def run_probtrackx2(i,
seeds_text,
dwi_dir,
probtrackx_output_dir_path,
procmem,
num_total_samples,
vent_CSF_diff_mask_path=None,
way_mask=None):
import random
import nipype.interfaces.fsl as fsl
samples_i = int(round(float(num_total_samples) / float(procmem[0]), 0))
nodif_brain_mask_path = "%s%s" % (dwi_dir, '/nodif_brain_mask.nii.gz')
merged_th_samples_path = "%s%s" % (dwi_dir, '/merged_th1samples.nii.gz')
merged_f_samples_path = "%s%s" % (dwi_dir, '/merged_f1samples.nii.gz')
merged_ph_samples_path = "%s%s" % (dwi_dir, '/merged_ph1samples.nii.gz')
tmp_dir = "%s%s%s" % (probtrackx_output_dir_path, '/tmp_samples_', str(i))
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
probtrackx2 = fsl.ProbTrackX2()
probtrackx2.inputs.network = True
probtrackx2.inputs.seed = seeds_text
probtrackx2.inputs.onewaycondition = True
probtrackx2.inputs.c_thresh = 0.2
probtrackx2.inputs.n_steps = 2000
probtrackx2.inputs.step_length = 0.5
probtrackx2.inputs.n_samples = samples_i
probtrackx2.inputs.dist_thresh = 0.0
probtrackx2.inputs.opd = True
probtrackx2.inputs.loop_check = True
probtrackx2.inputs.omatrix1 = False
probtrackx2.overwrite = True
probtrackx2.inputs.verbose = False
probtrackx2.inputs.mask = nodif_brain_mask_path
probtrackx2.inputs.out_dir = tmp_dir
probtrackx2.inputs.thsamples = merged_th_samples_path
probtrackx2.inputs.fsamples = merged_f_samples_path
probtrackx2.inputs.phsamples = merged_ph_samples_path
probtrackx2.inputs.use_anisotropy = False
if vent_CSF_diff_mask_path:
probtrackx2.inputs.avoid_mp = vent_CSF_diff_mask_path
else:
print('No ventricular CSF mask used. This is not recommended.')
if way_mask:
probtrackx2.inputs.waypoints = way_mask
probtrackx2.inputs.waycond = 'OR'
print(
'No waypointmask used. This will instantiate a computationally expensive probtrackx run and is generally not recommended.'
)
rseed_arg = ' --rseed=' + str(random.randint(1, 1000))
os.chdir(dwi_dir)
os.system(probtrackx2.cmdline + rseed_arg)
del probtrackx2
return
def run_probtrackx2(i, seeds_text, bedpostx_dir, probtrackx_output_dir_path,
vent_CSF_diff_mask_path, WM_diff_mask_path, procmem):
import random
import nipype.interfaces.fsl as fsl
num_total_samples = 5000
samples_i = int(round(float(num_total_samples) / float(procmem[0]), 0))
nodif_brain_mask_path = bedpostx_dir + '/nodif_brain_mask.nii.gz'
merged_th_samples_path = bedpostx_dir + '/merged_th1samples.nii.gz'
merged_f_samples_path = bedpostx_dir + '/merged_f1samples.nii.gz'
merged_ph_samples_path = bedpostx_dir + '/merged_ph1samples.nii.gz'
max_i = max(range(int(procmem[0])))
tmp_dir = probtrackx_output_dir_path + '/tmp_samples_' + str(i)
if not os.path.exists(tmp_dir):
os.makedirs(tmp_dir)
probtrackx2 = fsl.ProbTrackX2()
probtrackx2.inputs.network = True
probtrackx2.inputs.seed = seeds_text
probtrackx2.inputs.onewaycondition = True
probtrackx2.inputs.c_thresh = 0.2
probtrackx2.inputs.n_steps = 2000
probtrackx2.inputs.step_length = 0.5
probtrackx2.inputs.n_samples = samples_i
probtrackx2.inputs.dist_thresh = 0.0
probtrackx2.inputs.opd = True
probtrackx2.inputs.loop_check = True
probtrackx2.inputs.omatrix1 = True
probtrackx2.overwrite = True
probtrackx2.inputs.verbose = True
probtrackx2.inputs.mask = nodif_brain_mask_path
probtrackx2.inputs.out_dir = tmp_dir
probtrackx2.inputs.thsamples = merged_th_samples_path
probtrackx2.inputs.fsamples = merged_f_samples_path
probtrackx2.inputs.phsamples = merged_ph_samples_path
probtrackx2.inputs.use_anisotropy = True
try:
probtrackx2.inputs.avoid_mp = vent_CSF_diff_mask_path
except:
pass
try:
probtrackx2.inputs.waypoints = WM_diff_mask_path
probtrackx2.inputs.waycond = 'OR'
except:
pass
rseed_arg = ' --rseed=' + str(random.randint(1, 1000))
os.chdir(bedpostx_dir)
os.system(probtrackx2.cmdline + rseed_arg)
del (probtrackx2)
filename = probtrackx_output_dir_path + '/' + str(i) + '_complete.txt'
open(filename, 'w').close()
return max_i
def create_workflow(name='tracking'):
from nipype.workflows.dmri.fsl import create_bedpostx_pipeline
import nipype.interfaces.fsl as fsl
import nipype.pipeline.engine as pe
import nipype.interfaces.utility as niu
wf = pe.Workflow(name=name)
bed_wf = create_bedpostx_pipeline()
inputspec = pe.Node(niu.IdentityInterface(fields=[
'dwi', 'mask', 'reg', 'mean', 'bvecs', 'bvals', "subject_id",
"surf_dir"
]),
name="inputspec")
outputspec = pe.Node(niu.IdentityInterface(
fields=['fdt_paths', 'log', 'particle_files', 'targets', 'way_total']),
name='outputspec')
wf.connect(inputspec, 'dwi', bed_wf, 'inputnode.dwi')
wf.connect(inputspec, 'mask', bed_wf, 'inputnode.mask')
wf.connect(inputspec, 'bvecs', bed_wf, 'inputnode.bvecs')
wf.connect(inputspec, 'bvals', bed_wf, 'inputnode.bvals')
try:
prob2 = fsl.ProbTrackX2(verbose=2)
except AttributeError:
prob2 = fsl.ProbTrackX(verbose=2)
#prob2._cmd='probtrackx2'
#prob2.inputs.mode = Undefined
track = pe.MapNode(prob2, name='probtrackx', iterfield=["seed"])
wf.connect(bed_wf, 'outputnode.thsamples', track, 'thsamples')
wf.connect(bed_wf, 'outputnode.phsamples', track, 'phsamples')
wf.connect(bed_wf, 'outputnode.fsamples', track, 'fsamples')
wf.connect(inputspec, 'mask', track, 'mask')
regions = get_regions()
wf.connect(inputspec, "subject_id", regions, "inputspec.subject_id")
wf.connect(inputspec, "surf_dir", regions, "inputspec.surf_dir")
wf.connect(inputspec, "reg", regions, "inputspec.reg_file")
wf.connect(inputspec, "mean", regions, "inputspec.mean")
wf.connect(regions, "outputspec.ROIs", track, "seed")
wf.connect(regions, "outputspec.ROIs", track, "target_masks")
wf.connect(track, 'fdt_paths', outputspec, 'fdt_paths')
wf.connect(track, 'log', outputspec, 'log')
wf.connect(track, 'particle_files', outputspec, 'particle_files')
wf.connect(track, 'targets', outputspec, 'targets')
wf.connect(track, 'way_total', outputspec, 'way_total')
return wf
grad_dev = os.path.join(datadir, 'Diffusion_7T', 'grad_dev.nii.gz')
brain_mask = os.path.join(datadir, 'Diffusion_7T', 'nodif_brain_mask.nii.gz')
seed = os.path.join(datadir, 'Diffusion_7T', 'IC_L_sphere_bin.nii.gz')
from nipype.interfaces import fsl
bedp = pe.Node(fsl.BEDPOSTX5(), name='bedpost')
bedp.inputs.bvecs = bvecs
bedp.inputs.bvals = bvals
bedp.inputs.dwi = dwi
bedp.inputs.mask = brain_mask
bedp.inputs.grad_dev = grad_dev
bedp.inputs.n_fibres = 1
bedp.inputs.use_gpu = True
bedp.inputs.out_dir = os.path.join(datadir, 'Diffusion_7T.bedpostX')
from nipype.interfaces import fsl
pbx2 = pe.Node(fsl.ProbTrackX2(), name='probtrackx')
pbx2.inputs.seed = seed
pbx2.inputs.mask = brain_mask
pbx2.inputs.out_dir = os.path.join(datadir, 'Diffusion_7T.probtrackx2')
wf = pe.Workflow(name='fdt')
wf.connect([(bedp, pbx2, [('merged_fsamples', 'fsamples'),
('merged_phsamples', 'phsamples'),
('merged_thsamples', 'thsamples')])])
wf.run(plugin='SLURM',
plugin_args='--gres=gpu:1 --time=18:00:00 --qos=gablab --mem=40G -c 4')
name='cvtwarp_dti_to_mni')
stout_tractography.connect(dti_datasource, 'nodif_brain_mask',
cvtwarp_dti_to_mni, 'reference')
stout_tractography.connect(dti_datasource, 'MNI_to_struct', cvtwarp_dti_to_mni,
'warp1')
stout_tractography.connect(dti_datasource, 'struct_to_nodif_aff',
cvtwarp_dti_to_mni, 'postmat')
# $statement .= " --ref=$WORKINGDATAPATH/" . $subj[$i] . "/DTI/data/nodif_brain_mask.nii.gz ";
# $statement .= " --warp1=$WORKINGDATAPATH/" . $subj[$i] . "/xfms/MNI_warp_struc_warpfield.nii.gz ";
# $statement .= " --postmat=$WORKINGDATAPATH/" . $subj[$i] . "/xfms/struc_12dof_nodif.mat ";
# $statement .= " --out=$WORKINGDATAPATH/" . $subj[$i] . "/xfms/MNI_warp_struc_12dof_nodif_warpfield.nii.gz ";
#PERFORM PROBALISTIC TRACTOGRAPHY
pbx2 = pe.Node(interface=fsl.ProbTrackX2(), name='pbx2')
pbx2.inputs.c_thresh = 0.2
pbx2.inputs.n_steps = 2000
pbx2.inputs.step_length = 0.5
pbx2.inputs.n_samples = 5000
pbx2.inputs.opd = True
pbx2.inputs.loop_check = True
pbx2.inputs.omatrix2 = True ##This requires I include target2
pbx2.inputs.correct_path_distribution = True ##corresponds to the --pd flag
pbx2.inputs.onewaycondition = True
pbx2.inputs.target2 = os.path.join(
RAWDATAPATH, "MNI152_T1_1mm_brain_mask_downsample_2.nii.gz")
pbx2.inputs.rand_fib = 0
### This will eventually become an iterable
pbx2.inputs.seed = os.path.join(RAWDATAPATH, "ROIs/Human_IFG-vPrCG_L.nii.gz")
def run_struct_mapping(FSLDIR, ID, bedpostx_dir, dir_path, NETWORK, coords_MNI,
node_size, atlas_select, atlas_name, label_names,
plot_switch):
edge_threshold = 0.90
connectome_fdt_thresh = 1000
####Auto-set INPUTS####
nodif_brain_mask_path = bedpostx_dir + '/nodif_brain_mask.nii.gz'
merged_th_samples_path = bedpostx_dir + '/merged_th1samples.nii.gz'
merged_f_samples_path = bedpostx_dir + '/merged_f1samples.nii.gz'
merged_ph_samples_path = bedpostx_dir + '/merged_ph1samples.nii.gz'
input_MNI = FSLDIR + '/data/standard/MNI152_T1_2mm_brain.nii.gz'
probtrackx_output_dir_path = bedpostx_dir + '/probtrackx_' + NETWORK
####Auto-set INPUTS####
##Delete any existing roi spheres
del_files_spheres = glob.glob(bedpostx_dir + '/roi_sphere*diff.nii.gz')
try:
for i in del_files_spheres:
os.remove(i)
except:
pass
##Create transform matrix between diff and MNI using FLIRT
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'),
name='coregister')
flirt.inputs.reference = merged_f_samples_path
flirt.inputs.in_file = input_MNI
flirt.inputs.out_matrix_file = bedpostx_dir + '/xfms/MNI2diff.mat'
flirt.run()
##Apply transform between diff and MNI using FLIRT
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'),
name='coregister')
flirt.inputs.reference = merged_f_samples_path
flirt.inputs.in_file = input_MNI
flirt.inputs.apply_xfm = True
flirt.inputs.in_matrix_file = bedpostx_dir + '/xfms/MNI2diff.mat'
flirt.inputs.out_file = bedpostx_dir + '/xfms/MNI2diff_affine.nii.gz'
flirt.run()
x_vox = np.diagonal(
masking._load_mask_img(nodif_brain_mask_path)[1][:3, 0:3])[0]
y_vox = np.diagonal(
masking._load_mask_img(nodif_brain_mask_path)[1][:3, 0:3])[1]
z_vox = np.diagonal(
masking._load_mask_img(nodif_brain_mask_path)[1][:3, 0:3])[2]
def mmToVox(mmcoords):
voxcoords = ['', '', '']
voxcoords[0] = int((round(int(mmcoords[0]) / x_vox)) + 45)
voxcoords[1] = int((round(int(mmcoords[1]) / y_vox)) + 63)
voxcoords[2] = int((round(int(mmcoords[2]) / z_vox)) + 36)
return voxcoords
##Convert coords back to voxels
coords_vox = []
for coord in coords_MNI:
coords_vox.append(mmToVox(coord))
coords = list(tuple(x) for x in coords_vox)
j = 0
for i in coords:
##Grow spheres at ROI
X = coords[j][0]
Y = coords[j][1]
Z = coords[j][2]
out_file1 = bedpostx_dir + '/roi_point_' + str(j) + '.nii.gz'
args = '-mul 0 -add 1 -roi ' + str(X) + ' 1 ' + str(Y) + ' 1 ' + str(
Z) + ' 1 0 1'
maths = fsl.ImageMaths(in_file=input_MNI,
op_string=args,
out_file=out_file1)
os.system(maths.cmdline + ' -odt float')
out_file2 = bedpostx_dir + '/roi_sphere_' + str(j) + '.nii.gz'
args = '-kernel sphere ' + str(node_size) + ' -fmean -bin'
maths = fsl.ImageMaths(in_file=out_file1,
op_string=args,
out_file=out_file2)
os.system(maths.cmdline + ' -odt float')
##Map ROIs from Standard Space to diffusion Space:
##Applying xfm and input matrix to transform ROI's between diff and MNI using FLIRT,
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'),
name='coregister')
flirt.inputs.reference = nodif_brain_mask_path
flirt.inputs.in_file = out_file2
out_file_diff = out_file2.split('.nii')[0] + '_diff.nii.gz'
flirt.inputs.out_file = out_file_diff
flirt.inputs.apply_xfm = True
flirt.inputs.in_matrix_file = bedpostx_dir + '/xfms/MNI2diff.mat'
flirt.run()
j = j + 1
if not os.path.exists(probtrackx_output_dir_path):
os.makedirs(probtrackx_output_dir_path)
seed_files = glob.glob(bedpostx_dir + '/*diff.nii.gz')
seeds_text = probtrackx_output_dir_path + '/masks.txt'
try:
os.remove(seeds_text)
except OSError:
pass
seeds_file_list = []
for seed_file in seed_files:
seeds_file_list.append(seed_file)
f = open(seeds_text, 'w')
l1 = map(lambda x: x + '\n', seeds_file_list)
f.writelines(l1)
f.close()
del_files_points = glob.glob(bedpostx_dir + '/roi_point*.nii.gz')
for i in del_files_points:
os.remove(i)
del_files_spheres = glob.glob(bedpostx_dir + '/roi_sphere*[!diff].nii.gz')
for i in del_files_spheres:
os.remove(i)
mx_path = dir_path + '/' + str(ID) + '_' + NETWORK + '_structural_mx.txt'
probtrackx2 = pe.Node(interface=fsl.ProbTrackX2(), name='probtrackx2')
probtrackx2.inputs.network = True
probtrackx2.inputs.seed = seeds_text
probtrackx2.inputs.onewaycondition = True
probtrackx2.inputs.c_thresh = 0.2
probtrackx2.inputs.n_steps = 2000
probtrackx2.inputs.step_length = 0.5
probtrackx2.inputs.n_samples = 5000
probtrackx2.inputs.dist_thresh = 0.0
probtrackx2.inputs.opd = True
probtrackx2.inputs.loop_check = True
probtrackx2.inputs.omatrix1 = True
probtrackx2.overwrite = True
probtrackx2.inputs.verbose = True
probtrackx2.inputs.mask = nodif_brain_mask_path
probtrackx2.inputs.out_dir = probtrackx_output_dir_path
probtrackx2.inputs.thsamples = merged_th_samples_path
probtrackx2.inputs.fsamples = merged_f_samples_path
probtrackx2.inputs.phsamples = merged_ph_samples_path
probtrackx2.iterables = ("seed", seed_files)
try:
probtrackx2.inputs.avoid_mp = vetricular_CSF_mask_path
except:
pass
probtrackx2.run()
del (probtrackx2)
if os.path.exists(probtrackx_output_dir_path + '/fdt_network_matrix'):
mx = np.genfromtxt(probtrackx_output_dir_path + '/fdt_network_matrix')
waytotal = np.genfromtxt(probtrackx_output_dir_path + '/waytotal')
np.seterr(divide='ignore', invalid='ignore')
conn_matrix = np.divide(mx, waytotal)
conn_matrix[np.isnan(conn_matrix)] = 0
conn_matrix = np.nan_to_num(conn_matrix)
conn_matrix = normalize(conn_matrix)
##Save matrix
out_path_mx = dir_path + '/' + str(
ID) + '_' + NETWORK + '_structural_mx.txt'
np.savetxt(out_path_mx, conn_matrix, delimiter='\t')
if plot_switch == True:
rois_num = conn_matrix.shape[0]
print("Creating plot of dimensions:\n" + str(rois_num) + ' x ' +
str(rois_num))
plt.figure(figsize=(10, 10))
plt.imshow(conn_matrix,
interpolation="nearest",
vmax=1,
vmin=-1,
cmap=plt.cm.RdBu_r)
##And display the labels
plt.colorbar()
plt.title(atlas_select.upper() + ' ' + NETWORK +
' Structural Connectivity')
out_path_fig = dir_path + '/' + str(
ID) + '_' + NETWORK + '_structural_adj_mat.png'
plt.savefig(out_path_fig)
plt.close()
conn_matrix_symm = np.maximum(conn_matrix, conn_matrix.transpose())
fdt_paths_loc = probtrackx_output_dir_path + '/fdt_paths.nii.gz'
##Plotting with glass brain
##Create transform matrix between diff and MNI using FLIRT
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'),
name='coregister')
flirt.inputs.reference = input_MNI
flirt.inputs.in_file = nodif_brain_mask_path
flirt.inputs.out_matrix_file = bedpostx_dir + '/xfms/diff2MNI.mat'
flirt.run()
##Apply transform between diff and MNI using FLIRT
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'),
name='coregister')
flirt.inputs.reference = input_MNI
flirt.inputs.in_file = nodif_brain_mask_path
flirt.inputs.apply_xfm = True
flirt.inputs.in_matrix_file = bedpostx_dir + '/xfms/diff2MNI.mat'
flirt.inputs.out_file = bedpostx_dir + '/xfms/diff2MNI_affine.nii.gz'
flirt.run()
flirt = pe.Node(interface=fsl.FLIRT(cost_func='mutualinfo'),
name='coregister')
flirt.inputs.reference = input_MNI
flirt.inputs.in_file = fdt_paths_loc
out_file_MNI = fdt_paths_loc.split('.nii')[0] + '_MNI.nii.gz'
flirt.inputs.out_file = out_file_MNI
flirt.inputs.apply_xfm = True
flirt.inputs.in_matrix_file = bedpostx_dir + '/xfms/diff2MNI.mat'
flirt.run()
fdt_paths_MNI_loc = probtrackx_output_dir_path + '/fdt_paths_MNI.nii.gz'
if plot_switch == True:
norm = colors.Normalize(vmin=-1, vmax=1)
clust_pal = sns.color_palette("Blues_r", 4)
clust_colors = colors.to_rgba_array(clust_pal)
connectome = plotting.plot_connectome(
conn_matrix_symm,
coords_MNI,
edge_threshold=edge_threshold,
node_color=clust_colors,
edge_cmap=plotting.cm.black_blue_r)
connectome.add_overlay(img=fdt_paths_MNI_loc,
threshold=connectome_fdt_thresh,
cmap=plotting.cm.cyan_copper_r)
out_file_path = dir_path + '/structural_connectome_fig_' + NETWORK + '_' + str(
ID) + '.png'
plt.savefig(out_file_path)
plt.close()
from pynets import plotting as pynplot
NETWORK = NETWORK + '_structural'
pynplot.plot_connectogram(conn_matrix, conn_model, atlas_name,
dir_path, ID, NETWORK, label_names)
if NETWORK != None:
est_path = dir_path + '/' + ID + '_' + NETWORK + '_structural_est.txt'
else:
est_path = dir_path + '/' + ID + '_structural_est.txt'
try:
np.savetxt(est_path, conn_matrix_symm, delimiter='\t')
except RuntimeError:
print('Diffusion network connectome failed!')
return (est_path)
thsamples='%s/T1w/Diffusion.bedpostX/merged_%s.nii*',
fsamples='%s/T1w/Diffusion.bedpostX/merged_%s.nii*',
phsamples='%s/T1w/Diffusion.bedpostX/merged_%s.nii*',
nodif_brain_mask='%s/T1w/Diffusion.bedpostX/%s.nii*',
mniROIs='addlInfoV2/subject/%s/DTI_ROIs/Human_*_trans.nii.gz'
)
datasource.inputs.template_args = dict(
thsamples = [['subject_id','th1samples']],
phsamples = [['subject_id','ph1samples']],
fsamples = [['subject_id','f1samples']],
nodif_brain_mask = [['subject_id','nodif_brain_mask']],
mniROIs=[['subject_id']]
)
pbx2 = pe.MapNode(interface=fsl.ProbTrackX2(), name='pbx2', iterfield=['seed'])
pbx2.inputs.c_thresh = 0.2 # -c 0.2 F cutoff
pbx2.inputs.n_steps = 2000 # -S 2000
pbx2.inputs.step_length = 0.5 # --steplength=0.5
pbx2.inputs.n_samples = 25000 # -P 5000
pbx2.inputs.opd = True
pbx2.inputs.loop_check = True
pbx2.inputs.correct_path_distribution = True # -pd i.e. distance correction
#runpbx.connect(subj_infosource,'subject_id',datasource,'subject_id')
runpbx2 = pe.Workflow(name="runpbx2_gpu_dtispace_fixedwarps")
runpbx2.base_dir = "/data/NipypeScratch/"
samples_base_name_fxn = lambda x : x.replace('_th1samples.nii.gz','')
def pbX_wf(subject_id, sink_directory, name='hcp_pbX'):
hcp_pbX_wf = pe.Workflow(name='hcp_pbX_wf')
#making all the keys for the dictionary
info = dict(merged_thsamples=[['subject_id', 'merged_th']],
merged_phsamples=[['subject_id', 'merged_ph']],
merged_fsamples=[['subject_id', 'merged_f']],
dmri_brain=[['subject_id', 'T1w_acpc_dc_restore_1.25']],
fs_brain=[['subject_id', 'T1w_acpc_dc']],
aparcaseg=[['subject_id', 'aparc+aseg']],
mask=[['subject_id', 'nodif_brain_mask']])
# Create a datasource node to get the dwi, bvecs, and bvals
#This uses the dictionary created above and inputs the keys from the dictionary
datasource = pe.Node(interface=nio.DataGrabber(infields=['subject_id'],
outfields=list(
info.keys())),
name='datasource')
datasource.inputs.template = '%s/%s'
datasource.inputs.subject_id = subject_id
datasource.inputs.base_directory = os.path.abspath('/home/data/hcp')
datasource.inputs.field_template = dict(
merged_thsamples=
'/home/data/madlab/data/mri/hcp/bedpostX/%s/hcpbpX/thsamples/%s*.nii.gz',
merged_phsamples=
'/home/data/madlab/data/mri/hcp/bedpostX/%s/hcpbpX/phsamples/%s*.nii.gz',
merged_fsamples=
'/home/data/madlab/data/mri/hcp/bedpostX/%s/hcpbpX/fsamples/%s*.nii.gz',
dmri_brain='/home/data/hcp/%s/T1w/%s.nii.gz',
fs_brain='/home/data/hcp/%s/T1w/%s.nii.gz',
aparcaseg='/home/data/hcp/%s/T1w/%s.nii.gz',
mask='/home/data/hcp/%s/T1w/Diffusion/%s.nii.gz')
datasource.inputs.template_args = info
datasource.inputs.sort_filelist = True
# Create a flirt node to calculate the dmri_brain to fs_brain xfm
#Basically creating a conversion from DWI space to Freesurfer space
dmri2fs_xfm = pe.Node(fsl.FLIRT(), name='dmri2fs_xfm')
dmri2fs_xfm.inputs.out_matrix_file = 'dmri_2_fs_xfm.mat'
hcp_pbX_wf.connect(datasource, 'dmri_brain', dmri2fs_xfm, 'in_file')
hcp_pbX_wf.connect(datasource, 'fs_brain', dmri2fs_xfm, 'reference')
# Create a convertxfm node to create inverse xfm of dmri2fs affine
# Basicaaly creating a conversion from freesurfer space to DWI space
invt_dmri2fs = pe.Node(fsl.ConvertXFM(), name='invt_dmri2fs')
invt_dmri2fs.inputs.invert_xfm = True
invt_dmri2fs.inputs.out_file = 'fs_2_dmri_xfm.mat'
hcp_pbX_wf.connect(dmri2fs_xfm, 'out_matrix_file', invt_dmri2fs, 'in_file')
# Extract thalamus seed masks from aparc+aseg.nii.gz file
# Here 10 is the left thalamus, and 49 is the right thalamus
thal_seed_mask = pe.MapNode(fs.Binarize(),
iterfield=['match', 'binary_file'],
name='thal_seed_mask')
#thal_seed_mask.inputs.subject_dir = 'aparcaseg'
thal_seed_mask.inputs.match = [[10], [49]]
thal_seed_mask.inputs.binary_file = ['lft_thal.nii.gz', 'rt_thal.nii.gz']
hcp_pbX_wf.connect(datasource, 'aparcaseg', thal_seed_mask, 'in_file')
#Next we need to avoid the ventricles by creating an -avoid_mask
#There are no left and right 3rd and 4th ventricle, so we are making one mask
avoid_mask = pe.Node(
fs.Binarize(),
#out_type='nii.gz',
name='avoid_mask')
#avoid_mask.inputs.subject_dir = 'aparcaseg'
avoid_mask.inputs.match = [
4, 14, 15, 43, 72
] #lft_lat_ven, 3rd_ven, 4th_ven, rgt_lat_ven, 5th_ven
avoid_mask.inputs.binary_file = 'ventricles.nii.gz'
hcp_pbX_wf.connect(datasource, 'aparcaseg', avoid_mask, 'in_file')
# Extract cortical target masks from aparc+aseg.nii.gz file
# The ".match" is the freesurfer label and the binary_file is the label/name
ctx_targ_mask = pe.MapNode(fs.Binarize(),
iterfield=['match', 'binary_file'],
name='ctx_targ_mask')
#ctx_targ_mask.inputs.subject_dir = 'aparcaseg'
ctx_targ_mask.inputs.match = [[1024], [1022],
[1003, 1028, 1027, 1012, 1019, 1020, 1032],
[1031, 1029, 1008],
[1009, 1015, 1033, 1035, 1034, 1030], [1011],
[1017], [1002], [1014], [1026], [1028],
[1023, 1025, 1010], [1005, 1013,
1021], [1007], [1006],
[1016], [17], [18], [26], [2024], [2022],
[2003, 2028, 2027, 2012, 2019, 2020, 2032],
[2031, 2029, 2008],
[2009, 2015, 2033, 2035, 2034, 2030], [2011],
[2017], [2002], [2014], [2026], [2028],
[2023, 2025, 2010], [2005, 2013, 2021],
[2007], [2006], [2016], [53], [54], [58]]
ctx_targ_mask.inputs.binary_file = [
'ctx_lh_precentral.nii.gz', 'ctx_lh_postcentral.nii.gz',
'ctx_lh_latfront.nii.gz', 'ctx_lh_parietal.nii.gz',
'ctx_lh_temporal.nii.gz', 'ctx_lh_occipital.nii.gz',
'ctx_lh_paracentral.nii.gz', 'ctx_lh_caudantcing.nii.gz',
'ctx_lh_medorbfront.nii.gz', 'ctx_lh_rostantcing.nii.gz',
'ctx_lh_superfront.nii.gz', 'ctx_lh_medpost.nii.gz',
'ctx_lh_medoccipital.nii.gz', 'ctx_lh_fusiform.nii.gz',
'ctx_lh_entorhinal.nii.gz', 'ctx_lh_parahippocampal.nii.gz',
'lh_hpc.nii.gz', 'lh_amy.nii.gz', 'lh_nacc.nii.gz',
'ctx_rh_precentral.nii.gz', 'ctx_rh_postcentral.nii.gz',
'ctx_rh_latfront.nii.gz', 'ctx_rh_parietal.nii.gz',
'ctx_rh_temporal.nii.gz', 'ctx_rh_occipital.nii.gz',
'ctx_rh_paracentral.nii.gz', 'ctx_rh_caudantcing.nii.gz',
'ctx_rh_medorbfront.nii.gz', 'ctx_rh_rostantcing.nii.gz',
'ctx_rh_superfront.nii.gz', 'ctx_rh_medpost.nii.gz',
'ctx_rh_medoccipital.nii.gz', 'ctx_rh_fusiform.nii.gz',
'ctx_rh_entorhinal.nii.gz', 'ctx_rh_parahippocampal.nii.gz',
'rh_hpc.nii.gz', 'rh_amy.nii.gz', 'rh_nacc.nii.gz'
]
hcp_pbX_wf.connect(datasource, 'aparcaseg', ctx_targ_mask, 'in_file')
# Create a flirt node to apply inverse transform to seeds
# Basically you convert the masks (seeds) that were in freesurfer space to the DWI space
seedxfm_fs2dmri = pe.MapNode(fsl.FLIRT(),
iterfield=['in_file'],
name='seedxfm_fs2dmri')
seedxfm_fs2dmri.inputs.apply_xfm = True
seedxfm_fs2dmri.inputs.interp = 'nearestneighbour'
hcp_pbX_wf.connect(thal_seed_mask, 'binary_file', seedxfm_fs2dmri,
'in_file')
hcp_pbX_wf.connect(datasource, 'dmri_brain', seedxfm_fs2dmri, 'reference')
hcp_pbX_wf.connect(invt_dmri2fs, 'out_file', seedxfm_fs2dmri,
'in_matrix_file')
# Create a flirt node to apply inverse transform to targets
# You do the same as the previous node, but to the target masks
targxfm_fs2dmri = pe.MapNode(fsl.FLIRT(),
iterfield=['in_file'],
name='targxfm_fs2dmri')
targxfm_fs2dmri.inputs.apply_xfm = True
targxfm_fs2dmri.inputs.interp = 'nearestneighbour'
hcp_pbX_wf.connect(ctx_targ_mask, 'binary_file', targxfm_fs2dmri,
'in_file')
hcp_pbX_wf.connect(datasource, 'dmri_brain', targxfm_fs2dmri, 'reference')
hcp_pbX_wf.connect(invt_dmri2fs, 'out_file', targxfm_fs2dmri,
'in_matrix_file')
#Apply the inverse transform for the avoid masks from freesurfer to DWI space
avoidmaskxfm_fs2dmri = pe.Node(fsl.FLIRT(), name='avoidmaskxfm_fs2dmri')
avoidmaskxfm_fs2dmri.inputs.apply_xfm = True
avoidmaskxfm_fs2dmri.inputs.interp = 'nearestneighbour'
hcp_pbX_wf.connect(avoid_mask, 'binary_file', avoidmaskxfm_fs2dmri,
'in_file')
hcp_pbX_wf.connect(datasource, 'dmri_brain', avoidmaskxfm_fs2dmri,
'reference')
hcp_pbX_wf.connect(invt_dmri2fs, 'out_file', avoidmaskxfm_fs2dmri,
'in_matrix_file')
# Compute motion regressors (save file with 1st and 2nd derivatives)
#make_targ_lists = pe.Node(util.Function(input_names=['in_files'],
# output_names='out_list',
# function=create_two_lists),
# name='make_targ_lists')
#hcp_pbX_wf.connect(targxfm_fs2dmri, 'out_file', make_targ_lists, 'in_files')
#PROBTRACKX NODE
pbx2 = pe.MapNode(
fsl.ProbTrackX2(),
iterfield=['seed',
'target_masks'], #Should I have included avoid_mp here?
name='pbx2')
pbx2.inputs.c_thresh = 0.2
pbx2.inputs.n_steps = 2000
pbx2.inputs.step_length = 0.5
pbx2.inputs.n_samples = 25000
pbx2.inputs.opd = True
pbx2.inputs.os2t = True
pbx2.inputs.loop_check = True
#pbx2.plugin_args = {'bsub_args': '-q PQ_madlab'} #old way new way below
pbx2.plugin_args = {
'sbatch_args':
('-p IB_40C_1.5T --qos pq_madlab --account iacc_madlab -N 1 -n 6')
}
hcp_pbX_wf.connect(datasource, 'merged_thsamples', pbx2, 'thsamples')
hcp_pbX_wf.connect(datasource, 'merged_phsamples', pbx2, 'phsamples')
hcp_pbX_wf.connect(datasource, 'merged_fsamples', pbx2, 'fsamples')
hcp_pbX_wf.connect(seedxfm_fs2dmri, 'out_file', pbx2, 'seed')
hcp_pbX_wf.connect(targxfm_fs2dmri, ('out_file', hemispherize), pbx2,
'target_masks')
#hcp_pbX_wf.connect(make_targ_lists, 'out_list', pbx2, 'target_masks')
hcp_pbX_wf.connect(avoidmaskxfm_fs2dmri, 'out_file', pbx2, 'avoid_mp')
hcp_pbX_wf.connect(datasource, 'mask', pbx2, 'mask')
# Create a findthebiggest node to do hard segmentation between
# seeds and targets
#basically this segments the seed region on the basis of outputs of probtrackX when classification targets are being used.
findthebiggest = pe.MapNode(fsl.FindTheBiggest(),
iterfield=['in_files'],
name='findthebiggest')
hcp_pbX_wf.connect(pbx2, 'targets', findthebiggest, 'in_files')
# Create a datasink node to save outputs.
datasink = pe.Node(interface=nio.DataSink(), name='datasink')
datasink.inputs.base_directory = os.path.abspath(sink_directory)
datasink.inputs.container = subject_id + '/' + 'thal_seed'
hcp_pbX_wf.connect(pbx2, 'log', datasink, 'hcpprobX.log')
hcp_pbX_wf.connect(pbx2, 'fdt_paths', datasink, 'hcpprobX.fdt')
hcp_pbX_wf.connect(pbx2, 'way_total', datasink, 'hcpprobX.waytotal')
hcp_pbX_wf.connect(pbx2, 'targets', datasink, 'hcpprobX.targets')
hcp_pbX_wf.connect(findthebiggest, 'out_file', datasink,
'hcpprobX.fbiggest.@biggestsegmentation')
#hcp_pbX_wf.connect(thal_seed_mask, 'binary_file', datasink, 'hcpprobX.thal_mask')
hcp_pbX_wf.connect(seedxfm_fs2dmri, 'out_file', datasink,
'hcpprobX.seed_masks')
#from seed_xsfm(out_file) to datasink "seed_files"
#do we need this - > emu_pbX_wf.connect(datasource, 'ref_b0', datasink, 'emuprobX.b0')
#do we need this - > emu_pbX_wf.connect(thal_seed_mask, 'binary_file', datasink, 'emuprobX.thal_mask')
return hcp_pbX_wf