def applywarp(infile, ref, regdir, outname):
warp = os.path.join(regdir, 'highres2standard_warp.nii.gz')
premat = os.path.join(regdir, 'example_func2highres.mat')
if not os.path.isfile(warp):
print 'no warp found: ', warp
return None
if not os.path.isfile(premat):
print 'no premat found ', premat
return None
startdir = os.getcwd()
pth, nme = os.path.split(infile)
os.chdir(pth)
mywarp = ApplyWarp()
mywarp.inputs.in_file = infile
mywarp.inputs.ref_file = ref
mywarp.inputs.field_file = warp
mywarp.inputs.premat = premat
warpout = mywarp.run()
os.chdir(startdir)
if warpout.runtime.returncode == 0:
#apply warp successful
warped = warpout.outputs.out_file
return warped
else:
print warpout.runtime.stderr
return None
def make_sub_cnxn_visitation_map(sub,dpy_file,parc_file,warp_file,ref_file,
cnxn_inds_file,cnxn_name,vismap_fstr):
overwrite=True
import os,h5py,numpy as np,nibabel as nib
from dipy.io import Dpy
from dipy.tracking.utils import connectivity_matrix,length,density_map
from nipype.interfaces.fsl import ApplyWarp
F = h5py.File(cnxn_inds_file, 'r')
if cnxn_name in F.keys():
stream_idxs = F[cnxn_name].value
F.close()
D = Dpy(dpy_file, 'r')
streams = D.read_tracksi(stream_idxs)
D.close()
parc_img = nib.load(parc_file)
affine = np.eye(4)
outfile_nat = os.path.abspath('vismap_nat_%s.nii.gz' %cnxn_name)
outfile_std = os.path.abspath(vismap_fstr %cnxn_name)
dm = density_map(streams,parc_img.shape,affine=affine)
dm_img = nib.Nifti1Image(dm.astype("int16"), parc_img.affine)
dm_img.to_filename(outfile_nat)
print 'warping cnxn image to standard space'
aw = ApplyWarp(in_file=outfile_nat,
out_file=outfile_std,
ref_file =ref_file,
field_file =warp_file)
aw.run()
else:
outfile_std = 'cnxn not found'
F.close()
return outfile_std
def preproc(data_dir, sink_dir, subject, task, session, run, masks,
motion_thresh, moco):
from nipype.interfaces.fsl import MCFLIRT, FLIRT, FNIRT, ExtractROI, ApplyWarp, MotionOutliers, InvWarp, FAST
#from nipype.interfaces.afni import AlignEpiAnatPy
from nipype.interfaces.utility import Function
from nilearn.plotting import plot_anat
from nilearn import input_data
#WRITE A DARA GRABBER
def get_niftis(subject_id, data_dir, task, run, session):
from os.path import join, exists
t1 = join(data_dir, subject_id, 'session-{0}'.format(session),
'anatomical', 'anatomical-0', 'anatomical.nii.gz')
#t1_brain_mask = join(data_dir, subject_id, 'session-1', 'anatomical', 'anatomical-0', 'fsl', 'anatomical-bet.nii.gz')
epi = join(data_dir, subject_id, 'session-{0}'.format(session), task,
'{0}-{1}'.format(task, run), '{0}.nii.gz'.format(task))
assert exists(t1), "t1 does not exist at {0}".format(t1)
assert exists(epi), "epi does not exist at {0}".format(epi)
standard = '/home/applications/fsl/5.0.8/data/standard/MNI152_T1_2mm.nii.gz'
return t1, epi, standard
data = Function(
function=get_niftis,
input_names=["subject_id", "data_dir", "task", "run", "session"],
output_names=["t1", "epi", "standard"])
data.inputs.data_dir = data_dir
data.inputs.subject_id = subject
data.inputs.run = run
data.inputs.session = session
data.inputs.task = task
grabber = data.run()
if session == 0:
sesh = 'pre'
if session == 1:
sesh = 'post'
#reg_dir = '/home/data/nbc/physics-learning/data/first-level/{0}/session-1/retr/retr-{1}/retr-5mm.feat/reg'.format(subject, run)
#set output paths for quality assurance pngs
qa1 = join(
sink_dir, 'qa',
'{0}-session-{1}_{2}-{3}_t1_flirt.png'.format(subject, session, task,
run))
qa2 = join(
sink_dir, 'qa',
'{0}-session-{1}_{2}-{3}_mni_flirt.png'.format(subject, session, task,
run))
qa3 = join(
sink_dir, 'qa',
'{0}-session-{1}_{2}-{3}_mni_fnirt.png'.format(subject, session, task,
run))
confound_file = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_confounds.txt'.format(subject, session, task,
run))
#run motion correction if indicated
if moco == True:
mcflirt = MCFLIRT(ref_vol=144, save_plots=True, output_type='NIFTI_GZ')
mcflirt.inputs.in_file = grabber.outputs.epi
#mcflirt.inputs.in_file = join(data_dir, subject, 'session-1', 'retr', 'retr-{0}'.format(run), 'retr.nii.gz')
mcflirt.inputs.out_file = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_mcf.nii.gz'.format(
subject, session, task, run))
flirty = mcflirt.run()
motion = np.genfromtxt(flirty.outputs.par_file)
else:
print "no moco needed"
motion = 0
#calculate motion outliers
try:
mout = MotionOutliers(metric='fd', threshold=motion_thresh)
mout.inputs.in_file = grabber.outputs.epi
mout.inputs.out_file = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_fd-gt-{3}mm'.format(
subject, session, task, run, motion_thresh))
mout.inputs.out_metric_plot = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_metrics.png'.format(
subject, session, task, run))
mout.inputs.out_metric_values = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_fd.txt'.format(subject, session, task,
run))
moutliers = mout.run()
outliers = np.genfromtxt(moutliers.outputs.out_file)
e = 'no errors in motion outliers, yay'
except Exception as e:
print(e)
outliers = np.genfromtxt(mout.inputs.out_metric_values)
#set everything above the threshold to 1 and everything below to 0
outliers[outliers > motion_thresh] = 1
outliers[outliers < motion_thresh] = 0
#concatenate motion parameters and motion outliers to form confounds file
#outliers = outliers.reshape((outliers.shape[0],1))
conf = outliers
np.savetxt(confound_file, conf, delimiter=',')
#extract an example volume for normalization
ex_fun = ExtractROI(t_min=144, t_size=1)
ex_fun.inputs.in_file = flirty.outputs.out_file
ex_fun.inputs.roi_file = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}-example_func.nii.gz'.format(
subject, session, task, run))
fun = ex_fun.run()
warp = ApplyWarp(interp="nn", abswarp=True)
if not exists(
'/home/data/nbc/physics-learning/data/first-level/{0}/session-{1}/{2}/{2}-{3}/{2}-5mm.feat/reg/example_func2standard_warp.nii.gz'
.format(subject, session, task, run)):
#two-step normalization using flirt and fnirt, outputting qa pix
flit = FLIRT(cost_func="corratio", dof=12)
reg_func = flit.run(
reference=fun.outputs.roi_file,
in_file=grabber.outputs.t1,
searchr_x=[-180, 180],
searchr_y=[-180, 180],
out_file=join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_t1-flirt.nii.gz'.format(
subject, session, task, run)),
out_matrix_file=join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_t1-flirt.mat'.format(
subject, session, task, run)))
reg_mni = flit.run(
reference=grabber.outputs.t1,
in_file=grabber.outputs.standard,
searchr_y=[-180, 180],
searchr_z=[-180, 180],
out_file=join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_mni-flirt-t1.nii.gz'.format(
subject, session, task, run)),
out_matrix_file=join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_mni-flirt-t1.mat'.format(
subject, session, task, run)))
#plot_stat_map(aligner.outputs.out_file, bg_img=fun.outputs.roi_file, colorbar=True, draw_cross=False, threshold=1000, output_file=qa1a, dim=-2)
display = plot_anat(fun.outputs.roi_file, dim=-1)
display.add_edges(reg_func.outputs.out_file)
display.savefig(qa1, dpi=300)
display.close()
display = plot_anat(grabber.outputs.t1, dim=-1)
display.add_edges(reg_mni.outputs.out_file)
display.savefig(qa2, dpi=300)
display.close()
perf = FNIRT(output_type='NIFTI_GZ')
perf.inputs.warped_file = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_mni-fnirt-t1.nii.gz'.format(
subject, session, task, run))
perf.inputs.affine_file = reg_mni.outputs.out_matrix_file
perf.inputs.in_file = grabber.outputs.standard
perf.inputs.subsampling_scheme = [8, 4, 2, 2]
perf.inputs.fieldcoeff_file = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_mni-fnirt-t1-warpcoeff.nii.gz'.format(
subject, session, task, run))
perf.inputs.field_file = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_mni-fnirt-t1-warp.nii.gz'.format(
subject, session, task, run))
perf.inputs.ref_file = grabber.outputs.t1
reg2 = perf.run()
warp.inputs.field_file = reg2.outputs.field_file
#plot fnirted MNI overlaid on example func
display = plot_anat(grabber.outputs.t1, dim=-1)
display.add_edges(reg2.outputs.warped_file)
display.savefig(qa3, dpi=300)
display.close()
else:
warpspeed = InvWarp(output_type='NIFTI_GZ')
warpspeed.inputs.warp = '/home/data/nbc/physics-learning/data/first-level/{0}/session-{1}/{2}/{2}-{3}/{2}-5mm.feat/reg/example_func2standard_warp.nii.gz'.format(
subject, session, task, run)
warpspeed.inputs.reference = fun.outputs.roi_file
warpspeed.inputs.inverse_warp = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_mni-fnirt-t1-warp.nii.gz'.format(
subject, session, task, run))
mni2epiwarp = warpspeed.run()
warp.inputs.field_file = mni2epiwarp.outputs.inverse_warp
for key in masks.keys():
#warp takes us from mni to epi
warp.inputs.in_file = masks[key]
warp.inputs.ref_file = fun.outputs.roi_file
warp.inputs.out_file = join(
sink_dir, sesh, subject,
'{0}-session-{1}_{2}-{3}_{4}.nii.gz'.format(
subject, session, task, run, key))
net_warp = warp.run()
qa_file = join(
sink_dir, 'qa', '{0}-session-{1}_{2}-{3}_qa_{4}.png'.format(
subject, session, task, run, key))
display = plotting.plot_roi(net_warp.outputs.out_file,
bg_img=fun.outputs.roi_file,
colorbar=True,
vmin=0,
vmax=18,
draw_cross=False)
display.savefig(qa_file, dpi=300)
display.close()
return flirty.outputs.out_file, confound_file, e
#invert the epi-to-mni warpfield so you can run these analyses in native space
invert.inputs.warp = join(data_dir, subject, 'session-{0}'.format(session), 'resting-state/resting-state-0/endor1.feat/reg/example_func2standard_warp.nii.gz')
invert.inputs.inverse_warp = mni2epiwarp
invert.inputs.reference = join(data_dir, subject, 'session-{0}'.format(session), 'resting-state/resting-state-0/endor1.feat/reg/example_func.nii.gz')
inverted = invert.run()
warpspeed.inputs.ref_file = join(data_dir, subject, 'session-{0}'.format(session), 'resting-state/resting-state-0/endor1.feat/reg/example_func.nii.gz')
warpspeed.inputs.field_file = inverted.outputs.inverse_warp
xfmd_masks = {}
for mask in masks.keys():
mask_nativespace = join(sink_dir, sesh[session], subject, '{0}-session-{1}_rest_{2}.nii.gz'.format(subject, session, mask))
warpspeed.inputs.in_file = masks[mask]
warpspeed.inputs.out_file = mask_nativespace
warped = warpspeed.run()
xfmd_masks[mask] = mask_nativespace
shen_masker = NiftiLabelsMasker(xfmd_masks['shen2015'], background_label=0, standardize=True, detrend=True,t_r=3.)
craddock_masker = NiftiLabelsMasker(xfmd_masks['craddock2012'], background_label=0, standardize=True, detrend=True,t_r=3.)
confounds = '/home/data/nbc/physics-learning/anxiety-physics/output/{1}/{0}/{0}_confounds.txt'.format(subject, sesh[session])
epi_data = join(data_dir, subject, 'session-{0}'.format(session), 'resting-state/resting-state-0/endor1.feat', 'filtered_func_data.nii.gz')
shen_ts = shen_masker.fit_transform(epi_data, confounds)
shen_corrmat = correlation_measure.fit_transform([shen_ts])[0]
np.savetxt(join(sink_dir, sesh[session], subject, '{0}-session-{1}-rest_network_corrmat_shen2015.csv'.format(subject, session)), shen_corrmat, delimiter=",")
#shen_corrmat = np.genfromtxt(join(sink_dir, session, 'resting-state', subject, '{0}_network_corrmat_shen2015.csv'.format(subject)), delimiter=",")
craddock_ts = craddock_masker.fit_transform(epi_data, confounds)
craddock_corrmat = correlation_measure.fit_transform([craddock_ts])[0]
applywarp.inputs.field_file = join(
sink_dir,
session,
"resting-state",
subject,
"{0}-{1}_mni-fnirt-epi-warp.nii.gz".format(
subject, session),
)
applywarp.inputs.out_file = join(
sink_dir,
session,
"resting-state",
subject,
"{0}-{1}_{2}.nii.gz".format(subject, session, key),
)
res = applywarp.run()
qa_file = join(
sink_dir,
"output",
"qa",
"{0}-{1}_qa-rest_{2}.png".format(subject, session, key),
)
display = plotting.plot_roi(
res.outputs.out_file,
bg_img=example_func,
colorbar=True,
vmin=0,
vmax=18,
draw_cross=False,
def fnirt_again(data_dir, sink_dir, subject, run, masks, mask_names):
import numpy as np
def get_niftis(subject, data_dir, sink_dir, run, masks):
from os.path import join, exists
t1 = join(
data_dir,
subject,
"session-1",
"anatomical",
"anatomical-0",
"anatomical.nii.gz",
)
# t1_brain_mask = join(data_dir, subject, 'session-1', 'anatomical', 'anatomical-0', 'fsl', 'anatomical-bet.nii.gz')
example_func = join(
sink_dir, subject,
"{0}-{1}_retr-example_func.nii.gz".format(subject, run))
assert exists(t1), "t1 does not exist"
assert exists(example_func), "example_func does not exist"
standard = "/home/applications/fsl/5.0.8/data/standard/MNI152_T1_2mm.nii.gz"
mni2t1 = join(sink_dir, subject,
"{0}-{1}_mni-flirt-t1.mat".format(subject, run))
t12epi = join(sink_dir, subject,
"{0}-{1}_t1-flirt-retr.mat".format(subject, run))
masks = masks
return t1, example_func, standard, mni2t1, t12epi, masks
data = Function(
function=get_niftis,
input_names=["subject", "data_dir", "sink_dir", "run", "masks"],
output_names=[
"t1", "example_func", "standard", "mni2t1", "t12epi", "masks"
],
)
data.inputs.data_dir = data_dir
data.inputs.sink_dir = sink_dir
data.inputs.subject = subject
data.inputs.masks = masks
data.inputs.run = run
grabber = data.run()
perf = FNIRT(output_type="NIFTI_GZ")
perf.inputs.warped_file = join(
sink_dir, subject, "{0}-{1}_mni-fnirt-t1.nii.gz".format(subject, run))
perf.inputs.affine_file = grabber.outputs.mni2t1
perf.inputs.in_file = grabber.outputs.standard
perf.inputs.subsampling_scheme = [8, 4, 2, 2]
perf.inputs.fieldcoeff_file = join(
sink_dir, subject,
"{0}-{1}_mni-fnirt-t1-warpcoef.nii.gz".format(subject, run))
perf.inputs.field_file = join(
sink_dir, subject,
"{0}-{1}_mni-fnirt-t1-warp.nii.gz".format(subject, run))
perf.inputs.ref_file = grabber.outputs.t1
reg2 = perf.run()
for i in np.arange(0, len(masks)):
# warp takes us from mni to t1, postmat
warp = ApplyWarp(interp="nn", abswarp=True)
warp.inputs.in_file = grabber.outputs.masks[i]
warp.inputs.ref_file = grabber.outputs.example_func
warp.inputs.field_file = reg2.outputs.field_file
warp.inputs.postmat = grabber.outputs.t12epi
warp.inputs.out_file = join(
sink_dir,
subject,
"{0}-{1}_{2}_retr.nii.gz".format(subject, run, mask_names[i]),
)
net_warp = warp.run()
return "yay"