dir, subject)
qcdir = os.path.join(subject_dir,'QUALITY_CONTROL')
mkdir_path(qcdir)
os.chdir(qcdir)
print subject
#fd
movpar = os.path.join(subject_dir,'FUNC_PPROC/MOTION_CORRECTION/REST_DISCO_MOCO_2.1D')
fd = calc_FD_power(movpar)
fd_means.append(np.mean(np.loadtxt(fd)))
#tsnr
func_native = os.path.join(subject_dir, 'FUNC_PPROC/REST_PPROC_NATIVE_BRAIN.nii.gz')
func_native_mask = os.path.join(subject_dir, 'FUNC_PPROC/REST_PPROC_NATIVE_BRAIN_mask_ero.nii.gz')
func_native_first = os.path.join(subject_dir, 'FUNC_TRANSFORM/NATIVE_FUNC_FIRST.nii.gz')
# if not os.path.join(qcdir, 'REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz'):
print 'tsnr'
if not os.path.isfile(os.path.join(qcdir, 'REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz')):
tsnr = TSNR()
tsnr.inputs.in_file = func_native
tsnr.run()
tsnr_data = nb.load('REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz').get_data()
nan_mask = np.logical_not(np.isnan(tsnr_data))
brain_mask = nb.load(func_native_mask).get_data() > 0
first_mask = nb.load(func_native_first).get_data() > 0
tsnr_brain_median = np.median(tsnr_data[np.logical_and(nan_mask, brain_mask)])
tsnr_first_median = np.median(tsnr_data[np.logical_and(nan_mask, first_mask)])
print tsnr_brain_median, tsnr_first_median
tsnr_brain_medians.append(tsnr_brain_median)
tsnr_medians_first.append(tsnr_first_median)
np.savetxt(os.path.join(workspace_dir, 'population_fd_distributions.txt'), fd_means)
np.savetxt(os.path.join(workspace_dir, 'population_tsnr_distributions.txt'), tsnr_brain_medians)
np.savetxt(os.path.join(workspace_dir, 'population_tsnr_first_distributions.txt'), tsnr_medians_first)
def test_TSNR_outputs():
output_map = dict(detrended_file=dict(),
stddev_file=dict(),
tsnr_file=dict(),
mean_file=dict(),
)
outputs = TSNR.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_TSNR_outputs():
output_map = dict(
detrended_file=dict(),
mean_file=dict(),
stddev_file=dict(),
tsnr_file=dict(),
)
outputs = TSNR.output_spec()
for key, metadata in output_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(outputs.traits()[key], metakey), value
def test_TSNR_inputs():
input_map = dict(ignore_exception=dict(nohash=True,
usedefault=True,
),
regress_poly=dict(),
in_file=dict(mandatory=True,
),
)
inputs = TSNR.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def test_TSNR_inputs():
input_map = dict(
ignore_exception=dict(
nohash=True,
usedefault=True,
),
in_file=dict(mandatory=True, ),
regress_poly=dict(),
)
inputs = TSNR.input_spec()
for key, metadata in input_map.items():
for metakey, value in metadata.items():
yield assert_equal, getattr(inputs.traits()[key], metakey), value
def t_compcor(wf_name="t_compcor"):
cc = pe.Workflow(name=wf_name)
# Define nodes
inputnode = pe.Node(interface=util.IdentityInterface(
fields=['func', 'num_noise_components']),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(
fields=['noise_mask_file', 'noise_components', 'residual_file']),
name='outputspec')
tsnr = pe.MapNode(TSNR(regress_poly=2), name='tsnr', iterfield=['in_file'])
getthresh = pe.MapNode(interface=fsl.ImageStats(op_string='-p 98'),
name='getthreshold',
iterfield=['in_file'])
threshold_stddev = pe.MapNode(fsl.Threshold(),
name='threshold',
iterfield=['in_file', 'thresh'])
compcor = pe.MapNode(util.Function(
input_names=['realigned_file', 'noise_mask_file', 'num_components'],
output_names=['noise_components'],
function=extract_noise_components),
name='compcorr',
iterfield=['realigned_file', 'noise_mask_file'])
remove_noise = pe.MapNode(fsl.FilterRegressor(filter_all=True),
name='remove_noise',
iterfield=['in_file', 'design_file'])
cc.connect(inputnode, 'func', tsnr, 'in_file')
cc.connect(tsnr, 'stddev_file', threshold_stddev, 'in_file')
cc.connect(tsnr, 'stddev_file', getthresh, 'in_file')
cc.connect(getthresh, 'out_stat', threshold_stddev, 'thresh')
cc.connect(inputnode, 'func', compcor, 'realigned_file')
cc.connect(threshold_stddev, 'out_file', compcor, 'noise_mask_file')
cc.connect(inputnode, 'num_noise_components', compcor, 'num_components')
cc.connect(tsnr, 'detrended_file', remove_noise, 'in_file')
cc.connect(compcor, 'noise_components', remove_noise, 'design_file')
cc.connect(compcor, 'noise_components', outputnode, 'noise_components')
cc.connect(remove_noise, 'out_file', outputnode, 'residual_file')
cc.connect(threshold_stddev, 'out_file', outputnode, 'noise_mask_file')
return cc
def compcorr(name='compcorr'):
from nipype.workflows.rsfmri.fsl.resting import extract_noise_components
from nipype.algorithms.misc import TSNR
wkfl = pe.Workflow(name=name)
inputnode = pe.Node(utility.IdentityInterface(
fields=['in_file', 'mask', 'num_components']),
name='inputspec')
outputnode = pe.Node(utility.IdentityInterface(fields=['corrected_file']),
name='outputspec')
tsnr = pe.Node(TSNR(), name='tsnr')
getthresh = pe.Node(interface=fsl.ImageStats(op_string='-k %s -p 98'),
name='getthreshold')
threshold_stddev = pe.Node(fsl.Threshold(), name='threshold')
compcor = pe.Node(
utility.Function(input_names=[
'realigned_file', 'noise_mask_file', 'num_components'
],
output_names=['noise_components'],
function=extract_noise_components),
name='compcorr',
)
remove_noise = pe.Node(
fsl.FilterRegressor(filter_all=True),
name='remove_noise',
)
wkfl.connect([
(inputnode, tsnr, [('in_file', 'in_file')]),
(inputnode, compcor, [('in_file', 'realigned_file'),
('num_components', 'num_components')]),
(tsnr, threshold_stddev, [('stddev_file', 'in_file')]),
(tsnr, getthresh, [('stddev_file', 'in_file')]),
(inputnode, getthresh, [('mask', 'mask_file')]),
(inputnode, remove_noise, [('in_file', 'in_file')]),
(getthresh, threshold_stddev, [('out_stat', 'thresh')]),
(threshold_stddev, compcor, [('out_file', 'noise_mask_file')]),
(compcor, remove_noise, [('noise_components', 'design_file')]),
(inputnode, remove_noise, [('mask', 'mask')]),
(remove_noise, outputnode, [('out_file', 'corrected_file')]),
])
return wkfl
def create_workflow(files,
subject_id,
n_vol=0,
despike=True,
TR=None,
slice_times=None,
slice_thickness=None,
fieldmap_images=[],
norm_threshold=1,
num_components=6,
vol_fwhm=None,
surf_fwhm=None,
lowpass_freq=-1,
highpass_freq=-1,
sink_directory=os.getcwd(),
FM_TEdiff=2.46,
FM_sigma=2,
FM_echo_spacing=.7,
target_subject=['fsaverage3', 'fsaverage4'],
name='resting'):
wf = Workflow(name=name)
# Skip starting volumes
remove_vol = MapNode(fsl.ExtractROI(t_min=n_vol, t_size=-1),
iterfield=['in_file'],
name="remove_volumes")
remove_vol.inputs.in_file = files
# Run AFNI's despike. This is always run, however, whether this is fed to
# realign depends on the input configuration
despiker = MapNode(afni.Despike(outputtype='NIFTI_GZ'),
iterfield=['in_file'],
name='despike')
#despiker.plugin_args = {'qsub_args': '-l nodes=1:ppn='}
wf.connect(remove_vol, 'roi_file', despiker, 'in_file')
# Run Nipy joint slice timing and realignment algorithm
realign = Node(nipy.SpaceTimeRealigner(), name='realign')
realign.inputs.tr = TR
realign.inputs.slice_times = slice_times
realign.inputs.slice_info = 2
if despike:
wf.connect(despiker, 'out_file', realign, 'in_file')
else:
wf.connect(remove_vol, 'roi_file', realign, 'in_file')
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(realign, 'out_file', 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')
# Coregister the median to the surface
register = Node(freesurfer.BBRegister(), name='bbregister')
register.inputs.subject_id = subject_id
register.inputs.init = 'fsl'
register.inputs.contrast_type = 't2'
register.inputs.out_fsl_file = True
register.inputs.epi_mask = True
# Compute fieldmaps and unwarp using them
if fieldmap_images:
fieldmap = Node(interface=EPIDeWarp(), name='fieldmap_unwarp')
fieldmap.inputs.tediff = FM_TEdiff
fieldmap.inputs.esp = FM_echo_spacing
fieldmap.inputs.sigma = FM_sigma
fieldmap.inputs.mag_file = fieldmap_images[0]
fieldmap.inputs.dph_file = fieldmap_images[1]
wf.connect(calc_median, 'median_file', fieldmap, 'exf_file')
dewarper = MapNode(interface=fsl.FUGUE(),
iterfield=['in_file'],
name='dewarper')
wf.connect(tsnr, 'detrended_file', dewarper, 'in_file')
wf.connect(fieldmap, 'exf_mask', dewarper, 'mask_file')
wf.connect(fieldmap, 'vsm_file', dewarper, 'shift_in_file')
wf.connect(fieldmap, 'exfdw', register, 'source_file')
else:
wf.connect(calc_median, 'median_file', register, 'source_file')
# Get the subject's freesurfer source directory
fssource = Node(FreeSurferSource(), name='fssource')
fssource.inputs.subject_id = subject_id
fssource.inputs.subjects_dir = os.environ['SUBJECTS_DIR']
# Extract wm+csf, brain masks by eroding freesurfer labels and then
# transform the masks into the space of the median
wmcsf = Node(freesurfer.Binarize(), name='wmcsfmask')
mask = wmcsf.clone('anatmask')
wmcsftransform = Node(freesurfer.ApplyVolTransform(inverse=True,
interp='nearest'),
name='wmcsftransform')
wmcsftransform.inputs.subjects_dir = os.environ['SUBJECTS_DIR']
wmcsf.inputs.wm_ven_csf = True
wmcsf.inputs.match = [4, 5, 14, 15, 24, 31, 43, 44, 63]
wmcsf.inputs.binary_file = 'wmcsf.nii.gz'
wmcsf.inputs.erode = int(np.ceil(slice_thickness))
wf.connect(fssource, ('aparc_aseg', get_aparc_aseg), wmcsf, 'in_file')
if fieldmap_images:
wf.connect(fieldmap, 'exf_mask', wmcsftransform, 'source_file')
else:
wf.connect(calc_median, 'median_file', wmcsftransform, 'source_file')
wf.connect(register, 'out_reg_file', wmcsftransform, 'reg_file')
wf.connect(wmcsf, 'binary_file', wmcsftransform, 'target_file')
mask.inputs.binary_file = 'mask.nii.gz'
mask.inputs.dilate = int(np.ceil(slice_thickness)) + 1
mask.inputs.erode = int(np.ceil(slice_thickness))
mask.inputs.min = 0.5
wf.connect(fssource, ('aparc_aseg', get_aparc_aseg), mask, 'in_file')
masktransform = wmcsftransform.clone("masktransform")
if fieldmap_images:
wf.connect(fieldmap, 'exf_mask', masktransform, 'source_file')
else:
wf.connect(calc_median, 'median_file', masktransform, 'source_file')
wf.connect(register, 'out_reg_file', masktransform, 'reg_file')
wf.connect(mask, 'binary_file', masktransform, 'target_file')
# Compute Art outliers
art = Node(interface=ArtifactDetect(use_differences=[True, False],
use_norm=True,
norm_threshold=norm_threshold,
zintensity_threshold=3,
parameter_source='NiPy',
bound_by_brainmask=True,
save_plot=False,
mask_type='file'),
name="art")
if fieldmap_images:
wf.connect(dewarper, 'unwarped_file', art, 'realigned_files')
else:
wf.connect(tsnr, 'detrended_file', art, 'realigned_files')
wf.connect(realign, 'par_file', art, 'realignment_parameters')
wf.connect(masktransform, 'transformed_file', art, 'mask_file')
# Compute motion regressors
motreg = Node(Function(
input_names=['motion_params', 'order', 'derivatives'],
output_names=['out_files'],
function=motion_regressors,
imports=imports),
name='getmotionregress')
wf.connect(realign, 'par_file', motreg, 'motion_params')
# Create a filter to remove motion and art confounds
createfilter1 = Node(Function(
input_names=['motion_params', 'comp_norm', 'outliers'],
output_names=['out_files'],
function=build_filter1,
imports=imports),
name='makemotionbasedfilter')
wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
wf.connect(art, 'outlier_files', createfilter1, 'outliers')
# Filter the motion and art confounds
filter1 = MapNode(fsl.GLM(out_res_name='timeseries.nii.gz', demean=True),
iterfield=['in_file', 'design'],
name='filtermotion')
if fieldmap_images:
wf.connect(dewarper, 'unwarped_file', filter1, 'in_file')
else:
wf.connect(tsnr, 'detrended_file', filter1, 'in_file')
wf.connect(createfilter1, 'out_files', filter1, 'design')
wf.connect(masktransform, 'transformed_file', filter1, 'mask')
# Create a filter to remove noise components based on white matter and CSF
createfilter2 = MapNode(Function(
input_names=['realigned_file', 'mask_file', 'num_components'],
output_names=['out_files'],
function=extract_noise_components,
imports=imports),
iterfield=['realigned_file'],
name='makecompcorrfilter')
createfilter2.inputs.num_components = num_components
wf.connect(filter1, 'out_res', createfilter2, 'realigned_file')
wf.connect(masktransform, 'transformed_file', createfilter2, 'mask_file')
# Filter noise components
filter2 = MapNode(fsl.GLM(out_res_name='timeseries_cleaned.nii.gz',
demean=True),
iterfield=['in_file', 'design'],
name='filtercompcorr')
wf.connect(filter1, 'out_res', filter2, 'in_file')
wf.connect(createfilter2, 'out_files', filter2, 'design')
wf.connect(masktransform, 'transformed_file', filter2, 'mask')
# Smoothing using surface and volume smoothing
smooth = MapNode(freesurfer.Smooth(), iterfield=['in_file'], name='smooth')
smooth.inputs.proj_frac_avg = (0.1, 0.9, 0.1)
if surf_fwhm is None:
surf_fwhm = 5 * slice_thickness
smooth.inputs.surface_fwhm = surf_fwhm
if vol_fwhm is None:
vol_fwhm = 2 * slice_thickness
smooth.inputs.vol_fwhm = vol_fwhm
wf.connect(filter2, 'out_res', smooth, 'in_file')
wf.connect(register, 'out_reg_file', smooth, 'reg_file')
# Bandpass filter the data
bandpass = MapNode(fsl.TemporalFilter(),
iterfield=['in_file'],
name='bandpassfilter')
if highpass_freq < 0:
bandpass.inputs.highpass_sigma = -1
else:
bandpass.inputs.highpass_sigma = 1. / (2 * TR * highpass_freq)
if lowpass_freq < 0:
bandpass.inputs.lowpass_sigma = -1
else:
bandpass.inputs.lowpass_sigma = 1. / (2 * TR * lowpass_freq)
wf.connect(smooth, 'smoothed_file', bandpass, 'in_file')
# Convert aparc to subject functional space
aparctransform = wmcsftransform.clone("aparctransform")
if fieldmap_images:
wf.connect(fieldmap, 'exf_mask', aparctransform, 'source_file')
else:
wf.connect(calc_median, 'median_file', aparctransform, 'source_file')
wf.connect(register, 'out_reg_file', aparctransform, 'reg_file')
wf.connect(fssource, ('aparc_aseg', get_aparc_aseg), aparctransform,
'target_file')
# Sample the average time series in aparc ROIs
sampleaparc = MapNode(freesurfer.SegStats(avgwf_txt_file=True,
default_color_table=True),
iterfield=['in_file'],
name='aparc_ts')
sampleaparc.inputs.segment_id = ([8] + range(10, 14) + [17, 18, 26, 47] +
range(49, 55) + [58] + range(1001, 1036) +
range(2001, 2036))
wf.connect(aparctransform, 'transformed_file', sampleaparc,
'segmentation_file')
wf.connect(bandpass, 'out_file', sampleaparc, 'in_file')
# Sample the time series onto the surface of the target surface. Performs
# sampling into left and right hemisphere
target = Node(IdentityInterface(fields=['target_subject']), name='target')
target.iterables = ('target_subject', filename_to_list(target_subject))
samplerlh = MapNode(freesurfer.SampleToSurface(),
iterfield=['source_file'],
name='sampler_lh')
samplerlh.inputs.sampling_method = "average"
samplerlh.inputs.sampling_range = (0.1, 0.9, 0.1)
samplerlh.inputs.sampling_units = "frac"
samplerlh.inputs.interp_method = "trilinear"
#samplerlh.inputs.cortex_mask = True
samplerlh.inputs.out_type = 'niigz'
samplerlh.inputs.subjects_dir = os.environ['SUBJECTS_DIR']
samplerrh = samplerlh.clone('sampler_rh')
samplerlh.inputs.hemi = 'lh'
wf.connect(bandpass, 'out_file', samplerlh, 'source_file')
wf.connect(register, 'out_reg_file', samplerlh, 'reg_file')
wf.connect(target, 'target_subject', samplerlh, 'target_subject')
samplerrh.set_input('hemi', 'rh')
wf.connect(bandpass, 'out_file', samplerrh, 'source_file')
wf.connect(register, 'out_reg_file', samplerrh, 'reg_file')
wf.connect(target, 'target_subject', samplerrh, 'target_subject')
# Combine left and right hemisphere to text file
combiner = MapNode(Function(input_names=['left', 'right'],
output_names=['out_file'],
function=combine_hemi,
imports=imports),
iterfield=['left', 'right'],
name="combiner")
wf.connect(samplerlh, 'out_file', combiner, 'left')
wf.connect(samplerrh, 'out_file', combiner, 'right')
# 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 = ['Translation', 'Rigid', 'Affine', 'SyN']
reg.inputs.transform_parameters = [(0.1, ), (0.1, ), (0.1, ),
(0.2, 3.0, 0.0)]
# reg.inputs.number_of_iterations = ([[10000, 111110, 11110]]*3 +
# [[100, 50, 30]])
reg.inputs.number_of_iterations = [[100, 100, 100]] * 3 + [[100, 20, 10]]
reg.inputs.dimension = 3
reg.inputs.write_composite_transform = True
reg.inputs.collapse_output_transforms = False
reg.inputs.metric = ['Mattes'] * 3 + [['Mattes', 'CC']]
reg.inputs.metric_weight = [1] * 3 + [[0.5, 0.5]]
reg.inputs.radius_or_number_of_bins = [32] * 3 + [[32, 4]]
reg.inputs.sampling_strategy = ['Regular'] * 3 + [[None, None]]
reg.inputs.sampling_percentage = [0.3] * 3 + [[None, None]]
reg.inputs.convergence_threshold = [1.e-8] * 3 + [-0.01]
reg.inputs.convergence_window_size = [20] * 3 + [5]
reg.inputs.smoothing_sigmas = [[4, 2, 1]] * 3 + [[1, 0.5, 0]]
reg.inputs.sigma_units = ['vox'] * 4
reg.inputs.shrink_factors = [[6, 4, 2]] + [[3, 2, 1]] * 2 + [[4, 2, 1]]
reg.inputs.use_estimate_learning_rate_once = [True] * 4
reg.inputs.use_histogram_matching = [False] * 3 + [True]
reg.inputs.output_warped_image = 'output_warped_image.nii.gz'
reg.inputs.fixed_image = \
os.path.abspath('OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz')
reg.inputs.num_threads = 4
reg.plugin_args = {'qsub_args': '-l nodes=1:ppn=4'}
# Convert T1.mgz to nifti for using with ANTS
convert = Node(freesurfer.MRIConvert(out_type='niigz'), name='convert2nii')
wf.connect(fssource, 'T1', convert, 'in_file')
# Mask the T1.mgz file with the brain mask computed earlier
maskT1 = Node(fsl.BinaryMaths(operation='mul'), name='maskT1')
wf.connect(mask, 'binary_file', maskT1, 'operand_file')
wf.connect(convert, 'out_file', maskT1, 'in_file')
wf.connect(maskT1, 'out_file', reg, 'moving_image')
# Convert the BBRegister transformation to ANTS ITK format
convert2itk = MapNode(C3dAffineTool(),
iterfield=['transform_file', 'source_file'],
name='convert2itk')
convert2itk.inputs.fsl2ras = True
convert2itk.inputs.itk_transform = True
wf.connect(register, 'out_fsl_file', convert2itk, 'transform_file')
if fieldmap_images:
wf.connect(fieldmap, 'exf_mask', convert2itk, 'source_file')
else:
wf.connect(calc_median, 'median_file', convert2itk, 'source_file')
wf.connect(convert, 'out_file', convert2itk, 'reference_file')
# Concatenate the affine and ants transforms into a list
pickfirst = lambda x: x[0]
merge = MapNode(Merge(2), iterfield=['in2'], name='mergexfm')
wf.connect(convert2itk, 'itk_transform', merge, 'in2')
wf.connect(reg, ('composite_transform', pickfirst), merge, 'in1')
# Apply the combined transform to the time series file
sample2mni = MapNode(ants.ApplyTransforms(),
iterfield=['input_image', 'transforms'],
name='sample2mni')
sample2mni.inputs.input_image_type = 3
sample2mni.inputs.interpolation = 'BSpline'
sample2mni.inputs.invert_transform_flags = [False, False]
sample2mni.inputs.reference_image = \
os.path.abspath('OASIS-30_Atropos_template_in_MNI152_2mm.nii.gz')
sample2mni.inputs.terminal_output = 'file'
wf.connect(bandpass, 'out_file', sample2mni, 'input_image')
wf.connect(merge, 'out', sample2mni, 'transforms')
# Sample the time series file for each subcortical roi
ts2txt = MapNode(Function(
input_names=['timeseries_file', 'label_file', 'indices'],
output_names=['out_file'],
function=extract_subrois,
imports=imports),
iterfield=['timeseries_file'],
name='getsubcortts')
ts2txt.inputs.indices = [8] + range(10, 14) + [17, 18, 26, 47] +\
range(49, 55) + [58]
ts2txt.inputs.label_file = \
os.path.abspath(('OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_'
'2mm.nii.gz'))
wf.connect(sample2mni, 'output_image', ts2txt, 'timeseries_file')
# Save the relevant data into an output directory
datasink = Node(interface=DataSink(), name="datasink")
datasink.inputs.base_directory = sink_directory
datasink.inputs.container = subject_id
datasink.inputs.substitutions = [('_target_subject_', '')]
datasink.inputs.regexp_substitutions = (r'(/_.*(\d+/))', r'/run\2')
wf.connect(despiker, 'out_file', datasink, 'resting.qa.despike')
wf.connect(realign, 'par_file', datasink, 'resting.qa.motion')
wf.connect(tsnr, 'tsnr_file', datasink, 'resting.qa.tsnr')
wf.connect(tsnr, 'mean_file', datasink, 'resting.qa.tsnr.@mean')
wf.connect(tsnr, 'stddev_file', datasink, 'resting.qa.@tsnr_stddev')
if fieldmap_images:
wf.connect(fieldmap, 'exf_mask', datasink, 'resting.reference')
else:
wf.connect(calc_median, 'median_file', datasink, 'resting.reference')
wf.connect(art, 'norm_files', datasink, 'resting.qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'resting.qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'resting.qa.art.@outlier_files')
wf.connect(mask, 'binary_file', datasink, 'resting.mask')
wf.connect(masktransform, 'transformed_file', datasink,
'resting.mask.@transformed_file')
wf.connect(register, 'out_reg_file', datasink,
'resting.registration.bbreg')
wf.connect(reg, ('composite_transform', pickfirst), datasink,
'resting.registration.ants')
wf.connect(register, 'min_cost_file', datasink,
'resting.qa.bbreg.@mincost')
wf.connect(smooth, 'smoothed_file', datasink,
'resting.timeseries.fullpass')
wf.connect(bandpass, 'out_file', datasink, 'resting.timeseries.bandpassed')
wf.connect(sample2mni, 'output_image', datasink, 'resting.timeseries.mni')
wf.connect(createfilter1, 'out_files', datasink,
'resting.regress.@regressors')
wf.connect(createfilter2, 'out_files', datasink,
'resting.regress.@compcorr')
wf.connect(sampleaparc, 'summary_file', datasink,
'resting.parcellations.aparc')
wf.connect(sampleaparc, 'avgwf_txt_file', datasink,
'resting.parcellations.aparc.@avgwf')
wf.connect(ts2txt, 'out_file', datasink,
'resting.parcellations.grayo.@subcortical')
datasink2 = Node(interface=DataSink(), name="datasink2")
datasink2.inputs.base_directory = sink_directory
datasink2.inputs.container = subject_id
datasink2.inputs.substitutions = [('_target_subject_', '')]
datasink2.inputs.regexp_substitutions = (r'(/_.*(\d+/))', r'/run\2')
wf.connect(combiner, 'out_file', datasink2,
'resting.parcellations.grayo.@surface')
return wf
# Slicetiming - correct for slice wise acquisition
interleaved_order = range(1,number_of_slices+1,2) + range(2,number_of_slices+1,2)
sliceTiming = Node(SliceTiming(num_slices=number_of_slices,
time_repetition=TR,
time_acquisition=TR-TR/number_of_slices,
slice_order=interleaved_order,
ref_slice=2),
name="sliceTiming")
# Realign - correct for motion
realign = Node(Realign(register_to_mean=True),
name="realign")
# TSNR - remove polynomials 2nd order
tsnr = MapNode(TSNR(regress_poly=2),
name='tsnr', iterfield=['in_file'])
# Artifact Detection - determine which of the images in the functional series
# are outliers. This is based on deviation in intensity or movement.
art = Node(ArtifactDetect(norm_threshold=1,
zintensity_threshold=3,
mask_type='file',
parameter_source='SPM',
use_differences=[True, False]),
name="art")
# Smooth - to smooth the images with a given kernel
smooth = Node(Smooth(fwhm=fwhm_size),
name="smooth")
def create_resting_preproc(name='restpreproc'):
"""Create a "resting" time series preprocessing workflow
The noise removal is based on Behzadi et al. (2007)
Parameters
----------
name : name of workflow (default: restpreproc)
Inputs::
inputspec.func : functional run (filename or list of filenames)
Outputs::
outputspec.noise_mask_file : voxels used for PCA to derive noise components
outputspec.filtered_file : bandpass filtered and noise-reduced time series
Example
-------
>>> TR = 3.0
>>> wf = create_resting_preproc()
>>> wf.inputs.inputspec.func = 'f3.nii'
>>> wf.inputs.inputspec.num_noise_components = 6
>>> wf.inputs.inputspec.highpass_sigma = 100/(2*TR)
>>> wf.inputs.inputspec.lowpass_sigma = 12.5/(2*TR)
>>> wf.run() # doctest: +SKIP
"""
restpreproc = pe.Workflow(name=name)
# Define nodes
inputnode = pe.Node(interface=util.IdentityInterface(fields=[
'func', 'num_noise_components', 'highpass_sigma', 'lowpass_sigma'
]),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'noise_mask_file', 'filtered_file', 'motion_rms_files',
'motion_par_file', 'realigned_file', 'mask_file', 'outlier_files',
'intensity_files', 'outlier_plots'
]),
name='outputspec')
slicetimer = pe.Node(fsl.SliceTimer(), name='slicetimer')
realigner = create_realign_flow()
art_detector = pe.Node(ArtifactDetect(), name='art_detector')
art_detector.inputs.parameter_source = 'FSL'
art_detector.inputs.mask_type = 'spm_global'
art_detector.inputs.global_threshold = .5
art_detector.inputs.norm_threshold = .6
art_detector.inputs.use_differences = [True,
True] ## [Movement, Intensity]
art_detector.inputs.zintensity_threshold = 3
art_detector.inputs.intersect_mask = True
'''Mask smoother node, added by Pablo Polosecki to use EPI mask'''
mask_smoother = pe.Node(util.Function(input_names=['vol_in'],
output_names=['out_vol'],
function=morph_open_close),
name='mask_smoother')
tsnr = pe.Node(TSNR(regress_poly=2), name='tsnr')
getthresh = pe.Node(interface=fsl.ImageStats(op_string='-k %s -p 98'),
name='getthreshold')
threshold_stddev = pe.Node(fsl.Threshold(), name='threshold')
''' Mask conjunction, to limit noisy voxels to those inside brain mask'''
conj_masker = pe.Node(fsl.BinaryMaths(operation='mul'), name='conj_masker')
compcor = pe.Node(util.Function(
input_names=['realigned_file', 'noise_mask_file', 'num_components'],
output_names=['noise_components'],
function=extract_noise_components),
name='compcorr')
# cat_regressors = pe.Node(util.Function(input_names=['file1',
# 'file2'],
# output_names=['out_fn'],
# function=concatetante_reg_files),
# name='cat_regressors')
remove_noise = pe.Node(fsl.FilterRegressor(filter_all=True),
name='remove_noise')
bandpass_filter = pe.Node(fsl.TemporalFilter(), name='bandpass_filter')
# Define connections
restpreproc.connect(inputnode, 'func', slicetimer, 'in_file')
restpreproc.connect(slicetimer, 'slice_time_corrected_file', realigner,
'inputspec.func')
restpreproc.connect(realigner, 'outputspec.realigned_file', tsnr,
'in_file')
restpreproc.connect(tsnr, 'stddev_file', threshold_stddev, 'in_file')
restpreproc.connect(tsnr, 'stddev_file', getthresh, 'in_file')
restpreproc.connect(mask_smoother, 'out_vol', getthresh, 'mask_file')
restpreproc.connect(getthresh, 'out_stat', threshold_stddev, 'thresh')
restpreproc.connect(realigner, 'outputspec.realigned_file', compcor,
'realigned_file')
restpreproc.connect(inputnode, 'num_noise_components', compcor,
'num_components')
restpreproc.connect(tsnr, 'detrended_file', remove_noise, 'in_file')
# Combiinng compcorr with motion regressors:
#restpreproc.connect(compcor, 'noise_components',
# cat_regressors, 'file1')
#restpreproc.connect(realigner, 'outputspec.par_file',
# cat_regressors, 'file2')
#restpreproc.connect(cat_regressors, 'out_fn',
# remove_noise, 'design_file')
restpreproc.connect(compcor, 'noise_components', remove_noise,
'design_file')
restpreproc.connect(inputnode, 'highpass_sigma', bandpass_filter,
'highpass_sigma')
restpreproc.connect(inputnode, 'lowpass_sigma', bandpass_filter,
'lowpass_sigma')
restpreproc.connect(remove_noise, 'out_file', bandpass_filter, 'in_file')
restpreproc.connect(conj_masker, 'out_file', outputnode, 'noise_mask_file')
restpreproc.connect(bandpass_filter, 'out_file', outputnode,
'filtered_file')
restpreproc.connect(realigner, 'outputspec.rms_files', outputnode,
'motion_rms_files')
restpreproc.connect(realigner, 'outputspec.par_file', outputnode,
'motion_par_file')
restpreproc.connect(realigner, 'outputspec.realigned_file', outputnode,
'realigned_file')
restpreproc.connect(realigner, 'outputspec.realigned_file', art_detector,
'realigned_files')
restpreproc.connect(realigner, 'outputspec.par_file', art_detector,
'realignment_parameters')
restpreproc.connect(art_detector, 'mask_files', mask_smoother, 'vol_in')
restpreproc.connect(mask_smoother, 'out_vol', outputnode, 'mask_file')
restpreproc.connect(art_detector, 'outlier_files', outputnode,
'outlier_files')
restpreproc.connect(art_detector, 'intensity_files', outputnode,
'intensity_files')
#restpreproc.connect(art_detector, 'plot_files',
# outputnode, 'outlier_plots')
restpreproc.connect(mask_smoother, 'out_vol', conj_masker, 'in_file')
restpreproc.connect(threshold_stddev, 'out_file', conj_masker,
'operand_file')
restpreproc.connect(conj_masker, 'out_file', compcor, 'noise_mask_file')
return restpreproc
def create_compcorr(name='CompCor'):
"""Workflow that implements (t and/or a) compcor method from
Behzadi et al[1]_.
Parameters
----------
name : name of workflow. Default = 'CompCor'
Inputs
------
inputspec.num_components :
inputspec.realigned_file :
inputspec.in_file :
inputspec.reg_file :
inputspec.fsaseg_file :
inputspec.selector :
Outputs
-------
outputspec.noise_components :
outputspec.stddev_file :
outputspec.tsnr_file :
outputspec.csf_mask :
References
----------
.. [1] Behzadi Y, Restom K, Liau J, Liu TT. A component based\
noise correction method (CompCor) for BOLD and perfusion\
based fMRI. Neuroimage. 2007 Aug 1;37(1):90-101. DOI_.
.. _DOI: http://dx.doi.org/10.1016/j.neuroimage.2007.04.042
"""
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from nipype.algorithms.misc import TSNR
import nipype.interfaces.fsl as fsl
compproc = pe.Workflow(name=name)
inputspec = pe.Node(util.IdentityInterface(fields=[
'num_components', 'realigned_file', 'mean_file', 'reg_file',
'fsaseg_file', 'realignment_parameters', 'outlier_files', 'selector',
'regress_before_PCA'
]),
name='inputspec')
# selector input is bool list [True,True] where first is referring to
# tCompcorr and second refers to aCompcorr
outputspec = pe.Node(util.IdentityInterface(fields=[
'noise_components', 'stddev_file', 'tsnr_file', 'csf_mask',
'noise_mask', 'tsnr_detrended', 'pre_svd'
]),
name='outputspec')
# extract the principal components of the noise
tsnr = pe.MapNode(
TSNR(regress_poly=2), #SG: advanced parameter
name='tsnr',
iterfield=['in_file'])
# additional information for the noise prin comps
getthresh = pe.MapNode(interface=fsl.ImageStats(op_string='-p 98'),
name='getthreshold',
iterfield=['in_file'])
# and a bit more...
threshold_stddev = pe.MapNode(fsl.Threshold(),
name='threshold',
iterfield=['in_file', 'thresh'])
acomp = extract_csf_mask()
# compcor actually extracts the components
compcor = pe.MapNode(
util.Function(input_names=[
'realigned_file', 'noise_mask_file', 'num_components',
'csf_mask_file', 'realignment_parameters', 'outlier_file',
'selector', 'regress_before_PCA'
],
output_names=['noise_components', 'pre_svd'],
function=extract_noise_components),
name='compcor_components',
iterfield=[
'realigned_file', 'noise_mask_file', 'realignment_parameters',
'outlier_file'
])
# Make connections
compproc.connect(inputspec, 'mean_file', acomp, 'inputspec.mean_file')
compproc.connect(inputspec, 'reg_file', acomp, 'inputspec.reg_file')
compproc.connect(inputspec, 'fsaseg_file', acomp, 'inputspec.fsaseg_file')
compproc.connect(inputspec, 'selector', compcor, 'selector')
compproc.connect(acomp, ('outputspec.csf_mask', pickfirst), compcor,
'csf_mask_file')
compproc.connect(acomp, ('outputspec.csf_mask', pickfirst), outputspec,
'csf_mask')
compproc.connect(inputspec, 'realigned_file', tsnr, 'in_file')
compproc.connect(inputspec, 'num_components', compcor, 'num_components')
compproc.connect(inputspec, 'realignment_parameters', compcor,
'realignment_parameters')
compproc.connect(inputspec, 'outlier_files', compcor, 'outlier_file')
compproc.connect(getthresh, 'out_stat', threshold_stddev, 'thresh')
compproc.connect(threshold_stddev, 'out_file', compcor, 'noise_mask_file')
compproc.connect(threshold_stddev, 'out_file', outputspec, 'noise_mask')
compproc.connect(tsnr, 'stddev_file', threshold_stddev, 'in_file')
compproc.connect(tsnr, 'stddev_file', getthresh, 'in_file')
compproc.connect(tsnr, 'stddev_file', outputspec, 'stddev_file')
compproc.connect(tsnr, 'tsnr_file', outputspec, 'tsnr_file')
compproc.connect(tsnr, 'detrended_file', outputspec, 'tsnr_detrended')
compproc.connect(tsnr, 'detrended_file', compcor, 'realigned_file')
compproc.connect(compcor, 'noise_components', outputspec,
'noise_components')
compproc.connect(compcor, 'pre_svd', outputspec, 'pre_svd')
compproc.connect(inputspec, 'regress_before_PCA', compcor,
'regress_before_PCA')
return compproc
def make_quality_control_reports(population, workspace_dir):
for subject in population:
print '###############################################################################'
print ' Running Quality Control for subject %s' %subject
print ''
#input
subject_dir = os.path.join(workspace_dir, subject)
func_native = os.path.join(subject_dir, 'FUNC_PPROC/REST_PPROC_NATIVE_BRAIN.nii.gz')
func_native_mask = os.path.join(subject_dir, 'FUNC_PPROC/REST_PPROC_NATIVE_BRAIN_mask_ero.nii.gz')
func_native_mean = os.path.join(subject_dir, 'FUNC_PPROC/REST_PPROC_NATIVE_BRAIN_mean.nii.gz')
func_native_gm = os.path.join(subject_dir, 'FUNC_TRANSFORM/NATIVE_FUNC_GM.nii.gz')
func_native_wm = os.path.join(subject_dir, 'FUNC_TRANSFORM/NATIVE_FUNC_WM.nii.gz')
func_native_csf = os.path.join(subject_dir, 'FUNC_TRANSFORM/NATIVE_FUNC_CSF.nii.gz')
func_native_first = os.path.join(subject_dir, 'FUNC_TRANSFORM/NATIVE_FUNC_FIRST.nii.gz')
func_mni = os.path.join(subject_dir, 'FUNC_TRANSFORM/REST_PPROC_MNI2mm_BRAIN.nii.gz')
func_mni_mask = os.path.join(subject_dir, 'FUNC_TRANSFORM/REST_PPROC_MNI2mm_BRAIN_mask_ero.nii.gz')
func_mni_mean = os.path.join(subject_dir, 'FUNC_TRANSFORM/REST_PPROC_MNI2mm_BRAIN_mean.nii.gz')
func_mni_gm = os.path.join(subject_dir, 'FUNC_TRANSFORM/MNI2mm_FUNC_GM.nii.gz')
func_mni_wm = os.path.join(subject_dir, 'FUNC_TRANSFORM/MNI2mm_FUNC_WM.nii.gz')
func_mni_csf = os.path.join(subject_dir, 'FUNC_TRANSFORM/MNI2mm_FUNC_CSF.nii.gz')
residual_compor = os.path.join(subject_dir, 'FUNC_DENOISE/NUISANCE_MNI_COMPCOR/residual.nii.gz')
residual_wmcsf = os.path.join(subject_dir, 'FUNC_DENOISE/NUISANCE_MNI_WMCSF/residual.nii.gz')
residual_global = os.path.join(subject_dir, 'FUNC_DENOISE/NUISANCE_MNI_GLOBAL/residual.nii.gz')
aroma_compcor = os.path.join(subject_dir, 'FUNC_DENOISE/NUISANCE_MNI_AROMA_COMPCOR/residual.nii.gz')
aroma_wmcsf = os.path.join(subject_dir, 'FUNC_DENOISE/NUISANCE_MNI_AROMA_WMCSF/residual.nii.gz')
aroma_global = os.path.join(subject_dir, 'FUNC_DENOISE/NUISANCE_MNI_AROMA_GLOBAL/residual.nii.gz')
movpar = os.path.join(subject_dir,'FUNC_PPROC/MOTION_CORRECTION/REST_DISCO_MOCO_2.1D')
qcdir = os.path.join(subject_dir,'QUALITY_CONTROL')
mkdir_path(qcdir)
os.chdir(qcdir)
print 'Creating Quality Control Report'
################################################################################################################
print '1. Calculating FD'
fd = os.path.join(qcdir, 'FD.1D')
if not os.path.isfile(fd):
calc_FD_power(movpar)
population_FDs = np.genfromtxt(os.path.join(workspace_dir, 'population_fd_distributions.txt'))
if not os.path.isfile('plot_fd_qc.png'):
plot_FD(fd, population_FDs, subject,figsize = (8.3,8.3))
################################################################################################################
print '2. Grabbing accepted frames (FD<0.2mm) '
fd1d = np.loadtxt(fd)
in_frames = []
for frame, fd in enumerate(fd1d):
if fd < 0.2:
in_frames.append(frame)
print ' ...Subject has %s of 417 good frames'%len(in_frames)
np.save('in_frames', str(in_frames).replace(" ",""))
if len(in_frames) > 150:
in_frames_100 = in_frames[0:130]
np.save('in_frames_100', str(in_frames_100).replace(" ",""))
################################################################################################################
print '3. Calculating DVARS'
dvars = os.path.join(qcdir, 'DVARS.npy')
if not os.path.isfile(dvars):
calc_DVARS(func_native, func_native_mask)
print func_native
print func_native_mask
###############################################################################################################
print '4. TSNR'
#get TSNR median for whole brain
if not os.path.isfile(os.path.join(qcdir, 'REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz')):
tsnr = TSNR()
tsnr.inputs.in_file = func_native
tsnr.run()
if not os.path.isfile('TSNR_data.npy'):
tsnr_data = nb.load('./REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz').get_data()
nan_mask = np.logical_not(np.isnan(tsnr_data))
mask = nb.load(func_native_mask).get_data() > 0
#tsnr_median = np.median(tsnr_data[np.logical_and(nan_mask, mask)])
data = tsnr_data[np.logical_and(nan_mask, mask)]
tsnr_out = os.path.join(os.getcwd(), 'TSNR_data.npy')
np.save(tsnr_out, data)
tsnr_median = np.median(np.load('TSNR_data.npy'))
print '-->',tsnr_median
if not os.path.isfile(' plot_tsnr_mosaic.png'):
plot_mosaic(nifti_file = './REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz',
output_name = 'plot_tsnr_mosaic.pdf',
title="tSNR",
overlay_mask = None,
figsize=(8.3, 11.7))
os.system('convert -density 300 -trim plot_tsnr_mosaic.pdf -quality 300 -sharpen 0x1.0 -crop 2506x2570+1+470 plot_tsnr_mosaic.png')
# whole brain plots
if not os.path.isfile('plot_tsnr_brain_distribution.png'):
plot_distrbution_of_values(main_file = './REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz',
mask_file = func_native_mask,
xlabel = "%s Whole Brain tSNR distribution" % subject,
distribution= np.genfromtxt(os.path.join(workspace_dir, 'population_tsnr_distributions.txt')),
xlabel2= "%s median whole brain tSNR with respect to all subjects"%subject,
figsize=(11.7,8.3),
outname = 'plot_tsnr_brain_distribution.png')
d = plotting.plot_epi(func_native_mean, cmap='gray', display_mode='y', black_bg= 1, annotate=0)
d.add_contours(func_native_mask, colors='r')
d.savefig('plot_func_native_mask_y.png', dpi = 130)
d = plotting.plot_epi(func_native_mean, cmap='gray', display_mode='x', black_bg= 1, annotate=0)
d.add_contours(func_native_mask, colors='r')
d.savefig('plot_func_native_mask_x.png', dpi = 110)
d = plotting.plot_epi(func_native_mean, cmap='gray', display_mode='z', black_bg= 1, annotate=0)
d.add_contours(func_native_mask, colors='r')
d.savefig('plot_func_native_mask_z.png', dpi = 130)
# subcortical plots
if not os.path.isfile('plot_tsnr_first_distribution.png'):
plot_distrbution_of_values(main_file = './REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz',
mask_file = func_native_first,
xlabel = "%s Subcortical tSNR distribution" % subject,
distribution = np.genfromtxt(os.path.join(workspace_dir, 'population_tsnr_first_distributions.txt')),
xlabel2 = "%s median subcortical tSNR with respect to all subjects"%subject,
figsize =(11.7,8.3),
outname = 'plot_tsnr_first_distribution.png',
color = 'green')
plot_3d_overlay(underlay_file=func_native_mean, overlay_file=func_native_first, out_filename='plot_subcortical_mask.png', dpi = 215)
################################################################################################################
print '5. Plot FUNC 2 MNI Registration'
d = plotting.plot_epi( mni_brain_2mm, cmap='gray', display_mode='x', annotate=0, black_bg=0)
d.add_contours(func_mni_mean, colors='r')
d.savefig('plot_mnireg_x.png', dpi = 110)
d = plotting.plot_epi( mni_brain_2mm, cmap='gray', display_mode='y', annotate=0, black_bg=0)
d.add_contours(func_mni_mean, colors='r')
d.savefig('plot_mnireg_y.png', dpi = 130)
d = plotting.plot_epi( mni_brain_2mm, cmap='gray', display_mode='z', annotate=0, black_bg=1)
d.add_contours(func_mni_mean, colors='r')
d.savefig('plot_mnireg_z.png', dpi = 130)
################################################################################################################
print '6. Plot Func GM'
if not os.path.isfile('plot_func_native_gm.png'):
plot_mosaic(nifti_file = func_native_mean,
output_name = 'plot_func_native_gm.pdf',
title="Func Gray Matter",
overlay_mask = func_native_gm,
figsize=(8.3, 11.7))
os.system('convert -density 300 -trim plot_func_native_gm.pdf -quality 300 -sharpen 0x1.0 -crop 2506x2570+1+470 plot_func_native_gm.png')
################################################################################################################
print '7. Plot Nuisance Residuals'
mni_wm_sig = os.path.join(subject_dir, 'FUNC_DENOISE/TISSUE_SIGNALS_MNI/NUISANCE_SIGNALS_WM.npy')
mni_gm_sig = os.path.join(subject_dir, 'FUNC_DENOISE/TISSUE_SIGNALS_MNI/NUISANCE_SIGNALS_GM.npy')
mni_csf_sig = os.path.join(subject_dir, 'FUNC_DENOISE/TISSUE_SIGNALS_MNI/NUISANCE_SIGNALS_CSF.npy')
if not os.path.isfile('func_mni_detrend.nii.gz'):
calc_residuals(subject = func_mni, selector = nuisance_detrend,wm_sig_file = mni_wm_sig,
csf_sig_file= mni_csf_sig, gm_sig_file = mni_gm_sig,motion_file = movpar,
compcor_ncomponents = 0)
os.system('mv residual.nii.gz func_mni_detrend.nii.gz')
os.system('rm -rf quadratic_constant_linear.csv nuisance_regressors.mat')
if not os.path.isfile('plot_nuisance.png'):
plot_nuisance_residuals(mov_params = movpar,
fd1d = fd1d,
func_preprocessed = func_native,
func_preprocessed_mask = func_native_mask,
dvars = dvars,
func_gm = func_mni_gm,
residuals_dt = 'func_mni_detrend.nii.gz',
residuals_cc = residual_compor ,
residuals_gl = residual_global,
aroma_cc = aroma_compcor ,
aroma_gl = aroma_global,
out_name = 'plot_nuisance.png',
figsize = (8.3,8.3))
################################################################################################################
print '8. Calculating Motion statistics and saving as csv'
df = pd.DataFrame(index = [subject], columns = ['FD', 'FD_in','FD_topQua', 'FD_max', 'FD_RMS', 'DVARS', 'TSNR'])
df.loc[subject]['FD'] = str(np.round(np.mean(fd1d), 3))
df.loc[subject]['FD_in'] = str(np.round(len(in_frames), 3))
quat = int(len(fd1d)/4)
df.loc[subject]['FD_topQua'] = str(np.round(np.mean(np.sort(fd1d)[::-1][:quat]), 3))
df.loc[subject]['FD_max'] = str(np.round(np.max(fd1d), 3))
df.loc[subject]['FD_RMS'] = str(np.round(np.sqrt(np.mean(fd1d)), 3))
df.loc[subject]['DVARS'] = str(np.round(np.mean(np.load(dvars)), 3))
df.loc[subject]['TSNR'] = str(np.round(tsnr_median, 3))
df.to_csv ('quality_paramters.csv')
################################################################################################################
print 'Creating QC REPORT'
report = canvas.Canvas('QC_REPORT.pdf', pagesize=(1280 *1.9, 1556*1.9))
report.setFont("Helvetica", 100)
report.drawString(inch*15, inch*39.3, '%s'%subject)
report.setFont("Helvetica", 70)
report.drawString(inch*15, inch*1, 'tSNR')
report.drawImage('plot_tsnr_mosaic.png', inch*1.5, inch*2.6)
report.showPage()
report.setFont("Helvetica", 30)
report.drawString(inch*31, inch*40, '%s'%subject)
report.setFont("Helvetica", 70)
report.drawString(inch*12, inch*1, 'Func Native Gray Matter')
report.drawImage('plot_func_native_gm.png', inch*1.5, inch*2.6)
report.showPage()
report.setFont("Helvetica", 30)
report.drawString(inch*31, inch*40, '%s'%subject)
report.setFont("Helvetica", 50)
report.drawString(inch*10, inch*38.5, 'Func to MNI2mm Nonlinear Registration')
report.drawImage('plot_mnireg_x.png', inch*2.5, inch*34.5)
report.drawImage('plot_mnireg_y.png', inch*2.5, inch*30.5)
report.setFont("Helvetica", 30)
report.drawString(inch*31, inch*40, '%s'%subject)
report.setFont("Helvetica", 50)
report.drawString(inch*13, inch*29, 'Func Native Brain Mask')
report.drawImage('plot_func_native_mask_y.png', inch*2.5, inch*21)
report.drawImage('plot_func_native_mask_x.png', inch*2.5, inch*25)
report.drawImage('plot_tsnr_brain_distribution.png', inch*3.5, inch*1.7)
brain_tsnr = get_values_inside_a_mask('REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz', func_native_mask)
tsnr_brain_check_outlier = get_outlier(brain_tsnr, np.genfromtxt(os.path.join(workspace_dir, 'population_tsnr_distributions.txt')) )
if tsnr_brain_check_outlier is 'Accepted':
report.setFillColorRGB(0,180,0)
else:
report.setFillColorRGB(250,0,0)
report.setFont("Helvetica", 50)
report.drawString(inch*6, inch*10, tsnr_brain_check_outlier)
report.showPage()
report.setFont("Helvetica", 30)
report.drawString(inch*31, inch*40, '%s'%subject)
report.setFont("Helvetica", 50)
report.drawString(inch*12, inch*38.5, 'Func FIRST Subcortical Mask')
report.drawImage('plot_subcortical_mask.png', inch*7, inch*20)
report.drawImage('plot_tsnr_first_distribution.png', inch*3, inch*2)
first_tsnr = get_values_inside_a_mask('REST_PPROC_NATIVE_BRAIN_tsnr.nii.gz', func_native_first)
tsnr_first_check_outlier = get_outlier(first_tsnr, np.genfromtxt(os.path.join(workspace_dir, 'population_tsnr_distributions.txt')) )
if tsnr_first_check_outlier is 'Accepted':
report.setFillColorRGB(0,180,0)
else:
report.setFillColorRGB(250,0,0)
report.setFont("Helvetica", 50)
report.drawString(inch*6, inch*10, tsnr_first_check_outlier)
report.showPage()
report.setFont("Helvetica", 30)
report.drawString(inch*31, inch*40, '%s'%subject)
report.drawImage('plot_nuisance.png', inch*3, inch*2)
report.drawImage('plot_fd_qc.png', inch*3, inch*20)
fd_check_outlier = get_outlier(fd1d, np.genfromtxt(os.path.join(workspace_dir, 'population_fd_distributions.txt')))
if fd_check_outlier is 'Accepted':
report.setFillColorRGB(0,180,0)
else:
report.setFillColorRGB(250,0,0)
report.setFont("Helvetica", 50)
report.drawString(inch*25, inch*27.5, fd_check_outlier)
report.showPage()
report.save()
def simple_preproc(c):
from .fmri_preprocessing import extract_meta
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as util
from ...scripts.modular_nodes import mod_realign
from nipype.workflows.smri.freesurfer.utils import create_getmask_flow
from ...scripts.utils import art_mean_workflow
from nipype.algorithms.misc import TSNR
import nipype.interfaces.io as nio
import nipype.interfaces.fsl as fsl
wf = pe.Workflow(name='simple_preproc')
datagrabber = c.datagrabber.create_dataflow()
infosource = datagrabber.get_node('subject_id_iterable')
img2float = pe.MapNode(interface=fsl.ImageMaths(out_data_type='float',
op_string='',
suffix='_dtype'),
iterfield=['in_file'],
name='img2float')
motion_correct = pe.Node(util.Function(
input_names=[
'node', 'in_file', 'tr', 'do_slicetime', 'sliceorder', "parameters"
],
output_names=['out_file', 'par_file', 'parameter_source'],
function=mod_realign),
name="mod_realign")
meanfunc = art_mean_workflow()
art = meanfunc.get_node('strict_artifact_detect')
getmask = create_getmask_flow()
tsnr = pe.MapNode(
TSNR(regress_poly=2), #SG: advanced parameter
name='tsnr',
iterfield=['in_file'])
if c.use_metadata:
get_meta = pe.Node(util.Function(input_names=['func'],
output_names=['so', 'tr'],
function=extract_meta),
name="get_metadata")
wf.connect(datagrabber, 'datagrabber.epi', get_meta, 'func')
wf.connect(get_meta, 'so', motion_correct, "sliceorder")
wf.connect(get_meta, 'tr', motion_correct, "tr")
else:
motion_correct.inputs.sliceorder = c.SliceOrder
motion_correct.inputs.tr = c.TR
# inputs
motion_correct.inputs.do_slicetime = c.do_slicetiming
motion_correct.inputs.node = c.motion_correct_node
motion_correct.inputs.parameters = {
"loops": c.loops,
"speedup": c.speedup,
"order": c.order
}
wf.connect(datagrabber, 'datagrabber.epi', img2float, 'in_file')
wf.connect(img2float, 'out_file', motion_correct, 'in_file')
wf.connect(motion_correct, 'out_file', meanfunc,
'inputspec.realigned_files')
wf.connect(motion_correct, 'parameter_source', meanfunc,
'inputspec.parameter_source')
wf.connect(motion_correct, 'par_file', meanfunc,
'inputspec.realignment_parameters')
wf.connect(motion_correct, 'out_file', tsnr, 'in_file')
wf.connect(meanfunc, 'outputspec.mean_image', getmask,
'inputspec.source_file')
wf.connect(infosource, 'subject_id', getmask, 'inputspec.subject_id')
getmask.inputs.inputspec.subjects_dir = c.surf_dir
getmask.inputs.inputspec.contrast_type = 't2'
sink = pe.Node(nio.DataSink(), name='sinker')
sink.inputs.base_directory = c.sink_dir
wf.connect(infosource, 'subject_id', sink, 'container')
wf.connect(infosource, ('subject_id', get_substitutions), sink,
'substitutions')
wf.connect(motion_correct, 'out_file', sink, 'simple_preproc.output')
wf.connect(motion_correct, 'par_file', sink, 'simple_preproc.motion')
wf.connect(meanfunc, 'outputspec.mean_image', sink, 'simple_preproc.mean')
wf.connect(getmask, 'outputspec.mask_file', sink, 'simple_preproc.mask')
wf.connect(getmask, 'outputspec.reg_file', sink,
'simple_preproc.bbreg.@reg')
wf.connect(getmask, 'outputspec.reg_cost', sink,
'simple_preproc.bbreg.@regcost')
wf.connect(tsnr, 'tsnr_file', sink, 'simple_preproc.tsnr.@tsnr')
wf.connect(tsnr, 'detrended_file', sink, 'simple_preproc.tsnr.@detrended')
wf.connect(tsnr, 'stddev_file', sink, 'simple_preproc.tsnr.@stddev')
wf.connect(tsnr, 'mean_file', sink, 'simple_preproc.tsnr.@mean')
wf.connect(art, 'intensity_files', sink, 'simple_preproc.art.@intensity')
wf.connect(art, 'norm_files', sink, 'simple_preproc.art.@norm')
wf.connect(art, 'outlier_files', sink, 'simple_preproc.art.@outlier')
wf.connect(art, 'statistic_files', sink, 'simple_preproc.art.@stats')
return wf
def create_workflow(files,
target_file,
subject_id,
TR,
slice_times,
norm_threshold=1,
num_components=5,
vol_fwhm=None,
surf_fwhm=None,
lowpass_freq=-1,
highpass_freq=-1,
subjects_dir=None,
sink_directory=os.getcwd(),
target_subject=['fsaverage3', 'fsaverage4'],
name='resting'):
wf = Workflow(name=name)
# Rename files in case they are named identically
name_unique = MapNode(Rename(format_string='rest_%(run)02d'),
iterfield=['in_file', 'run'],
name='rename')
name_unique.inputs.keep_ext = True
name_unique.inputs.run = list(range(1, len(files) + 1))
name_unique.inputs.in_file = files
realign = Node(interface=spm.Realign(), name="realign")
realign.inputs.jobtype = 'estwrite'
num_slices = len(slice_times)
slice_timing = Node(interface=spm.SliceTiming(), name="slice_timing")
slice_timing.inputs.num_slices = num_slices
slice_timing.inputs.time_repetition = TR
slice_timing.inputs.time_acquisition = TR - TR / float(num_slices)
slice_timing.inputs.slice_order = (np.argsort(slice_times) + 1).tolist()
slice_timing.inputs.ref_slice = int(num_slices / 2)
# Comute TSNR on realigned data regressing polynomials upto order 2
tsnr = MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(slice_timing, 'timecorrected_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')
"""Segment and Register
"""
registration = create_reg_workflow(name='registration')
wf.connect(calc_median, 'median_file', registration,
'inputspec.mean_image')
registration.inputs.inputspec.subject_id = subject_id
registration.inputs.inputspec.subjects_dir = subjects_dir
registration.inputs.inputspec.target_image = target_file
"""Use :class:`nipype.algorithms.rapidart` to determine which of the
images in the functional series are outliers based on deviations in
intensity or movement.
"""
art = Node(interface=ArtifactDetect(), name="art")
art.inputs.use_differences = [True, True]
art.inputs.use_norm = True
art.inputs.norm_threshold = norm_threshold
art.inputs.zintensity_threshold = 9
art.inputs.mask_type = 'spm_global'
art.inputs.parameter_source = 'SPM'
"""Here we are connecting all the nodes together. Notice that we add the merge node only if you choose
to use 4D. Also `get_vox_dims` function is passed along the input volume of normalise to set the optimal
voxel sizes.
"""
wf.connect([
(name_unique, realign, [('out_file', 'in_files')]),
(realign, slice_timing, [('realigned_files', 'in_files')]),
(slice_timing, art, [('timecorrected_files', 'realigned_files')]),
(realign, art, [('realignment_parameters', 'realignment_parameters')]),
])
def selectindex(files, idx):
import numpy as np
from nipype.utils.filemanip import filename_to_list, list_to_filename
return list_to_filename(
np.array(filename_to_list(files))[idx].tolist())
mask = Node(fsl.BET(), name='getmask')
mask.inputs.mask = True
wf.connect(calc_median, 'median_file', mask, 'in_file')
# get segmentation in normalized functional space
def merge_files(in1, in2):
out_files = filename_to_list(in1)
out_files.extend(filename_to_list(in2))
return out_files
# filter some noise
# Compute motion regressors
motreg = Node(Function(
input_names=['motion_params', 'order', 'derivatives'],
output_names=['out_files'],
function=motion_regressors,
imports=imports),
name='getmotionregress')
wf.connect(realign, 'realignment_parameters', motreg, 'motion_params')
# Create a filter to remove motion and art confounds
createfilter1 = Node(Function(
input_names=['motion_params', 'comp_norm', 'outliers', 'detrend_poly'],
output_names=['out_files'],
function=build_filter1,
imports=imports),
name='makemotionbasedfilter')
createfilter1.inputs.detrend_poly = 2
wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
wf.connect(art, 'outlier_files', createfilter1, 'outliers')
filter1 = MapNode(fsl.GLM(out_f_name='F_mcart.nii',
out_pf_name='pF_mcart.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filtermotion')
wf.connect(slice_timing, 'timecorrected_files', filter1, 'in_file')
wf.connect(slice_timing, ('timecorrected_files', rename, '_filtermotart'),
filter1, 'out_res_name')
wf.connect(createfilter1, 'out_files', filter1, 'design')
createfilter2 = MapNode(Function(input_names=[
'realigned_file', 'mask_file', 'num_components', 'extra_regressors'
],
output_names=['out_files'],
function=extract_noise_components,
imports=imports),
iterfield=['realigned_file', 'extra_regressors'],
name='makecompcorrfilter')
createfilter2.inputs.num_components = num_components
wf.connect(createfilter1, 'out_files', createfilter2, 'extra_regressors')
wf.connect(filter1, 'out_res', createfilter2, 'realigned_file')
wf.connect(registration,
('outputspec.segmentation_files', selectindex, [0, 2]),
createfilter2, 'mask_file')
filter2 = MapNode(fsl.GLM(out_f_name='F.nii',
out_pf_name='pF.nii',
demean=True),
iterfield=['in_file', 'design', 'out_res_name'],
name='filter_noise_nosmooth')
wf.connect(filter1, 'out_res', filter2, 'in_file')
wf.connect(filter1, ('out_res', rename, '_cleaned'), filter2,
'out_res_name')
wf.connect(createfilter2, 'out_files', filter2, 'design')
wf.connect(mask, 'mask_file', filter2, 'mask')
bandpass = Node(Function(
input_names=['files', 'lowpass_freq', 'highpass_freq', 'fs'],
output_names=['out_files'],
function=bandpass_filter,
imports=imports),
name='bandpass_unsmooth')
bandpass.inputs.fs = 1. / TR
bandpass.inputs.highpass_freq = highpass_freq
bandpass.inputs.lowpass_freq = lowpass_freq
wf.connect(filter2, 'out_res', bandpass, 'files')
"""Smooth the functional data using
:class:`nipype.interfaces.spm.Smooth`.
"""
smooth = Node(interface=spm.Smooth(), name="smooth")
smooth.inputs.fwhm = vol_fwhm
wf.connect(bandpass, 'out_files', smooth, 'in_files')
collector = Node(Merge(2), name='collect_streams')
wf.connect(smooth, 'smoothed_files', collector, 'in1')
wf.connect(bandpass, 'out_files', collector, 'in2')
"""
Transform the remaining images. First to anatomical and then to target
"""
warpall = MapNode(ants.ApplyTransforms(),
iterfield=['input_image'],
name='warpall')
warpall.inputs.input_image_type = 3
warpall.inputs.interpolation = 'Linear'
warpall.inputs.invert_transform_flags = [False, False]
warpall.inputs.terminal_output = 'file'
warpall.inputs.reference_image = target_file
warpall.inputs.args = '--float'
warpall.inputs.num_threads = 1
# transform to target
wf.connect(collector, 'out', warpall, 'input_image')
wf.connect(registration, 'outputspec.transforms', warpall, 'transforms')
mask_target = Node(fsl.ImageMaths(op_string='-bin'), name='target_mask')
wf.connect(registration, 'outputspec.anat2target', mask_target, 'in_file')
maskts = MapNode(fsl.ApplyMask(), iterfield=['in_file'], name='ts_masker')
wf.connect(warpall, 'output_image', maskts, 'in_file')
wf.connect(mask_target, 'out_file', maskts, 'mask_file')
# map to surface
# extract aparc+aseg ROIs
# extract subcortical ROIs
# extract target space ROIs
# combine subcortical and cortical rois into a single cifti file
#######
# Convert aparc to subject functional space
# Sample the average time series in aparc ROIs
sampleaparc = MapNode(
freesurfer.SegStats(default_color_table=True),
iterfield=['in_file', 'summary_file', 'avgwf_txt_file'],
name='aparc_ts')
sampleaparc.inputs.segment_id = ([8] + list(range(10, 14)) +
[17, 18, 26, 47] + list(range(49, 55)) +
[58] + list(range(1001, 1036)) +
list(range(2001, 2036)))
wf.connect(registration, 'outputspec.aparc', sampleaparc,
'segmentation_file')
wf.connect(collector, 'out', sampleaparc, 'in_file')
def get_names(files, suffix):
"""Generate appropriate names for output files
"""
from nipype.utils.filemanip import (split_filename, filename_to_list,
list_to_filename)
out_names = []
for filename in files:
_, name, _ = split_filename(filename)
out_names.append(name + suffix)
return list_to_filename(out_names)
wf.connect(collector, ('out', get_names, '_avgwf.txt'), sampleaparc,
'avgwf_txt_file')
wf.connect(collector, ('out', get_names, '_summary.stats'), sampleaparc,
'summary_file')
# Sample the time series onto the surface of the target surface. Performs
# sampling into left and right hemisphere
target = Node(IdentityInterface(fields=['target_subject']), name='target')
target.iterables = ('target_subject', filename_to_list(target_subject))
samplerlh = MapNode(freesurfer.SampleToSurface(),
iterfield=['source_file'],
name='sampler_lh')
samplerlh.inputs.sampling_method = "average"
samplerlh.inputs.sampling_range = (0.1, 0.9, 0.1)
samplerlh.inputs.sampling_units = "frac"
samplerlh.inputs.interp_method = "trilinear"
samplerlh.inputs.smooth_surf = surf_fwhm
# samplerlh.inputs.cortex_mask = True
samplerlh.inputs.out_type = 'niigz'
samplerlh.inputs.subjects_dir = subjects_dir
samplerrh = samplerlh.clone('sampler_rh')
samplerlh.inputs.hemi = 'lh'
wf.connect(collector, 'out', samplerlh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerlh, 'reg_file')
wf.connect(target, 'target_subject', samplerlh, 'target_subject')
samplerrh.set_input('hemi', 'rh')
wf.connect(collector, 'out', samplerrh, 'source_file')
wf.connect(registration, 'outputspec.out_reg_file', samplerrh, 'reg_file')
wf.connect(target, 'target_subject', samplerrh, 'target_subject')
# Combine left and right hemisphere to text file
combiner = MapNode(Function(input_names=['left', 'right'],
output_names=['out_file'],
function=combine_hemi,
imports=imports),
iterfield=['left', 'right'],
name="combiner")
wf.connect(samplerlh, 'out_file', combiner, 'left')
wf.connect(samplerrh, 'out_file', combiner, 'right')
# Sample the time series file for each subcortical roi
ts2txt = MapNode(Function(
input_names=['timeseries_file', 'label_file', 'indices'],
output_names=['out_file'],
function=extract_subrois,
imports=imports),
iterfield=['timeseries_file'],
name='getsubcortts')
ts2txt.inputs.indices = [8] + list(range(10, 14)) + [17, 18, 26, 47] +\
list(range(49, 55)) + [58]
ts2txt.inputs.label_file = \
os.path.abspath(('OASIS-TRT-20_jointfusion_DKT31_CMA_labels_in_MNI152_'
'2mm_v2.nii.gz'))
wf.connect(maskts, 'out_file', ts2txt, 'timeseries_file')
######
substitutions = [('_target_subject_', ''),
('_filtermotart_cleaned_bp_trans_masked', ''),
('_filtermotart_cleaned_bp', '')]
regex_subs = [
('_ts_masker.*/sar', '/smooth/'),
('_ts_masker.*/ar', '/unsmooth/'),
('_combiner.*/sar', '/smooth/'),
('_combiner.*/ar', '/unsmooth/'),
('_aparc_ts.*/sar', '/smooth/'),
('_aparc_ts.*/ar', '/unsmooth/'),
('_getsubcortts.*/sar', '/smooth/'),
('_getsubcortts.*/ar', '/unsmooth/'),
('series/sar', 'series/smooth/'),
('series/ar', 'series/unsmooth/'),
('_inverse_transform./', ''),
]
# Save the relevant data into an output directory
datasink = Node(interface=DataSink(), name="datasink")
datasink.inputs.base_directory = sink_directory
datasink.inputs.container = subject_id
datasink.inputs.substitutions = substitutions
datasink.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(realign, 'realignment_parameters', datasink,
'resting.qa.motion')
wf.connect(art, 'norm_files', datasink, 'resting.qa.art.@norm')
wf.connect(art, 'intensity_files', datasink, 'resting.qa.art.@intensity')
wf.connect(art, 'outlier_files', datasink, 'resting.qa.art.@outlier_files')
wf.connect(registration, 'outputspec.segmentation_files', datasink,
'resting.mask_files')
wf.connect(registration, 'outputspec.anat2target', datasink,
'resting.qa.ants')
wf.connect(mask, 'mask_file', datasink, 'resting.mask_files.@brainmask')
wf.connect(mask_target, 'out_file', datasink, 'resting.mask_files.target')
wf.connect(filter1, 'out_f', datasink, 'resting.qa.compmaps.@mc_F')
wf.connect(filter1, 'out_pf', datasink, 'resting.qa.compmaps.@mc_pF')
wf.connect(filter2, 'out_f', datasink, 'resting.qa.compmaps')
wf.connect(filter2, 'out_pf', datasink, 'resting.qa.compmaps.@p')
wf.connect(bandpass, 'out_files', datasink,
'resting.timeseries.@bandpassed')
wf.connect(smooth, 'smoothed_files', datasink,
'resting.timeseries.@smoothed')
wf.connect(createfilter1, 'out_files', datasink,
'resting.regress.@regressors')
wf.connect(createfilter2, 'out_files', datasink,
'resting.regress.@compcorr')
wf.connect(maskts, 'out_file', datasink, 'resting.timeseries.target')
wf.connect(sampleaparc, 'summary_file', datasink,
'resting.parcellations.aparc')
wf.connect(sampleaparc, 'avgwf_txt_file', datasink,
'resting.parcellations.aparc.@avgwf')
wf.connect(ts2txt, 'out_file', datasink,
'resting.parcellations.grayo.@subcortical')
datasink2 = Node(interface=DataSink(), name="datasink2")
datasink2.inputs.base_directory = sink_directory
datasink2.inputs.container = subject_id
datasink2.inputs.substitutions = substitutions
datasink2.inputs.regexp_substitutions = regex_subs # (r'(/_.*(\d+/))', r'/run\2')
wf.connect(combiner, 'out_file', datasink2,
'resting.parcellations.grayo.@surface')
return wf
name='remove_vol')
remove_vol.inputs.t_min = vol_to_remove
preproc.connect([(selectfiles, remove_vol, [('rest', 'in_file')])])
brain_extract_mag1 = Node(fsl.BET(), name='brain_extract_mag1')
preproc.connect([(selectfiles, brain_extract_mag1, [('mag1', 'in_file')])])
# simultaneous slice time and motion correction
slicemoco = Node(nipy.SpaceTimeRealigner(), name="spacetime_realign")
preproc.connect([(remove_vol, slicemoco, [('out_file', 'in_file')])])
# compute first tsnr and detrend
tsnr = Node(TSNR(regress_poly=2), name='tsnr')
preproc.connect([(slicemoco, tsnr, [('out_file', 'in_file')])])
# compute median of realigned timeseries for preperation for fieldmap
median1 = Node(util.Function(input_names=['in_files'],
output_names=['median_file'],
function=median),
name='median1')
#median = Node(SpatialFilter(operation='median'),
# name='median')
preproc.connect([(tsnr, median1, [('detrended_file', 'in_files')])])
#prelude phase unwrapping x 2
output_names=['TR', 'slice_times', 'slice_thickness'],
function=get_info),
name='getinfo')
preproc.connect([(selectfiles, getinfo, [('dicom', 'dicom_file')])])
# simultaneous slice time and motion correction
slicemoco = Node(nipy.SpaceTimeRealigner(), name="spacetime_realign")
slicemoco.inputs.slice_info = 2
preproc.connect([(getinfo, slicemoco, [('slice_times', 'slice_times'),
('TR', 'tr')]),
(remove_vol, slicemoco, [('out_file', 'in_file')])])
# compute tsnr and detrend
# (mostly for tsnr qa, detrending could actually be skipped at this stage)
tsnr = Node(TSNR(regress_poly=2), name='tsnr')
preproc.connect([(slicemoco, tsnr, [('out_file', 'in_file')])])
# compute median of realigned timeseries for coregistration to anatomy
median = Node(util.Function(input_names=['in_files'],
output_names=['median_file'],
function=median),
name='median')
preproc.connect([(tsnr, median, [('detrended_file', 'in_files')])])
# make FOV mask for later nonlinear coregistration
fov = Node(fsl.maths.MathsCommand(args='-bin', out_file='fov_mask.nii.gz'),
name='fov_mask')
preproc.connect([(median, fov, [('median_file', 'in_file')])])
# Slicetiming - correct for slice wise acquisition
interleaved_order = range(1, number_of_slices + 1, 2) + range(
2, number_of_slices + 1, 2)
sliceTiming = Node(SliceTiming(num_slices=number_of_slices,
time_repetition=TR,
time_acquisition=TR - TR / number_of_slices,
slice_order=interleaved_order,
ref_slice=2),
name="sliceTiming")
# Realign - correct for motion
realign = Node(Realign(register_to_mean=True), name="realign")
# TSNR - remove polynomials 2nd order
tsnr = MapNode(TSNR(regress_poly=2), name='tsnr', iterfield=['in_file'])
# Artifact Detection - determine which of the images in the functional series
# are outliers. This is based on deviation in intensity or movement.
art = Node(ArtifactDetect(norm_threshold=1,
zintensity_threshold=3,
mask_type='file',
parameter_source='SPM',
use_differences=[True, False]),
name="art")
# Gunzip - unzip functional
gunzip = MapNode(Gunzip(), name="gunzip", iterfield=['in_file'])
# Smooth - to smooth the images with a given kernel
#I will not be using this if I'm doing MVPA analysis, but may use it for GLM analysis
def return_fd_tsnr_dist(population, out_dir, pipeline_name):
fd_means = []
tsnr_files = []
mask_files = []
missing_subjects = []
for subject in population:
subject_dir = os.path.join(out_dir, pipeline_name, subject)
mkdir_path(os.path.join(subject_dir, 'quality_control'))
qc_dir = os.path.join(subject_dir, 'quality_control')
subject_dir = os.path.join(out_dir, pipeline_name, subject)
fd1d = os.path.join(subject_dir, 'functional_motion_FDPower/FD.1D')
if os.path.isfile(fd1d):
fd_means.append(np.mean(np.genfromtxt(fd1d)))
else:
print subject, 'has no fd1d'
missing_subjects.append(subject)
os.chdir(qc_dir)
pp_file = os.path.join(
subject_dir,
'functional_native_brain_preproc/REST_calc_resample_corrected_volreg_maths_brain.nii.gz'
)
tsnr_file = os.path.join(
qc_dir,
'REST_calc_resample_corrected_volreg_maths_brain_tsnr.nii.gz')
mask_file = os.path.join(
subject_dir,
'functional_native_brain_preproc_mask/REST_calc_resample_corrected_volreg_maths_brain_mask.nii.gz'
)
if os.path.isfile(tsnr_file):
tsnr_files.append(tsnr_file)
mask_files.append(mask_file)
else:
if os.path.isfile(pp_file):
tsnr = TSNR()
tsnr.inputs.in_file = pp_file
res = tsnr.run()
tsnr_files.append(res.outputs.tsnr_file)
else:
print subject, 'has no functional_native_preproc'
tsnr_distributions = volumes.get_median_distribution(
tsnr_files, mask_files)
population_fd_means = fd_means
np.savetxt(
os.path.join(out_dir, 'GluConnectivity',
'population_fd_distributions.txt'), population_fd_means)
np.savetxt(
os.path.join(out_dir, 'GluConnectivity',
'population_tsnr_distributions.txt'), tsnr_distributions)
print 'FD mean=', population_fd_means
print 'TSNR_distribution=', tsnr_distributions
print ''
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):
from nipype.external import six
import numpy as np
num_conds = len(conds)
if isinstance(behav, six.string_types):
behav = [behav]
behav_array = np.array(behav).flatten()
num_elements = behav_array.shape[0]
return behav_array.reshape(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(len(all_copes)/num_copes, num_copes).T.tolist()
outvarcopes = all_varcopes.reshape(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
def create_workflow(func_runs,
subject_id,
subjects_dir,
fwhm,
slice_times,
highpass_frequency,
lowpass_frequency,
TR,
sink_directory,
use_fsl_bp,
num_components,
whichvol,
name='wmaze'):
wf = pe.Workflow(name=name)
datasource = pe.Node(nio.DataGrabber(infields=['subject_id', 'run'],
outfields=['func']),
name='datasource')
datasource.inputs.subject_id = subject_id
datasource.inputs.run = func_runs
datasource.inputs.template = '/home/data/madlab/data/mri/wmaze/%s/bold/bold_%03d/bold.nii.gz'
datasource.inputs.sort_filelist = True
# Rename files in case they are named identically
name_unique = pe.MapNode(util.Rename(format_string='wmaze_%(run)02d'),
iterfield = ['in_file', 'run'],
name='rename')
name_unique.inputs.keep_ext = True
name_unique.inputs.run = func_runs
wf.connect(datasource, 'func', name_unique, 'in_file')
# Define the outputs for the preprocessing workflow
output_fields = ['reference',
'motion_parameters',
'motion_parameters_plusDerivs',
'motionandoutlier_noise_file',
'noise_components',
'realigned_files',
'motion_plots',
'mask_file',
'smoothed_files',
'bandpassed_files',
'reg_file',
'reg_cost',
'reg_fsl_file',
'artnorm_files',
'artoutlier_files',
'artdisplacement_files',
'tsnr_file']
outputnode = pe.Node(util.IdentityInterface(fields=output_fields),
name='outputspec')
# Convert functional images to float representation
img2float = pe.MapNode(fsl.ImageMaths(out_data_type='float',
op_string = '',
suffix='_dtype'),
iterfield=['in_file'],
name='img2float')
wf.connect(name_unique, 'out_file', img2float, 'in_file')
# Run AFNI's despike. This is always run, however, whether this is fed to
# realign depends on the input configuration
despiker = pe.MapNode(afni.Despike(outputtype='NIFTI_GZ'),
iterfield=['in_file'],
name='despike')
num_threads = 4
despiker.inputs.environ = {'OMP_NUM_THREADS': '%d' % num_threads}
despiker.plugin_args = {'bsub_args': '-n %d' % num_threads}
despiker.plugin_args = {'bsub_args': '-R "span[hosts=1]"'}
wf.connect(img2float, 'out_file', despiker, 'in_file')
# Extract the first volume of the first run as the reference
extractref = pe.Node(fsl.ExtractROI(t_size=1),
iterfield=['in_file'],
name = "extractref")
wf.connect(despiker, ('out_file', pickfirst), extractref, 'in_file')
wf.connect(despiker, ('out_file', pickvol, 0, whichvol), extractref, 't_min')
wf.connect(extractref, 'roi_file', outputnode, 'reference')
if slice_times is not None:
# Simultaneous motion and slice timing correction with Nipy algorithm
motion_correct = pe.Node(nipy.SpaceTimeRealigner(), name='motion_correct')
motion_correct.inputs.tr = TR
motion_correct.inputs.slice_times = slice_times
motion_correct.inputs.slice_info = 2
motion_correct.plugin_args = {'bsub_args': '-n %s' %os.environ['MKL_NUM_THREADS']}
motion_correct.plugin_args = {'bsub_args': '-R "span[hosts=1]"'}
wf.connect(despiker, 'out_file', motion_correct, 'in_file')
wf.connect(motion_correct, 'par_file', outputnode, 'motion_parameters')
wf.connect(motion_correct, 'out_file', outputnode, 'realigned_files')
else:
# Motion correct functional runs to the reference (1st volume of 1st run)
motion_correct = pe.MapNode(fsl.MCFLIRT(save_mats = True,
save_plots = True,
interpolation = 'sinc'),
name = 'motion_correct',
iterfield = ['in_file'])
wf.connect(despiker, 'out_file', motion_correct, 'in_file')
wf.connect(extractref, 'roi_file', motion_correct, 'ref_file')
wf.connect(motion_correct, 'par_file', outputnode, 'motion_parameters')
wf.connect(motion_correct, 'out_file', outputnode, 'realigned_files')
# Compute TSNR on realigned data regressing polynomials upto order 2
tsnr = pe.MapNode(TSNR(regress_poly=2), iterfield=['in_file'], name='tsnr')
wf.connect(motion_correct, 'out_file', tsnr, 'in_file')
wf.connect(tsnr, 'tsnr_file', outputnode, 'tsnr_file')
# Plot the estimated motion parameters
plot_motion = pe.MapNode(fsl.PlotMotionParams(in_source='fsl'),
name='plot_motion',
iterfield=['in_file'])
plot_motion.iterables = ('plot_type', ['rotations', 'translations'])
wf.connect(motion_correct, 'par_file', plot_motion, 'in_file')
wf.connect(plot_motion, 'out_file', outputnode, 'motion_plots')
# Register a source file to fs space and create a brain mask in source space
fssource = pe.Node(nio.FreeSurferSource(),
name ='fssource')
fssource.inputs.subject_id = subject_id
fssource.inputs.subjects_dir = subjects_dir
# Extract aparc+aseg brain mask and binarize
fs_threshold = pe.Node(fs.Binarize(min=0.5, out_type='nii'),
name ='fs_threshold')
wf.connect(fssource, ('aparc_aseg', get_aparc_aseg), fs_threshold, 'in_file')
# Calculate the transformation matrix from EPI space to FreeSurfer space
# using the BBRegister command
fs_register = pe.MapNode(fs.BBRegister(init='fsl'),
iterfield=['source_file'],
name ='fs_register')
fs_register.inputs.contrast_type = 't2'
fs_register.inputs.out_fsl_file = True
fs_register.inputs.subject_id = subject_id
fs_register.inputs.subjects_dir = subjects_dir
wf.connect(extractref, 'roi_file', fs_register, 'source_file')
wf.connect(fs_register, 'out_reg_file', outputnode, 'reg_file')
wf.connect(fs_register, 'min_cost_file', outputnode, 'reg_cost')
wf.connect(fs_register, 'out_fsl_file', outputnode, 'reg_fsl_file')
# Extract wm+csf, brain masks by eroding freesurfer lables
wmcsf = pe.MapNode(fs.Binarize(),
iterfield=['match', 'binary_file', 'erode'], name='wmcsfmask')
#wmcsf.inputs.wm_ven_csf = True
wmcsf.inputs.match = [[2, 41], [4, 5, 14, 15, 24, 31, 43, 44, 63]]
wmcsf.inputs.binary_file = ['wm.nii.gz', 'csf.nii.gz']
wmcsf.inputs.erode = [2, 2] #int(np.ceil(slice_thickness))
wf.connect(fssource, ('aparc_aseg', get_aparc_aseg), wmcsf, 'in_file')
# Now transform the wm and csf masks to 1st volume of 1st run
wmcsftransform = pe.MapNode(fs.ApplyVolTransform(inverse=True,
interp='nearest'),
iterfield=['target_file'],
name='wmcsftransform')
wmcsftransform.inputs.subjects_dir = subjects_dir
wf.connect(extractref, 'roi_file', wmcsftransform, 'source_file')
wf.connect(fs_register, ('out_reg_file', pickfirst), wmcsftransform, 'reg_file')
wf.connect(wmcsf, 'binary_file', wmcsftransform, 'target_file')
# Transform the binarized aparc+aseg file to the 1st volume of 1st run space
fs_voltransform = pe.MapNode(fs.ApplyVolTransform(inverse=True),
iterfield = ['source_file', 'reg_file'],
name='fs_transform')
fs_voltransform.inputs.subjects_dir = subjects_dir
wf.connect(extractref, 'roi_file', fs_voltransform, 'source_file')
wf.connect(fs_register, 'out_reg_file', fs_voltransform, 'reg_file')
wf.connect(fs_threshold, 'binary_file', fs_voltransform, 'target_file')
# Dilate the binarized mask by 1 voxel that is now in the EPI space
fs_threshold2 = pe.MapNode(fs.Binarize(min=0.5, out_type='nii'),
iterfield=['in_file'],
name='fs_threshold2')
fs_threshold2.inputs.dilate = 1
wf.connect(fs_voltransform, 'transformed_file', fs_threshold2, 'in_file')
wf.connect(fs_threshold2, 'binary_file', outputnode, 'mask_file')
# Use RapidART to detect motion/intensity outliers
art = pe.MapNode(ra.ArtifactDetect(use_differences = [True, False],
use_norm = True,
zintensity_threshold = 3,
norm_threshold = 1,
bound_by_brainmask=True,
mask_type = "file"),
iterfield=["realignment_parameters","realigned_files"],
name="art")
if slice_times is not None:
art.inputs.parameter_source = "NiPy"
else:
art.inputs.parameter_source = "FSL"
wf.connect(motion_correct, 'par_file', art, 'realignment_parameters')
wf.connect(motion_correct, 'out_file', art, 'realigned_files')
wf.connect(fs_threshold2, ('binary_file', pickfirst), art, 'mask_file')
wf.connect(art, 'norm_files', outputnode, 'artnorm_files')
wf.connect(art, 'outlier_files', outputnode, 'artoutlier_files')
wf.connect(art, 'displacement_files', outputnode, 'artdisplacement_files')
# Compute motion regressors (save file with 1st and 2nd derivatives)
motreg = pe.Node(util.Function(input_names=['motion_params', 'order',
'derivatives'],
output_names=['out_files'],
function=motion_regressors,
imports=imports),
name='getmotionregress')
wf.connect(motion_correct, 'par_file', motreg, 'motion_params')
wf.connect(motreg, 'out_files', outputnode, 'motion_parameters_plusDerivs')
# Create a filter text file to remove motion (+ derivatives), art confounds,
# and 1st, 2nd, and 3rd order legendre polynomials.
createfilter1 = pe.Node(util.Function(input_names=['motion_params', 'comp_norm',
'outliers', 'detrend_poly'],
output_names=['out_files'],
function=build_filter1,
imports=imports),
name='makemotionbasedfilter')
createfilter1.inputs.detrend_poly = 3
wf.connect(motreg, 'out_files', createfilter1, 'motion_params')
wf.connect(art, 'norm_files', createfilter1, 'comp_norm')
wf.connect(art, 'outlier_files', createfilter1, 'outliers')
wf.connect(createfilter1, 'out_files', outputnode, 'motionandoutlier_noise_file')
# Create a filter to remove noise components based on white matter and CSF
createfilter2 = pe.MapNode(util.Function(input_names=['realigned_file', 'mask_file',
'num_components',
'extra_regressors'],
output_names=['out_files'],
function=extract_noise_components,
imports=imports),
iterfield=['realigned_file', 'extra_regressors'],
name='makecompcorrfilter')
createfilter2.inputs.num_components = num_components
wf.connect(createfilter1, 'out_files', createfilter2, 'extra_regressors')
wf.connect(motion_correct, 'out_file', createfilter2, 'realigned_file')
wf.connect(wmcsftransform, 'transformed_file', createfilter2, 'mask_file')
wf.connect(createfilter2, 'out_files', outputnode, 'noise_components')
# Mask the functional runs with the extracted mask
maskfunc = pe.MapNode(fsl.ImageMaths(suffix='_bet',
op_string='-mas'),
iterfield=['in_file'],
name = 'maskfunc')
wf.connect(motion_correct, 'out_file', maskfunc, 'in_file')
wf.connect(fs_threshold2, ('binary_file', pickfirst), maskfunc, 'in_file2')
# Smooth each run using SUSAn with the brightness threshold set to 75%
# of the median value for each run and a mask constituting the mean functional
smooth_median = pe.MapNode(fsl.ImageStats(op_string='-k %s -p 50'),
iterfield = ['in_file'],
name='smooth_median')
wf.connect(maskfunc, 'out_file', smooth_median, 'in_file')
wf.connect(fs_threshold2, ('binary_file', pickfirst), smooth_median, 'mask_file')
smooth_meanfunc = pe.MapNode(fsl.ImageMaths(op_string='-Tmean',
suffix='_mean'),
iterfield=['in_file'],
name='smooth_meanfunc')
wf.connect(maskfunc, 'out_file', smooth_meanfunc, 'in_file')
smooth_merge = pe.Node(util.Merge(2, axis='hstack'),
name='smooth_merge')
wf.connect(smooth_meanfunc, 'out_file', smooth_merge, 'in1')
wf.connect(smooth_median, 'out_stat', smooth_merge, 'in2')
smooth = pe.MapNode(fsl.SUSAN(),
iterfield=['in_file', 'brightness_threshold', 'usans'],
name='smooth')
smooth.inputs.fwhm=fwhm
wf.connect(maskfunc, 'out_file', smooth, 'in_file')
wf.connect(smooth_median, ('out_stat', getbtthresh), smooth, 'brightness_threshold')
wf.connect(smooth_merge, ('out', getusans), smooth, 'usans')
# Mask the smoothed data with the dilated mask
maskfunc2 = pe.MapNode(fsl.ImageMaths(suffix='_mask',
op_string='-mas'),
iterfield=['in_file'],
name='maskfunc2')
wf.connect(smooth, 'smoothed_file', maskfunc2, 'in_file')
wf.connect(fs_threshold2, ('binary_file', pickfirst), maskfunc2, 'in_file2')
wf.connect(maskfunc2, 'out_file', outputnode, 'smoothed_files')
# Band-pass filter the timeseries
if use_fsl_bp == 'True':
determine_bp_sigmas = pe.Node(util.Function(input_names=['tr',
'highpass_freq',
'lowpass_freq'],
output_names = ['out_sigmas'],
function=calc_fslbp_sigmas),
name='determine_bp_sigmas')
determine_bp_sigmas.inputs.tr = float(TR)
determine_bp_sigmas.inputs.highpass_freq = float(highpass_frequency)
determine_bp_sigmas.inputs.lowpass_freq = float(lowpass_frequency)
bandpass = pe.MapNode(fsl.ImageMaths(suffix='_tempfilt'),
iterfield=["in_file"],
name="bandpass")
wf.connect(determine_bp_sigmas, ('out_sigmas', highpass_operand), bandpass, 'op_string')
wf.connect(maskfunc2, 'out_file', bandpass, 'in_file')
wf.connect(bandpass, 'out_file', outputnode, 'bandpassed_files')
else:
bandpass = pe.Node(util.Function(input_names=['files',
'lowpass_freq',
'highpass_freq',
'fs'],
output_names=['out_files'],
function=bandpass_filter,
imports=imports),
name='bandpass')
bandpass.inputs.fs = 1./TR
if highpass_frequency < 0:
bandpass.inputs.highpass_freq = -1
else:
bandpass.inputs.highpass_freq = highpass_frequency
if lowpass_frequency < 0:
bandpass.inputs.lowpass_freq = -1
else:
bandpass.inputs.lowpass_freq = lowpass_frequency
wf.connect(maskfunc2, 'out_file', bandpass, 'files')
wf.connect(bandpass, 'out_files', outputnode, 'bandpassed_files')
# Save the relevant data into an output directory
datasink = pe.Node(nio.DataSink(), name="datasink")
datasink.inputs.base_directory = sink_directory
datasink.inputs.container = subject_id
wf.connect(outputnode, 'reference', datasink, 'ref')
wf.connect(outputnode, 'motion_parameters', datasink, 'motion')
wf.connect(outputnode, 'realigned_files', datasink, 'func.realigned')
wf.connect(outputnode, 'motion_plots', datasink, 'motion.@plots')
wf.connect(outputnode, 'mask_file', datasink, 'ref.@mask')
wf.connect(outputnode, 'smoothed_files', datasink, 'func.smoothed_fullspectrum')
wf.connect(outputnode, 'bandpassed_files', datasink, 'func.smoothed_bandpassed')
wf.connect(outputnode, 'reg_file', datasink, 'bbreg.@reg')
wf.connect(outputnode, 'reg_cost', datasink, 'bbreg.@cost')
wf.connect(outputnode, 'reg_fsl_file', datasink, 'bbreg.@regfsl')
wf.connect(outputnode, 'artnorm_files', datasink, 'art.@norm_files')
wf.connect(outputnode, 'artoutlier_files', datasink, 'art.@outlier_files')
wf.connect(outputnode, 'artdisplacement_files', datasink, 'art.@displacement_files')
wf.connect(outputnode, 'motion_parameters_plusDerivs', datasink, 'noise.@motionplusDerivs')
wf.connect(outputnode, 'motionandoutlier_noise_file', datasink, 'noise.@motionplusoutliers')
wf.connect(outputnode, 'noise_components', datasink, 'compcor')
wf.connect(outputnode, 'tsnr_file', datasink, 'tsnr')
return wf
def create_resting_preproc(name='restpreproc'):
"""Create a "resting" time series preprocessing workflow
The noise removal is based on Behzadi et al. (2007)
Parameters
----------
name : name of workflow (default: restpreproc)
Inputs::
inputspec.func : functional run (filename or list of filenames)
Outputs::
outputspec.noise_mask_file : voxels used for PCA to derive noise components
outputspec.filtered_file : bandpass filtered and noise-reduced time series
Example
-------
>>> TR = 3.0
>>> wf = create_resting_preproc()
>>> wf.inputs.inputspec.func = 'f3.nii'
>>> wf.inputs.inputspec.num_noise_components = 6
>>> wf.inputs.inputspec.highpass_sigma = 100/(2*TR)
>>> wf.inputs.inputspec.lowpass_sigma = 12.5/(2*TR)
>>> wf.run() # doctest: +SKIP
"""
restpreproc = pe.Workflow(name=name)
# Define nodes
inputnode = pe.Node(interface=util.IdentityInterface(fields=[
'func', 'num_noise_components', 'highpass_sigma', 'lowpass_sigma'
]),
name='inputspec')
outputnode = pe.Node(interface=util.IdentityInterface(fields=[
'noise_mask_file',
'filtered_file',
]),
name='outputspec')
slicetimer = pe.Node(fsl.SliceTimer(), name='slicetimer')
realigner = create_realign_flow()
tsnr = pe.Node(TSNR(regress_poly=2), name='tsnr')
getthresh = pe.Node(interface=fsl.ImageStats(op_string='-p 98'),
name='getthreshold')
threshold_stddev = pe.Node(fsl.Threshold(), name='threshold')
compcor = pe.Node(util.Function(
input_names=['realigned_file', 'noise_mask_file', 'num_components'],
output_names=['noise_components'],
function=extract_noise_components),
name='compcorr')
remove_noise = pe.Node(fsl.FilterRegressor(filter_all=True),
name='remove_noise')
bandpass_filter = pe.Node(fsl.TemporalFilter(), name='bandpass_filter')
# Define connections
restpreproc.connect(inputnode, 'func', slicetimer, 'in_file')
restpreproc.connect(slicetimer, 'slice_time_corrected_file', realigner,
'inputspec.func')
restpreproc.connect(realigner, 'outputspec.realigned_file', tsnr,
'in_file')
restpreproc.connect(tsnr, 'stddev_file', threshold_stddev, 'in_file')
restpreproc.connect(tsnr, 'stddev_file', getthresh, 'in_file')
restpreproc.connect(getthresh, 'out_stat', threshold_stddev, 'thresh')
restpreproc.connect(realigner, 'outputspec.realigned_file', compcor,
'realigned_file')
restpreproc.connect(threshold_stddev, 'out_file', compcor,
'noise_mask_file')
restpreproc.connect(inputnode, 'num_noise_components', compcor,
'num_components')
restpreproc.connect(tsnr, 'detrended_file', remove_noise, 'in_file')
restpreproc.connect(compcor, 'noise_components', remove_noise,
'design_file')
restpreproc.connect(inputnode, 'highpass_sigma', bandpass_filter,
'highpass_sigma')
restpreproc.connect(inputnode, 'lowpass_sigma', bandpass_filter,
'lowpass_sigma')
restpreproc.connect(remove_noise, 'out_file', bandpass_filter, 'in_file')
restpreproc.connect(threshold_stddev, 'out_file', outputnode,
'noise_mask_file')
restpreproc.connect(bandpass_filter, 'out_file', outputnode,
'filtered_file')
return restpreproc
import nipype.algorithms.misc as misc
import argparse
from nipype.algorithms.misc import TSNR
parser=argparse.ArgumentParser()
parser.add_argument('--input', '-i', nargs='+', required=True, help='Image to process')
cmdInput=parser.parse_args()
for filename in cmdInput.input:
tsnr=TSNR()
print(filename)
tsnr.inputs.in_file=filename
results=tsnr.run()
results=tsnr.run()
