def run_fast_ecm(nuisance_file, gm_mask, string):
if not os.path.isfile(os.path.join(subject_dir, 'FAST_ECM',string, 'zscore_fastECM.nii.gz')):
#create output dir
dir = os.path.join(subject_dir,'FAST_ECM', string)
mkdir_path(dir)
os.chdir(dir)
#copy nuisance file locally
shutil.copy(nuisance_file, './residual.nii.gz')
#gunzip
if not os.path.isfile('residual.nii'):
os.system('gunzip residual.nii.gz')
os.system('rm -rf residual.nii.gz')
# run Fast ECM
pproc = os.path.join(dir, 'residual.nii')
matlab_cmd = ['matlab', '-version', '8.2', '-nodesktop' ,'-nosplash' ,'-nojvm' ,'-r "fastECM(\'%s\', \'1\', \'1\', \'1\', \'20\', \'%s\') ; quit;"' %(pproc, gm_mask)]
print ' ... Running ECM'
subprocess.call(matlab_cmd)
def z_score_centrality(image, outname):
print ' ... z-scoring %s'%outname
# zscore fastECM image
std = commands.getoutput('fslstats %s -k %s -s | awk \'{print $1}\''%(image, group_gm_mask))
mean = commands.getoutput('fslstats %s -k %s -m | awk \'{print $1}\''%(image, group_gm_mask))
os.system('fslmaths %s -sub %s -div %s -mas %s %s'%(image, mean, std, group_gm_mask, outname))
z_score_centrality('residual_fastECM.nii', 'zscore_fastECM')
z_score_centrality('residual_degCM.nii' , 'zscore_degCM')
z_score_centrality('residual_normECM.nii', 'zscore_normECM')
z_score_centrality('residual_rankECM.nii', 'zscore_rankECM')
def create_svs_masks(voxel_name):
#matlab related defenitions.... check RDA2NII.m file
anatomical_dir = os.path.join(subject_workspace, 'anatomical_original')
T1Path = os.path.join(subject_workspace, 'anatomical_original' + '/')
T1Image = 'ANATOMICAL.nii'
svs_path = os.path.join(subject_workspace, 'svs_rda', voxel_name, 'met' + '/')
svs_file = '%s%s_%s_SUPPRESSED.rda' %(subject ,workspace_dir[-10:-9], voxel_name)
# output dir
mkdir_path(os.path.join(subject_workspace, 'svs_voxel_mask'))
mask_dir = os.path.join(subject_workspace, 'svs_voxel_mask')
#run matlab code to create registered mask from rda file
matlab_command = ['matlab', '--version', '8.2', '-nodesktop' ,'-nosplash' ,'-nojvm' ,'-r "RDA_TO_NIFTI(\'%s\', \'%s\', \'%s\', \'%s\') ; quit;"'
%(T1Path, T1Image, svs_path, svs_file)]
if not os.path.isfile(os.path.join(mask_dir, '%s%s_%s_RDA_MASK.nii' %(subject,workspace_dir[-10:-9], voxel_name))):
print '..... extracting geometry from RDA and creating mask for %s'%voxel_name
subprocess.call(matlab_command)
for file in os.listdir(anatomical_dir):
if 'rda' in file and '%s'%voxel_name in file:
shutil.move(os.path.join(anatomical_dir, file),
os.path.join(mask_dir, '%s%s_%s_RDA_MASK.nii' %(subject,workspace_dir[-10:-9], voxel_name)))
elif 'coord' in file:
shutil.move(os.path.join(anatomical_dir, file),
os.path.join(mask_dir, '%s%s_%s_RDA_coord.txt' %(subject,workspace_dir[-10:-9], voxel_name)))
else:
print '%s SVS mask already created..... moving on'%voxel_name
print '========================================================================================'
def dicom_convert(population, data_dir, workspace_dir):
print '#############################################################################'
print ''
print ' RUNNNING PROJECT %s %s' %(data_dir[12:19], workspace_dir[-8:])
print ''
print '#############################################################################'
count=0
for subject in population:
count +=1
print '====================================================================='
print '%s- DICOM CONVERSION for %s' %(count,subject)
# define dicom directory for each subject
dicom_dir = os.path.join(data_dir, subject, 'DICOM')
# define destination directory for NIFTI outputs
mkdir_path(os.path.join(workspace_dir, subject, 'anatomical_original'))
out_nifti_dir = str(os.path.join(workspace_dir, subject, 'anatomical_original'))
if not os.path.isfile(os.path.join(out_nifti_dir, 'ANATOMICAL.nii')):
# create a list of all dicoms with absolute paths for each file
dicom_list = []
for dicom in os.listdir(dicom_dir):
dicomstr = os.path.join(dicom_dir, dicom)
dicom_list.append(dicomstr)
# grab SeriesDescription and append T1 files to list
T1_list = []
print 'Reading dicom series descriptions'
for dicom in dicom_list:
try:
dcm_read = pydicom.read_file(dicom, force = True)
sequence = dcm_read.SeriesDescription
except AttributeError:
continue
if 'mp2rage_p3_602B_UNI_Images' in sequence:
T1_list.append(dicom)
# convert T1 anatomical to NIFTI with SPM
print 'Converting Dicom to Nifti for %s' %subject
spm_dicom_convert = spmu.DicomImport()
spm_dicom_convert.inputs.format = 'nii'
spm_dicom_convert.inputs.in_files = T1_list
spm_dicom_convert.inputs.output_dir = out_nifti_dir
spm_dicom_convert.run()
#rename output file
for file in os.listdir(out_nifti_dir):
if file.endswith('nii'):
os.rename(str(os.path.join(out_nifti_dir, file)),
str(os.path.join(out_nifti_dir, 'ANATOMICAL.nii')))
else:
print 'subject already processed.......moving on'
print '====================================================================='
print ''
def calc_ecm(population, workspace_dir):
# all_gm = []
#
# if not os.path.isfile(os.path.join(workspace_dir, 'GluConnectivity', 'COMBINED_GM_MASK.nii.gz')):
# print 'Creating Group GM mask'
# for subject in population:
#
# # input and output folders
# subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
# out_mask = os.path.join(workspace_dir, 'GluConnectivity', 'COMBINED_GM_MASK.nii.gz')
# MNI2mm_gm = os.path.join(subject_dir , 'anatomical_MNI2mm_tissue_gm/TISSUE_CLASS_1_GM_OPTIMIZED_resample_warp_thresh.nii.gz')
#
# if os.path.isfile(MNI2mm_gm):
# all_gm.append(MNI2mm_gm)
#
# maths_input = []
# for i in all_gm:
# x = '-add %s'%i
# maths_input.append(x)
#
# maths_string = ' '.join(maths_input)[5:]
# os.system('fslmaths %s -mul /usr/share/fsl/data/standard/MNI152_T1_2mm_brain_mask.nii.gz %s'%(maths_string, out_mask))
#
# out_mask_4mm = os.path.join(workspace_dir, 'GluConnectivity', 'COMBINED_GM_MASK_4mm.nii.gz')
# mni_4mm = '/SCR/ROI/brain_4mm.nii.gz'
# os.system('flirt -in %s -ref %s -out %s -applyisoxfm 4' %(out_mask, mni_4mm, out_mask_4mm ))
for subject in population:
print 'Running Subject %s'%subject
# input and output folders
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
outdir = os.path.join(subject_dir, 'ECM_LIPSIA')
mkdir_path(outdir)
os.chdir(outdir)
# TRANSFORM NATIVE IMAGE TO MNI2mm
mkdir_path( os.path.join(subject_dir , 'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp'))
pproc = os.path.join(subject_dir , 'functional_native_brain_preproc_FWHM_AROMA_residual_bp/bandpassed_demeaned_filtered.nii.gz')
pproc_2mm = os.path.join(subject_dir , 'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/pproc.nii.gz')
anat2mni = os.path.join(subject_dir , 'anatomical_MNI2mm_xfm/MP2RAGE_DESKULL_RPI_resample_ero_fieldwarp.nii.gz')
func2anat = os.path.join(subject_dir , 'functional_ANAT2mm_xfm/REST_calc_resample_corrected_volreg_maths_tstat_flirt.mat')
if not os.path.isfile(pproc_2mm):
if os.path.isfile(pproc):
print '... Warping to MNI'
os.system(' '.join([ 'applywarp',
'--in=' + pproc,
'--ref=' + '/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain.nii.gz',
'--out=' + pproc_2mm,
'--warp=' + anat2mni,
'--premat=' + func2anat]))
# Convert VISTA to NIFTI
pproc_2mmv = os.path.join(subject_dir , 'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/pproc.v')
if not os.path.isfile(pproc_2mmv):
print '...Converting NIFTI to VISTA.. make sure you are running this on telemann'
os.system('isisconv -in %s -out %s' %(pproc_2mm, pproc_2mmv))
def calc_ecm(population, workspace_dir):
for subject in population:
print '####################################'
print 'Running fast ECM Subject %s' % subject
#############################################################
#################### Input and output folders
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
outdir = os.path.join(subject_dir, 'FAST_ECM_SCRUBBED')
mkdir_path(outdir)
os.chdir(outdir)
pproc_2mm_scrubbed = os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_scrubbed.nii'
)
#############################################################
#################### Run Fast ECM
mask = '/SCR4/workspace/project_GluRest/OUT_DIR_A/GluConnectivity/GM2mm_bin.nii'
if os.path.isfile(pproc_2mm_scrubbed):
if not os.path.isfile(os.path.join(outdir, 'FAST_ECM.nii')):
matlab_cmd = [
'matlab', '-version', '8.2', '-nodesktop', '-nosplash',
'-nojvm',
'-r "fastECM(\'%s\', \'1\', \'1\', \'1\', \'20\', \'%s\') ; quit;"'
% (pproc_2mm_scrubbed, mask)
]
subprocess.call(matlab_cmd)
shutil.move(
os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_fastECM.nii'
), os.path.join(outdir, 'FAST_ECM.nii'))
shutil.move(
os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_rankECM.nii'
), os.path.join(outdir, 'RANK_ECM.nii'))
shutil.move(
os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_normECM.nii'
), os.path.join(outdir, 'NORM_ECM.nii'))
shutil.move(
os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_degCM.nii'
), os.path.join(outdir, 'DCM.nii'))
def run_randomise_baseline(population_1, population_2, workspace_dir):
print "#######################################"
for string in ecm_strings:
print "Running FSL-randomise on ECM maps = %s" % string[16:]
print ""
ecm_list_1 = []
ecm_list_2 = []
subs_1 = []
subs_2 = []
for subject in population_1:
ecm = os.path.join(workspace_dir, subject, "FAST_ECM/%s/zscore_fastECM.nii.gz" % string)
if os.path.isfile(ecm):
ecm_list_1.append(ecm)
subs_1.append(subject)
for subject in population_2:
ecm = os.path.join(workspace_dir, subject, "FAST_ECM/%s/zscore_fastECM.nii.gz" % string)
if os.path.isfile(ecm):
ecm_list_2.append(ecm)
subs_2.append(subject)
print "...Concatenating ECM maps into 4D"
# calculate group mean
out_dir = os.path.join(workspace_dir, "STATISTICS/ECM/%s" % string)
mkdir_path(out_dir)
os.chdir(out_dir)
if not os.path.isfile("ECM_all_concat.nii.gz"):
os.system("fslmerge -t ECM_controls_concat.nii.gz %s" % " ".join(ecm_list_1))
print "Controls n=%s" % len(subs_1), subs_1
os.system("fslmerge -t ECM_patients_concat.nii.gz %s" % " ".join(ecm_list_2))
print "Patients n=%s" % len(subs_2), subs_2
print ""
os.system("fslmaths ECM_controls_concat.nii.gz -Tmean ECM_controls_mean.nii.gz")
os.system("fslmaths ECM_patients_concat.nii.gz -Tmean ECM_patients_mean.nii.gz")
os.system("fslmerge -t ECM_all_concat.nii.gz ECM_controls_concat.nii.gz ECM_patients_concat.nii.gz")
print "...Running Non-paramteric permuation tests"
# Runs FSL Randomise - nonparametric permutation inference
# Two-Sample Unpaired T-test
group_gm_mask = "/SCR4/workspace/project_GluRest/OUT_DIR_A/GluConnectivity/GM2mm_bin.nii"
glm_mat = os.path.join(workspace_dir, "STATISTICS/ECM/GLM_baseline/randomise_baseline.mat")
glm_con = os.path.join(workspace_dir, "STATISTICS/ECM/GLM_baseline/randomise_baseline.con")
if not os.path.isfile("randomise_baseline_tfce_corrp_tstat2.nii.gz"):
os.system(
"randomise -i ECM_all_concat.nii.gz -o randomise_baseline -d %s -t %s -T -R -P -N --uncorrp -n 5000 -m %s "
% (glm_mat, glm_con, group_gm_mask)
)
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 calc_ecm(population, workspace_dir):
for subject in population:
print 'Running Subject %s' % subject
# input and output folders
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
outdir = os.path.join(subject_dir, 'ECM')
mkdir_path(outdir)
os.chdir(outdir)
#inputdata
pproc_4mm = os.path.join(
subject_dir,
'functional_MNI4mm_preproc_FWHM_AROMA_residual_high_pass/bandpassed_demeaned_filtered.nii.gz'
)
if os.path.isfile(pproc_4mm):
# bp 4mm pproc data
if not os.path.isfile(
os.path.join(subject_dir,
'ECM/bandpassed_demeaned_filtered.nii.gz')):
bp_file = bandpass_voxels(pproc_4mm, (0.01, 0.1))
MNI2mm_gm = os.path.join(
subject_dir,
'anatomical_MNI2mm_tissue_gm/TISSUE_CLASS_1_GM_OPTIMIZED_resample_warp_thresh.nii.gz'
)
MNI4mm_gm = os.path.join(subject_dir, 'ECM/mask_4mm.nii.gz')
MNI4mm_gm_bin = os.path.join(subject_dir,
'ECM/mask_4mm_bin.nii.gz')
os.system('flirt -in %s -ref %s -out %s -applyxfm' %
(MNI2mm_gm, pproc_4mm, MNI4mm_gm))
os.system('fslmaths %s -thr 0.3 -bin %s' %
(MNI4mm_gm, MNI4mm_gm_bin))
if not os.path.isfile(
os.path.join(
subject_dir,
'ECM/resting_state_graph/calculate_centrality/eigenvector_centrality_binarize.nii.gz'
)):
print '...computing ecm'
ecm_graph = create_resting_state_graphs()
ecm_graph.inputs.inputspec.method_option = 1
ecm_graph.inputs.inputspec.subject = bp_file
ecm_graph.inputs.inputspec.template = mask
ecm_graph.inputs.inputspec.threshold_option = 0
ecm_graph.inputs.inputspec.threshold = 0.001
ecm_graph.inputs.inputspec.weight_options = [True, True]
ecm_graph.base_dir = outdir
ecm_graph.run()
def make_r1_surf(population, workspace, freesurfer_dir):
for subject in population:
print '========================================================================================'
print '%s-Preprocessing T1MAPSfor %s' %(subject, subject)
# I/O
T1MAPS = os.path.join(workspace, subject, 'RAW', 'T1MAPS.nii.gz')
T1MGZ = os.path.join(freesurfer_dir, subject, 'mri', 'T1.mgz')
mask = os.path.join(workspace, subject, 'ANATOMICAL','ANATOMICAL_BRAIN_MASK.nii.gz')
t1_dir = mkdir_path(os.path.join(workspace, subject, 'T1MAPS'))
os.chdir(t1_dir)
if not os.path.isfile(os.path.join(t1_dir, 'R1.mgz')):
# Deskull
os.system('fslmaths %s -mul %s T1MAPS_brain.nii.gz '%(T1MAPS, mask))
# Swapdim
os.system('fslswapdim T1MAPS_brain LR SI PA T1MAPS_brain_rsp')
# get fs t1
os.system('mri_convert %s T1mgz.nii.gz'%T1MGZ)
# reg
os.system('flirt -in T1MAPS_brain_rsp -ref T1mgz -dof 6 -cost mutualinfo -out T1MAPS_fs -omat NATIVE2FS.mat')
#get reciprocal
os.system('fslmaths T1MAPS_fs -recip -mul 10000 R1')
os.system('mri_convert R1.nii.gz R1.mgz')
#mri_vol2surf --mov R1.mgz --regheader LZ050 --projfrac 0.2 0.4 0.1 --hemi --interp nearest --out depth2.mgh
#mri_surf2surf --s LZ050 --sval depth2.mgh --trgsubject fsaverage5 --tval depth2_fs5.mgh --fwhm 6 --hemi lh --cortex
proj_fracs = {'depth1': '0.0 0.2 0.1', 'depth2': '0.2 0.4 0.1','depth3': '0.4 0.6 0.1',
'depth4': '0.6 0.8 0.1', 'depth5': '0.8 1.0 0.1'}
fwhm = 6
# vol2surf iterate of five laminar layers
if not os.path.isfile(os.path.join(t1_dir, '%s_depth5_rh_R1.mgh' % subject)):
for hemi in ['lh', 'rh']:
for depth in proj_fracs.keys():
print hemi, proj_fracs
os.system('mri_vol2surf --mov R1.mgz --regheader %s --projfrac-avg %s --interp nearest --hemi %s '
'--out %s_%s_%s_R1.mgh '
%(subject, proj_fracs[depth], hemi,
subject, depth, hemi,
))
os.system('mri_surf2surf --s %s --sval %s_%s_%s_R1.mgh --trgsubject fsaverage5 '
'--tval %s_%s_%s_fs5_R1.mgh --hemi %s --noreshape --cortex --fwhm %s '
%(subject,
subject, depth, hemi,
subject, depth, hemi,
hemi,
fwhm,
))
def get_spectra(voxel_name, string1, string2, string3):
rda_met = []
rda_h2o = []
twix_met= []
twix_h2o= []
for root, dirs, files, in os.walk(subject_afs, topdown= False):
for name in files:
if 'SUPP' in name:
if string1 in name or string2 in name or string3 in name:
rda_met.append(os.path.join(root, name))
if 'HEAD' in name and 'REF' in name and 'SUPP' not in name and 'meas' not in name:
if string1 in name or string2 in name or string3 in name:
rda_h2o.append(os.path.join(root, name))
if 'meas' in name and 'rda' not in name:
if string1 in name or string2 in name or string3 in name:
if 'ref' not in name and 'head' not in name and 'body' not in name and 'HEAD' not in name:
twix_met.append(os.path.join(root, name))
elif 'ref' in name or 'REF' in name:
if 'body' not in name:
twix_h2o.append(os.path.join(root, name))
print rda_met
if rda_met is []:
print 'RDA metabolite data does not exist for subject %s' %subject
elif rda_h2o is []:
print 'RDA water data does not exist for subject %s' %subject
elif twix_met is []:
print 'TWIX metabolite data does not exist for subject %s' %subject
elif twix_h2o is []:
print 'TWIX water data does not exist for subject %s' %subject
mkdir_path(os.path.join(subject_workspace, 'svs_rda', voxel_name, 'met'))
mkdir_path(os.path.join(subject_workspace, 'svs_rda', voxel_name, 'h2o'))
mkdir_path(os.path.join(subject_workspace, 'svs_twix', voxel_name, voxel_name))
mkdir_path(os.path.join(subject_workspace, 'svs_twix', voxel_name, '%s_w'%voxel_name))
rda_met_dir = os.path.join(workspace_dir, subject, 'svs_rda', voxel_name, 'met')
rda_h2o_dir = os.path.join(workspace_dir, subject, 'svs_rda', voxel_name, 'h2o')
twx_met_dir = os.path.join(workspace_dir, subject, 'svs_twix', voxel_name, voxel_name)
twx_h2o_dir = os.path.join(workspace_dir, subject, 'svs_twix', voxel_name, '%s_w'%voxel_name)
shutil.copy(rda_met[0], os.path.join(rda_met_dir, '%s%s_%s_SUPPRESSED.rda' %(subject, workspace_dir[-10:-9], voxel_name)))
shutil.copy(rda_h2o[0], os.path.join(rda_h2o_dir, '%s%s_%s_WATER.rda' %(subject, workspace_dir[-10:-9], voxel_name)))
shutil.copy(twix_met[0], os.path.join(twx_met_dir, '%s%s_%s_SUPPRESSED_TWIX.dat' %(subject, workspace_dir[-10:-9], voxel_name)))
shutil.copy(twix_h2o[0], os.path.join(twx_h2o_dir, '%s%s_%s_WATER_TWIX.dat' %(subject,workspace_dir[-10:-9], voxel_name)))
print '.....done voxel %s'%voxel_name
def run_CanICA_NILEAN(output_dir, working_dir, population, n_components=20):
#create group ica outputdir
mkdir_path(os.path.join(working_dir, 'CANICA_GROUP_ICA'))
canica_dir = os.path.join(working_dir, 'CANICA_GROUP_ICA')
# grab subjects
preprocssed_all = []
for subject in population:
preprocssed_subject = os.path.join(output_dir, subject, 'xxxx.nii.gz')
preprocssed_all.append(preprocssed_subject)
canica = CanICA(n_components=n_components,
smoothing_fwhm=0.,
memory='nilearn_cashe',
memory_level=5,
threshold=3.,
verbose=10,
random_state=10)
canica.fit(preprocssed_all)
# save data
components_img = canica.masker_.inverse_transform(canica.components_)
components_img.to_filename(os.path.join(canica_dir, 'canica_IC.nii.gz'))
def run_CanICA_NILEAN(output_dir, working_dir, population, n_components = 20):
#create group ica outputdir
mkdir_path(os.path.join(working_dir, 'CANICA_GROUP_ICA'))
canica_dir = os.path.join(working_dir, 'CANICA_GROUP_ICA')
# grab subjects
preprocssed_all =[]
for subject in population:
preprocssed_subject = os.path.join(output_dir, subject, 'xxxx.nii.gz')
preprocssed_all.append(preprocssed_subject)
canica = CanICA(n_components=n_components,
smoothing_fwhm= 0.,
memory= 'nilearn_cashe',
memory_level= 5,
threshold = 3.,
verbose = 10,
random_state=10)
canica.fit(preprocssed_all)
# save data
components_img = canica.masker_.inverse_transform(canica.components_)
components_img.to_filename(os.path.join(canica_dir, 'canica_IC.nii.gz'))
def calc_ecm(population, workspace_dir):
for subject in population:
print 'Running Subject %s'%subject
# input and output folders
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
outdir = os.path.join(subject_dir, 'ECM')
mkdir_path(outdir)
os.chdir(outdir)
#inputdata
pproc_4mm = os.path.join(subject_dir , 'functional_MNI4mm_preproc_FWHM_AROMA_residual_high_pass/bandpassed_demeaned_filtered.nii.gz')
if os.path.isfile(pproc_4mm):
# bp 4mm pproc data
if not os.path.isfile(os.path.join(subject_dir, 'ECM/bandpassed_demeaned_filtered.nii.gz')):
bp_file = bandpass_voxels(pproc_4mm, (0.01,0.1))
MNI2mm_gm = os.path.join(subject_dir , 'anatomical_MNI2mm_tissue_gm/TISSUE_CLASS_1_GM_OPTIMIZED_resample_warp_thresh.nii.gz')
MNI4mm_gm = os.path.join(subject_dir , 'ECM/mask_4mm.nii.gz')
MNI4mm_gm_bin = os.path.join(subject_dir , 'ECM/mask_4mm_bin.nii.gz')
os.system('flirt -in %s -ref %s -out %s -applyxfm'%(MNI2mm_gm, pproc_4mm, MNI4mm_gm))
os.system('fslmaths %s -thr 0.3 -bin %s' %(MNI4mm_gm,MNI4mm_gm_bin))
if not os.path.isfile(os.path.join(subject_dir, 'ECM/resting_state_graph/calculate_centrality/eigenvector_centrality_binarize.nii.gz')):
print '...computing ecm'
ecm_graph = create_resting_state_graphs()
ecm_graph.inputs.inputspec.method_option = 1
ecm_graph.inputs.inputspec.subject = bp_file
ecm_graph.inputs.inputspec.template = mask
ecm_graph.inputs.inputspec.threshold_option = 0
ecm_graph.inputs.inputspec.threshold = 0.001
ecm_graph.inputs.inputspec.weight_options = [True, True]
ecm_graph.base_dir = outdir
ecm_graph.run()
def run_group_ica(output_dir, working_dir, population, pipeline_name):
print '#############################################################################'
print ''
print ' RUNNNING GROUP ICA'
print ''
print '#############################################################################'
#define ica outputdir
group_ica_dir = os.path.join(working_dir, 'MELODIC_GROUP_ICA')
mkdir_path(group_ica_dir)
# concatenate fmri preprocessed and high-pass filtered data (0.001hz) for group ICA
preprocssed_all =[]
for subject in population:
preprocssed_subject = os.path.join(output_dir, pipeline_name, subject, 'functional_MNI4mm_preproc_FWHM_AROMA_residual_high_pass/bandpassed_demeaned_filtered.nii.gz')
if os.path.isfile(preprocssed_subject):
preprocssed_all.append(preprocssed_subject)
else:
print 'subjects with missing data ',subject
data_4_melodic = ','.join(preprocssed_all)
#print data_4_melodic
def run_melodic(func, brain_mask, TR = 1.4, melodic_dir= group_ica_dir):
# Run MELODIC
#os.system('melodic --in=%s --outdir=%s --mask=%s -Ostats --nobet --mmthresh=0.5 --report --tr=%s' %(func, melodic_dir, brain_mask, str(TR)))
os.system(' '.join([ 'melodic',
'--in=' + func,
'--mask=' + brain_mask,
'-v',
'-d 25',
'--outdir=' + melodic_dir,
'--Ostats --nobet --mmthresh=0.5 --report',
'--tr=' + str(TR)]))
#if os.path.isfile(os.path.join(melodic_dir, 'melodic_IC.nii.gz')):
# Get number of components
melodic_4d = nb.load(os.path.join(melodic_dir, 'melodic_IC.nii.gz'))
n_componenets = melodic_4d.shape[3]
for n_componenet in range(1,n_componenets):
z_thresh = os.path.join(melodic_dir, 'stats/thresh_zstat%s.nii.gz'%n_componenet)
cmd = ' '.join(['fslinfo', z_thresh, '| grep dim4 | head -n1 | awk \'{print $2}\''])
z_thresh_dim4 = int(float(commands.getoutput(cmd)))
# Zero-pad the IC number and extract the 3D data........
# For cases where the mixture modeling does not converge, 2nd img in the 4th dimension wil be the results of the null hypothesis test.
cmd = ' '.join(['zeropad', str(n_componenet), '4'])
z_thresh_zeropad = os.path.join(melodic_dir,'thr_zstat' + commands.getoutput(cmd))
# Extract last spatial map within the thresh_zstat file
os.system('fslroi %s %s %s 1' %(z_thresh, z_thresh_zeropad, str(z_thresh_dim4-1)))
# Merge and subsequently remove all mixture modeled Z-maps within the output directory
z_thresh_zeropad_all = os.path.join(melodic_dir, 'thr_zstat????.nii.gz')
z_thresh_merged = os.path.join(melodic_dir, 'melodic_IC_thr.nii.gz')
os.system('fslmerge -t %s %s ' %(z_thresh_merged , z_thresh_zeropad_all))
os.system('rm ' + z_thresh_zeropad_all)
# Apply the mask to the merged file (in case a melodic-directory was predefined and run with a different mask)
os.system('fslmaths %s -mas %s %s' %(z_thresh_merged, brain_mask, z_thresh_merged))
run_melodic(data_4_melodic, mni_brain_mask_4mm, TR = 1.4, melodic_dir = group_ica_dir)
def quantiation_correction(population, workspace_dir, analysis_type):
print '#############################################################################'
print ''
print ' RUNNNING PROJECT NMR-093%s %s' % (
workspace_dir[-10:-9], workspace_dir[-8:])
print ''
print '#############################################################################'
# output dir
mkdir_path(os.path.join(workspace_dir[:-8], 'group_statistics'))
results_dir = os.path.join(workspace_dir[:-8], 'group_statistics')
def save_reliable_concentrations(population, workspace_dir, voxel_name,
analysis_type):
csv_list = []
for subject in population:
# get metabolite data for each subject and append to a list
csv = os.path.join(workspace_dir, subject,
'lcmodel_%s' % analysis_type, voxel_name,
'spreadsheet.csv')
if os.path.isfile(csv):
reader = pd.read_csv(csv)
reader.insert(0, 'Subject', subject)
csv_list.append(reader)
# creat a dataframe and place reliable metabolite data for every subject
df = pd.concat(csv_list, ignore_index=True)
reliable = df.loc[:, [
'Subject', ' Cre', ' Cre %SD', ' GPC+PCh', ' GPC+PCh %SD',
' NAA+NAAG', ' NAA+NAAG %SD', ' mI', ' mI %SD', ' Glu', ' Glu %SD',
' Gln', ' Gln %SD', ' Glu+Gln', ' Glu+Gln %SD'
]]
# sort subjects alphabetically and reset index....
reliable.sort(columns='Subject', inplace=True)
reliable.reset_index(drop=True, inplace=True)
# save reliable dataframe
reliable.to_csv(
os.path.join(
results_dir, 'lcmodel_%s_%s_%s_%s.csv' %
(analysis_type, voxel_name, workspace_dir[-8:],
workspace_dir[-10:-9])))
return reliable
print '1. Creating new dataframe with reliable LC-Model concentrations for ACC,THA,STR'
acc_reliable = save_reliable_concentrations(population, workspace_dir,
'ACC', analysis_type)
tha_reliable = save_reliable_concentrations(population, workspace_dir,
'THA', analysis_type)
str_reliable = save_reliable_concentrations(population, workspace_dir,
'STR', analysis_type)
def save_tissue_proportions(population, workspace_dir, voxel_name):
list_spm = []
for subject in population:
# grab tissue proportion data for all subjects and dump into list
spm = pd.read_csv(os.path.join(
workspace_dir, subject, 'svs_voxel_stats',
'%s_voxel_statistics_spm.txt' % voxel_name),
header=None)
spm.insert(0, 'SUBJECT', subject)
list_spm.append(spm)
# create concatenated dataframe for all tissue data in list
df_spm = pd.concat(list_spm, ignore_index=True)
df_spm.columns = [
'SUBJECT',
'%s_GM' % voxel_name,
'%s_WM' % voxel_name,
'%s_CSF' % voxel_name,
'%s_SUM' % voxel_name
]
df_spm.to_csv(
os.path.join(
results_dir, 'proportions_%s_%s_%s.csv' %
(voxel_name, workspace_dir[-8:], workspace_dir[-10:-9])))
return df_spm
print '2. Creating new dataframe with SPM tissue proportions for ACC,THA,STR'
acc_props = save_tissue_proportions(population, workspace_dir, 'ACC')
tha_prpos = save_tissue_proportions(population, workspace_dir, 'THA')
str_props = save_tissue_proportions(population, workspace_dir, 'STR')
def calc_asbolute(lcmodel, frac_gm, frac_wm, frac_csf):
import math
#lcmodel correction factor
factor = (55.55 / (35.88 * 0.7))
# relative water content in tissue.. determined experimentally.
alpha_gm = 0.81 # 0.78
alpha_wm = 0.71 # 0.65
alpha_csf = 1.0 # 1.0
#attentuation factor for water
R_H2O_GM = (1.0 - math.e**(-3000.0 / 1820.0)) * math.e**(-30.0 / 99.0)
R_H2O_WM = (1.0 - math.e**(-3000.0 / 1084.0)) * math.e**(-30.0 / 69.0)
R_H2O_CSF = (1.0 - math.e**(-3000.0 / 4163.0)) * math.e**(-30.0 /
503.0)
######### Correction Equations #######
# tissel equation
Cmet1 = (lcmodel * (((frac_csf * 1. * (1. - frac_csf)) +
(frac_gm * 0.81 + frac_wm * 0.71)) /
(1. - frac_csf)))
# gusseuw equation
Cmet2 = (lcmodel) * (
((frac_gm * alpha_gm * R_H2O_GM + frac_wm * alpha_wm * R_H2O_WM +
frac_csf * alpha_csf * R_H2O_CSF) /
(frac_gm * 1.0 + frac_wm * 1.0))) * factor
# gusseew csf equation
Cmet3 = (lcmodel) * (1 / (1 - frac_csf))
return Cmet2
def create_absolute_df(reliable, proportions, voxel_name):
cre = calc_asbolute(reliable[' Cre'],
proportions['%s_GM' % voxel_name],
proportions['%s_WM' % voxel_name],
proportions['%s_CSF' % voxel_name])
cho = calc_asbolute(reliable[' GPC+PCh'],
proportions['%s_GM' % voxel_name],
proportions['%s_WM' % voxel_name],
proportions['%s_CSF' % voxel_name])
naa = calc_asbolute(reliable[' NAA+NAAG'],
proportions['%s_GM' % voxel_name],
proportions['%s_WM' % voxel_name],
proportions['%s_CSF' % voxel_name])
ino = calc_asbolute(reliable[' mI'], proportions['%s_GM' % voxel_name],
proportions['%s_WM' % voxel_name],
proportions['%s_CSF' % voxel_name])
glu = calc_asbolute(reliable[' Glu'],
proportions['%s_GM' % voxel_name],
proportions['%s_WM' % voxel_name],
proportions['%s_CSF' % voxel_name])
gln = calc_asbolute(reliable[' Gln'],
proportions['%s_GM' % voxel_name],
proportions['%s_WM' % voxel_name],
proportions['%s_CSF' % voxel_name])
glx = calc_asbolute(reliable[' Glu+Gln'],
proportions['%s_GM' % voxel_name],
proportions['%s_WM' % voxel_name],
proportions['%s_CSF' % voxel_name])
absolute = pd.DataFrame({
'Subjects': reliable['Subject'],
'Cre': cre,
'GPC+PCh': cho,
'NAA+NAAG': naa,
'mI': ino,
'Glu': glu,
'Gln': gln,
'Glu+Gln': glx,
})
column_order = [
'Subjects', 'Cre', 'GPC+PCh', 'NAA+NAAG', 'mI', 'Glu', 'Gln',
'Glu+Gln'
]
absolute = absolute.reindex(columns=column_order)
absolute.to_csv(
os.path.join(
results_dir, 'absolute_%s_%s_%s_%s.csv' %
(analysis_type, voxel_name, workspace_dir[-8:],
workspace_dir[-10:-9])))
print '3. Creating new dataframe with Absolute Concentrations for ACC,THA,STR'
create_absolute_df(acc_reliable, acc_props, 'ACC')
create_absolute_df(tha_reliable, tha_prpos, 'THA')
create_absolute_df(str_reliable, str_props, 'STR')
def run_freesurfer_mask_connectivity(pop_name, population, freesurfer_dir, workspace_dir, mrs_datadir, ):
df = pd.DataFrame(index = [population], columns= columnx )
for subject in population:
print '####################### Subject %s' %subject
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
outdir = os.path.join(subject_dir, 'RSFC_CONNECTIVITY')
mkdir_path(outdir)
func_pproc = os.path.join(subject_dir, 'functional_native_brain_preproc_FWHM_AROMA_residual_bp/bandpassed_demeaned_filtered.nii.gz') # 2.3 mm
func_mean = os.path.join(subject_dir, 'functional_native_brain_preproc_mean/REST_calc_resample_corrected_volreg_maths_tstat.nii')
func_aroma = os.path.join(subject_dir, 'functional_native_brain_preproc_FWHM_AROMA/denoised_func_data_nonaggr.nii.gz')
func_gm = os.path.join(subject_dir, 'functional_native_gm/TISSUE_CLASS_1_GM_OPTIMIZED_resample_flirt_thresh_maths.nii.gz')
anat_func = os.path.join(subject_dir, 'anatomical_FUNC2mm_brain/MP2RAGE_DESKULL_RPI_resample_ero_flirt_flirt.nii.gz') # 2.3mm
anat_func_xfm = os.path.join(subject_dir, 'anatomical_FUNC2mm_xfm/REST_calc_resample_corrected_volreg_maths_tstat_flirt_inv.mat')
mni2natwarp = os.path.join(subject_dir, 'MNI2mm_ANAT_xfm/MP2RAGE_DESKULL_RPI_resample_ero_fieldwarp_inverse.nii.gz')
####################################### Grab ATAG masks #######################################
STN_LEFT = os.path.join(outdir, 'ATAG_STN_LEFT.nii.gz')
SN_LEFT = os.path.join(outdir, 'ATAG_SN_LEFT.nii.gz')
GPe_LEFT = os.path.join(outdir, 'ATAG_GPE_left.nii.gz')
GPi_LEFT = os.path.join(outdir, 'ATAG_GPi_left.nii.gz')
os.system('applywarp -i %s -r %s -w %s --postmat=%s -o %s' %(mni_stn_left_1mm, anat_func,mni2natwarp, anat_func_xfm, STN_LEFT ))
os.system('applywarp -i %s -r %s -w %s --postmat=%s -o %s' %(mni_sn_left_1mm, anat_func,mni2natwarp, anat_func_xfm, SN_LEFT ))
os.system('fslmaths %s -bin %s' %(STN_LEFT, STN_LEFT ))
os.system('fslmaths %s -bin %s' %(SN_LEFT, SN_LEFT ))
####################################### Grab Subcortical masks #######################################
print '1. grabbing FIRST Subcortical masks'
STR = os.path.join(subject_dir, 'functional_subcortical', 'left_str.nii.gz')
CAUx= os.path.join(subject_dir, 'functional_subcortical', 'left_caudate.nii.gz')
PUT = os.path.join(subject_dir, 'functional_subcortical', 'left_putamen.nii.gz')
PAL = os.path.join(subject_dir, 'functional_subcortical', 'left_pallidum.nii.gz')
NAC = os.path.join(subject_dir, 'functional_subcortical', 'left_nacc.nii.gz')
HIP = os.path.join(subject_dir, 'functional_subcortical', 'left_hipoocampus.nii.gz')
AMG = os.path.join(subject_dir, 'functional_subcortical', 'left_amygdala.nii.gz')
THA = os.path.join(subject_dir, 'functional_subcortical', 'thalamus.nii.gz')
lTHA = os.path.join(subject_dir, 'functional_subcortical', 'left_thalamus.nii.gz')
rTHA = os.path.join(subject_dir, 'functional_subcortical', 'right_thalamus.nii.gz')
####################################### Fill Caudate Holes #######################################
CAU = os.path.join(subject_dir, 'functional_subcortical', 'left_caudate_fill.nii.gz')
if not os.path.isfile(CAU):
os.system('fslmaths %s -fillh %s' %(CAUx, CAU))
####################################### Grab svs masks #######################################
print '2. grabbing SVS masks'
svs_acc_src = os.path.join(mrs_datadir, pop_name, subject, 'svs_voxel_mask', '%s%s_ACC_RDA_MASK.nii' %(subject, mrs_datadir[-1]))
svs_tha_src = os.path.join(mrs_datadir, pop_name, subject, 'svs_voxel_mask', '%s%s_THA_RDA_MASK.nii' %(subject, mrs_datadir[-1]))
svs_str_src = os.path.join(mrs_datadir, pop_name, subject, 'svs_voxel_mask', '%s%s_STR_RDA_MASK.nii' %(subject, mrs_datadir[-1]))
svs_acc = os.path.join(outdir, 'svs_acc.nii.gz')
svs_tha = os.path.join(outdir, 'svs_tha.nii.gz')
svs_str = os.path.join(outdir, 'svs_str.nii.gz')
svs_acc_func = os.path.join(outdir, 'svs_acc_func.nii.gz')
svs_tha_func = os.path.join(outdir, 'svs_tha_func.nii.gz')
svs_str_func = os.path.join(outdir, 'svs_str_func.nii.gz')
if not os.path.isfile(svs_acc_func):
os.system('fslswapdim %s RL PA IS %s' %(svs_acc_src, svs_acc))
os.system('fslswapdim %s RL PA IS %s' %(svs_tha_src, svs_tha))
os.system('fslswapdim %s RL PA IS %s' %(svs_str_src, svs_str))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %(svs_acc, anat_func, anat_func_xfm, svs_acc_func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %(svs_tha, anat_func, anat_func_xfm, svs_tha_func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %(svs_str, anat_func, anat_func_xfm, svs_str_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %(svs_acc_func, svs_acc_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %(svs_tha_func, svs_tha_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %(svs_str_func, svs_str_func))
####################################### Grab freesurfer masks #######################################
print '3. grabbing Freesurfer masks'
os.system('export SUBJECTS_DIR=%s'%(freesurfer_dir))
t1mgz = os.path.join(freesurfer_dir, subject, 'mri', 'T1.mgz')
segmgz = os.path.join(freesurfer_dir, subject, 'mri', 'aparc.a2009s+aseg.mgz')
t1nii = os.path.join(outdir, 'freesurfer_T1.nii.gz')
segnii = os.path.join(outdir, 'freesurfer_seg.nii.gz')
fs_la_acc = os.path.join(outdir, 'freesurfer_seg_la_MCC_11107.nii.gz') # 11107 ctx_lh_G_and_S_cingul-Mid-Ant
fs_ra_acc = os.path.join(outdir, 'freesurfer_seg_ra_MCC_12107.nii.gz') # 12107 ctx_lh_G_and_S_cingul-Mid-Ant
fs_acc = os.path.join(outdir, 'freesurfer_seg_aMCC_11107_12107.nii.gz')
fs_la_insula = os.path.join(outdir, 'freesurfer_seg_la_INS_11148.nii.gz') # 11148 ctx_lh_S_circular_insula_ant
fs_ra_insula = os.path.join(outdir, 'freesurfer_seg_ra_INS_12148.nii.gz') # 12148 ctx_lh_S_circular_insula_ant
if not os.path.isfile(fs_acc):
os.system('mri_convert %s %s' %(t1mgz, t1nii))
os.system('mri_convert %s %s' %(segmgz, segnii))
os.system('fslmaths %s -thr 11107 -uthr 11107 %s ' %(segnii, fs_la_acc))
os.system('fslmaths %s -thr 12107 -uthr 12107 %s ' %(segnii, fs_ra_acc))
os.system('fslmaths %s -add %s -dilM -bin %s' %(fs_la_acc, fs_ra_acc, fs_acc))
os.system('fslmaths %s -thr 11148 -uthr 11148 -dilM -bin %s' %(segnii, fs_la_insula))
os.system('fslmaths %s -thr 12148 -uthr 12148 -dilM -bin %s' %(segnii, fs_ra_insula))
labels_dir = os.path.join(freesurfer_dir, subject, 'label')
fs_ba6_rh = os.path.join(outdir, 'freesurfer_seg_SMA_BA6_rh.nii.gz')
fs_ba6_lh = os.path.join(outdir, 'freesurfer_seg_SMA_BA6_lh.nii.gz')
fs_sma = os.path.join(outdir, 'freesurfer_seg_SMA_BA6.nii.gz')
if not os.path.isfile(fs_sma):
os.system('mri_label2vol --label %s/rh.BA6.thresh.label --subject %s --temp %s --regheader %s --o %s' %(labels_dir, subject,t1mgz, t1mgz,fs_ba6_rh))
os.system('mri_label2vol --label %s/lh.BA6.thresh.label --subject %s --temp %s --regheader %s --o %s' %(labels_dir, subject,t1mgz, t1mgz,fs_ba6_lh))
os.system('fslmaths %s -add %s -dilM -dilM %s' %(fs_ba6_rh,fs_ba6_lh, fs_sma))
####################################### TRANSFORM Freesurfer masks to native func space #######################################
print '4. Transforming Freesurfer masks to native func space'
t1nii_rpi = os.path.join(outdir, 'freesurfer_T1_RPI.nii.gz')
fs_acc_rpi = os.path.join(outdir, 'freesurfer_seg_aMCC_11107_12107_RPI.nii.gz')
fs_la_insula_rpi = os.path.join(outdir, 'freesurfer_seg_la_INS_11148_RPI.nii.gz')
fs_ra_insula_rpi = os.path.join(outdir, 'freesurfer_seg_ra_INS_12148_RPI.nii.gz')
fs_sma_rpi = os.path.join(outdir, 'freesurfer_seg_SMA_BA6_RPI.nii.gz')
fst1omat = os.path.join(outdir, 'freesurfer2func.mat')
fst1func = os.path.join(outdir, 'freesurfer_T1_func.nii.gz')
fs_acc_func = os.path.join(outdir, 'freesurfer_seg_aMCC_11107_12107_func.nii.gz')
fs_la_insula_func = os.path.join(outdir, 'freesurfer_seg_la_INS_11148_func.nii.gz')
fs_ra_insula_func = os.path.join(outdir, 'freesurfer_seg_ra_INS_11148_func.nii.gz')
fs_sma_func = os.path.join(outdir, 'freesurfer_seg_SMA_BA6_func.nii.gz')
if not os.path.isfile(t1nii_rpi):
os.system('fslswapdim %s RL PA IS %s' %(t1nii, t1nii_rpi))
os.system('fslswapdim %s RL PA IS %s' %(fs_acc, fs_acc_rpi))
os.system('fslswapdim %s RL PA IS %s' %(fs_la_insula, fs_la_insula_rpi))
os.system('fslswapdim %s RL PA IS %s' %(fs_ra_insula, fs_ra_insula_rpi))
os.system('fslswapdim %s RL PA IS %s' %(fs_sma, fs_sma_rpi))
os.system('flirt -in %s -ref %s -omat %s -dof 6 -out %s -cost mutualinfo' %(t1nii_rpi, anat_func, fst1omat, fst1func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %(fs_acc_rpi, anat_func, fst1omat, fs_acc_func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %(fs_la_insula_rpi, anat_func, fst1omat, fs_la_insula_func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %(fs_ra_insula_rpi, anat_func, fst1omat, fs_ra_insula_func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %(fs_sma_rpi, anat_func, fst1omat, fs_sma_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %(fs_acc_func,fs_acc_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %(fs_la_insula_func,fs_la_insula_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %(fs_ra_insula_func,fs_ra_insula_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %(fs_sma_func,fs_sma_func))
if os.path.isfile(fs_sma_func):
sma_load = nb.load(fs_sma_func).get_data()
x, y, z = center_of_mass(sma_load)
sma_point = os.path.join(outdir, 'sma_point.nii.gz')
fs_sma_optimized = os.path.join(outdir, 'freesurfer_seg_SMA_BA6_func_opt.nii.gz')
os.system('fslmaths %s -mul 0 -add 1 -roi %s 1 %s 1 %s 1 0 1 %s -odt float'%(func_mean, x,y,z, sma_point))
os.system('fslmaths %s -kernel sphere 10 -fmean -dilM -dilM -ero -ero %s -odt float'%(sma_point, fs_sma_optimized))
####################################### GET MOTION PARAMS #######################################
print '5. Grabbing motion paramaters'
motion = os.path.join(subject_dir, 'functional_motion_statistics/motion_power_params.txt')
if os.path.isfile(motion):
power = pd.read_csv(motion) #n, ignore_index = True)
exclude = power.loc[subject][' FD_exclude']
fd = power.loc[subject]['Subject']
if os.path.isfile(func_aroma) and os.path.isfile(func_gm):
dvars = np.mean(return_DVARS(func_aroma, func_gm))
print dvars
####################################### GEN TIMESERIES OF ROIs #######################################
print '6. Extracting timeseries and calculating connectivity'
if os.path.isfile(func_pproc):
stn_timeseries = input_data.NiftiLabelsMasker(labels_img= STN_LEFT, standardize=True).fit_transform(func_pproc)
sn_timeseries = input_data.NiftiLabelsMasker(labels_img= SN_LEFT, standardize=True).fit_transform(func_pproc)
str_timeseries = input_data.NiftiLabelsMasker(labels_img= STR, standardize=True).fit_transform(func_pproc)
tha_timeseries = input_data.NiftiLabelsMasker(labels_img= THA, standardize=True).fit_transform(func_pproc)
thaL_timeseries = input_data.NiftiLabelsMasker(labels_img= lTHA, standardize=True).fit_transform(func_pproc)
thaR_timeseries = input_data.NiftiLabelsMasker(labels_img= rTHA, standardize=True).fit_transform(func_pproc)
cau_timeseries = input_data.NiftiLabelsMasker(labels_img= CAU, standardize=True).fit_transform(func_pproc)
put_timeseries = input_data.NiftiLabelsMasker(labels_img= PUT, standardize=True).fit_transform(func_pproc)
pal_timeseries = input_data.NiftiLabelsMasker(labels_img= PAL, standardize=True).fit_transform(func_pproc)
nac_timeseries = input_data.NiftiLabelsMasker(labels_img= NAC, standardize=True).fit_transform(func_pproc)
hip_timeseries = input_data.NiftiLabelsMasker(labels_img= HIP, standardize=True).fit_transform(func_pproc)
amg_timeseries = input_data.NiftiLabelsMasker(labels_img= AMG, standardize=True).fit_transform(func_pproc)
mACC_timeseries = input_data.NiftiLabelsMasker(labels_img= fs_acc_func, standardize=True).fit_transform(func_pproc)
lINS_timeseries = input_data.NiftiLabelsMasker(labels_img= fs_la_insula_func, standardize=True).fit_transform(func_pproc)
rINS_timeseries = input_data.NiftiLabelsMasker(labels_img= fs_ra_insula_func, standardize=True).fit_transform(func_pproc)
SMA_timeseries = input_data.NiftiLabelsMasker(labels_img= fs_sma_optimized, standardize=True).fit_transform(func_pproc)
mACCX_timeseries = input_data.NiftiLabelsMasker(labels_img= svs_acc_func, standardize=True).fit_transform(func_pproc)
strX_timeseries = input_data.NiftiLabelsMasker(labels_img= svs_str_func, standardize=True).fit_transform(func_pproc)
thaX_timeseries = input_data.NiftiLabelsMasker(labels_img= svs_tha_func, standardize=True).fit_transform(func_pproc)
print '......calculating Subthalamic Nucleus connectivity'
df.loc[subject]['stn_pal'] = float(pearsonr(stn_timeseries, pal_timeseries)[0])
df.loc[subject]['stn_acc'] = float(pearsonr(stn_timeseries, mACC_timeseries)[0])
df.loc[subject]['stn_tha'] = float(pearsonr(stn_timeseries, tha_timeseries)[0])
df.loc[subject]['stn_thaX'] = float(pearsonr(stn_timeseries, thaX_timeseries)[0])
df.loc[subject]['stn_thaL'] = float(pearsonr(stn_timeseries, thaL_timeseries)[0])
df.loc[subject]['stn_thaR'] = float(pearsonr(stn_timeseries, thaR_timeseries)[0])
df.loc[subject]['stn_hip'] = float(pearsonr(stn_timeseries, hip_timeseries)[0])
df.loc[subject]['stn_amg'] = float(pearsonr(stn_timeseries, amg_timeseries)[0])
df.loc[subject]['stn_accX'] = float(pearsonr(stn_timeseries, mACCX_timeseries)[0])
df.loc[subject]['stn_lins'] = float(pearsonr(stn_timeseries, lINS_timeseries)[0])
df.loc[subject]['stn_rins'] = float(pearsonr(stn_timeseries, rINS_timeseries)[0])
df.loc[subject]['stn_sma'] = float(pearsonr(stn_timeseries, SMA_timeseries)[0])
df.loc[subject]['stn_strX'] = float(pearsonr(stn_timeseries, strX_timeseries)[0])
df.loc[subject]['stn_str'] = float(pearsonr(stn_timeseries, str_timeseries)[0])
df.loc[subject]['stn_cau'] = float(pearsonr(stn_timeseries, cau_timeseries)[0])
df.loc[subject]['stn_put'] = float(pearsonr(stn_timeseries, put_timeseries)[0])
df.loc[subject]['stn_nac'] = float(pearsonr(stn_timeseries, nac_timeseries)[0])
print '......calculating Substantia Nigra connectivity'
df.loc[subject]['sn_pal'] = float(pearsonr(sn_timeseries, pal_timeseries)[0])
df.loc[subject]['sn_acc'] = float(pearsonr(sn_timeseries, mACC_timeseries)[0])
df.loc[subject]['sn_tha'] = float(pearsonr(sn_timeseries, tha_timeseries)[0])
df.loc[subject]['sn_thaX'] = float(pearsonr(sn_timeseries, thaX_timeseries)[0])
df.loc[subject]['sn_thaL'] = float(pearsonr(sn_timeseries, thaL_timeseries)[0])
df.loc[subject]['sn_thaR'] = float(pearsonr(sn_timeseries, thaR_timeseries)[0])
df.loc[subject]['sn_hip'] = float(pearsonr(sn_timeseries, hip_timeseries)[0])
df.loc[subject]['sn_amg'] = float(pearsonr(sn_timeseries, amg_timeseries)[0])
df.loc[subject]['sn_accX'] = float(pearsonr(sn_timeseries, mACCX_timeseries)[0])
df.loc[subject]['sn_lins'] = float(pearsonr(sn_timeseries, lINS_timeseries)[0])
df.loc[subject]['sn_rins'] = float(pearsonr(sn_timeseries, rINS_timeseries)[0])
df.loc[subject]['sn_sma'] = float(pearsonr(sn_timeseries, SMA_timeseries)[0])
df.loc[subject]['sn_strX'] = float(pearsonr(sn_timeseries, strX_timeseries)[0])
df.loc[subject]['sn_str'] = float(pearsonr(sn_timeseries, str_timeseries)[0])
df.loc[subject]['sn_cau'] = float(pearsonr(sn_timeseries, cau_timeseries)[0])
df.loc[subject]['sn_put'] = float(pearsonr(sn_timeseries, put_timeseries)[0])
df.loc[subject]['sn_nac'] = float(pearsonr(sn_timeseries, nac_timeseries)[0])
print '......calculating STR_SVS connectivity'
df.loc[subject]['strX_acc'] = float(pearsonr(strX_timeseries, mACC_timeseries)[0])
df.loc[subject]['strX_tha'] = float(pearsonr(strX_timeseries, tha_timeseries)[0])
df.loc[subject]['strX_thaX'] = float(pearsonr(strX_timeseries, thaX_timeseries)[0])
df.loc[subject]['strX_thaL'] = float(pearsonr(strX_timeseries, thaL_timeseries)[0])
df.loc[subject]['strX_thaR'] = float(pearsonr(strX_timeseries, thaR_timeseries)[0])
df.loc[subject]['strX_hip'] = float(pearsonr(strX_timeseries, hip_timeseries)[0])
df.loc[subject]['strX_amg'] = float(pearsonr(strX_timeseries, amg_timeseries)[0])
df.loc[subject]['strX_accX'] = float(pearsonr(strX_timeseries, mACCX_timeseries)[0])
df.loc[subject]['strX_lins'] = float(pearsonr(strX_timeseries, lINS_timeseries)[0])
df.loc[subject]['strX_rins'] = float(pearsonr(strX_timeseries, rINS_timeseries)[0])
df.loc[subject]['strX_sma'] = float(pearsonr(strX_timeseries, SMA_timeseries)[0])
print '......calculating STR connetivity'
df.loc[subject]['str_acc'] = float(pearsonr(str_timeseries, mACC_timeseries)[0])
df.loc[subject]['str_tha'] = float(pearsonr(str_timeseries, tha_timeseries)[0])
df.loc[subject]['str_thaX'] = float(pearsonr(str_timeseries, thaX_timeseries)[0])
df.loc[subject]['str_thaL'] = float(pearsonr(str_timeseries, thaL_timeseries)[0])
df.loc[subject]['str_thaR'] = float(pearsonr(str_timeseries, thaR_timeseries)[0])
df.loc[subject]['str_hip'] = float(pearsonr(str_timeseries, hip_timeseries)[0])
df.loc[subject]['str_amg'] = float(pearsonr(str_timeseries, amg_timeseries)[0])
df.loc[subject]['str_accX'] = float(pearsonr(str_timeseries, mACCX_timeseries)[0])
df.loc[subject]['str_lins'] = float(pearsonr(str_timeseries, lINS_timeseries)[0])
df.loc[subject]['str_rins'] = float(pearsonr(str_timeseries, rINS_timeseries)[0])
df.loc[subject]['str_sma'] = float(pearsonr(str_timeseries, SMA_timeseries)[0])
print '......calculating CAUDATE connectivity'
df.loc[subject]['cau_acc'] = float(pearsonr(cau_timeseries, mACC_timeseries)[0])
df.loc[subject]['cau_tha'] = float(pearsonr(cau_timeseries, tha_timeseries)[0])
df.loc[subject]['cau_thaX'] = float(pearsonr(cau_timeseries, thaX_timeseries)[0])
df.loc[subject]['cau_thaL'] = float(pearsonr(cau_timeseries, thaL_timeseries)[0])
df.loc[subject]['cau_thaR'] = float(pearsonr(cau_timeseries, thaR_timeseries)[0])
df.loc[subject]['cau_pal'] = float(pearsonr(cau_timeseries, pal_timeseries)[0])
df.loc[subject]['cau_hip'] = float(pearsonr(cau_timeseries, hip_timeseries)[0])
df.loc[subject]['cau_amg'] = float(pearsonr(cau_timeseries, amg_timeseries)[0])
df.loc[subject]['cau_accX'] = float(pearsonr(cau_timeseries, mACCX_timeseries)[0])
df.loc[subject]['cau_lins'] = float(pearsonr(cau_timeseries, lINS_timeseries)[0])
df.loc[subject]['cau_rins'] = float(pearsonr(cau_timeseries, rINS_timeseries)[0])
df.loc[subject]['cau_sma'] = float(pearsonr(cau_timeseries, SMA_timeseries)[0])
print '......calculating PUTAMEN connectivity'
df.loc[subject]['put_tha'] = float(pearsonr(put_timeseries, tha_timeseries)[0])
df.loc[subject]['put_thaX'] = float(pearsonr(put_timeseries, thaX_timeseries)[0])
df.loc[subject]['put_thaL'] = float(pearsonr(put_timeseries, thaL_timeseries)[0])
df.loc[subject]['put_thaR'] = float(pearsonr(put_timeseries, thaR_timeseries)[0])
df.loc[subject]['put_pal'] = float(pearsonr(put_timeseries, pal_timeseries)[0])
df.loc[subject]['put_hip'] = float(pearsonr(put_timeseries, hip_timeseries)[0])
df.loc[subject]['put_amg'] = float(pearsonr(put_timeseries, amg_timeseries)[0])
df.loc[subject]['put_acc'] = float(pearsonr(put_timeseries, mACC_timeseries)[0])
df.loc[subject]['put_accX'] = float(pearsonr(put_timeseries, mACCX_timeseries)[0])
df.loc[subject]['put_lins'] = float(pearsonr(put_timeseries, lINS_timeseries)[0])
df.loc[subject]['put_rins'] = float(pearsonr(put_timeseries, rINS_timeseries)[0])
df.loc[subject]['put_sma'] = float(pearsonr(put_timeseries, SMA_timeseries)[0])
print '......calcualting NUCLESUS ACCUMBENS connectivity'
df.loc[subject]['nac_tha'] = float(pearsonr(nac_timeseries, tha_timeseries)[0])
df.loc[subject]['nac_thaX'] = float(pearsonr(nac_timeseries, thaX_timeseries)[0])
df.loc[subject]['nac_thaL'] = float(pearsonr(nac_timeseries, thaL_timeseries)[0])
df.loc[subject]['nac_thaR'] = float(pearsonr(nac_timeseries, thaR_timeseries)[0])
df.loc[subject]['nac_pal'] = float(pearsonr(nac_timeseries, pal_timeseries)[0])
df.loc[subject]['nac_hip'] = float(pearsonr(nac_timeseries, hip_timeseries)[0])
df.loc[subject]['nac_amg'] = float(pearsonr(nac_timeseries, amg_timeseries)[0])
df.loc[subject]['nac_acc'] = float(pearsonr(nac_timeseries, mACC_timeseries)[0])
df.loc[subject]['nac_accX'] = float(pearsonr(nac_timeseries, mACCX_timeseries)[0])
df.loc[subject]['nac_lins'] = float(pearsonr(nac_timeseries, lINS_timeseries)[0])
df.loc[subject]['nac_rins'] = float(pearsonr(nac_timeseries, rINS_timeseries)[0])
df.loc[subject]['nac_sma'] = float(pearsonr(nac_timeseries, SMA_timeseries)[0])
print '......calcualting PALLIDUM connectivity'
df.loc[subject]['pal_tha'] = float(pearsonr(pal_timeseries, tha_timeseries)[0])
df.loc[subject]['pal_thaX'] = float(pearsonr(pal_timeseries, thaX_timeseries)[0])
df.loc[subject]['pal_thaL'] = float(pearsonr(pal_timeseries, thaL_timeseries)[0])
df.loc[subject]['pal_thaR'] = float(pearsonr(pal_timeseries, thaR_timeseries)[0])
df.loc[subject]['pal_hip'] = float(pearsonr(pal_timeseries, hip_timeseries)[0])
df.loc[subject]['pal_amg'] = float(pearsonr(pal_timeseries, amg_timeseries)[0])
df.loc[subject]['pal_acc'] = float(pearsonr(pal_timeseries, mACC_timeseries)[0])
df.loc[subject]['pal_accX'] = float(pearsonr(pal_timeseries, mACCX_timeseries)[0])
df.loc[subject]['pal_lins'] = float(pearsonr(pal_timeseries, lINS_timeseries)[0])
df.loc[subject]['pal_rins'] = float(pearsonr(pal_timeseries, rINS_timeseries)[0])
df.loc[subject]['pal_sma'] = float(pearsonr(pal_timeseries, SMA_timeseries)[0])
print '......calcualting THA_SVS connectivity'
df.loc[subject]['thaX_cau'] = float(pearsonr(thaX_timeseries, cau_timeseries)[0])
df.loc[subject]['thaX_put'] = float(pearsonr(thaX_timeseries, put_timeseries)[0])
df.loc[subject]['thaX_pal'] = float(pearsonr(thaX_timeseries, pal_timeseries)[0])
df.loc[subject]['thaX_nac'] = float(pearsonr(thaX_timeseries, nac_timeseries)[0])
df.loc[subject]['thaX_hip'] = float(pearsonr(thaX_timeseries, hip_timeseries)[0])
df.loc[subject]['thaX_amg'] = float(pearsonr(thaX_timeseries, amg_timeseries)[0])
df.loc[subject]['thaX_acc'] = float(pearsonr(thaX_timeseries, mACC_timeseries)[0])
df.loc[subject]['thaX_accX'] = float(pearsonr(thaX_timeseries, mACCX_timeseries)[0])
df.loc[subject]['thaX_lins'] = float(pearsonr(thaX_timeseries, lINS_timeseries)[0])
df.loc[subject]['thaX_rins'] = float(pearsonr(thaX_timeseries, rINS_timeseries)[0])
df.loc[subject]['thaX_sma'] = float(pearsonr(thaX_timeseries, SMA_timeseries)[0])
print '......calcualting THALAMUS FULL connectivity'
df.loc[subject]['tha_cau'] = float(pearsonr(tha_timeseries, cau_timeseries)[0])
df.loc[subject]['tha_put'] = float(pearsonr(tha_timeseries, put_timeseries)[0])
df.loc[subject]['tha_pal'] = float(pearsonr(tha_timeseries, pal_timeseries)[0])
df.loc[subject]['tha_nac'] = float(pearsonr(tha_timeseries, nac_timeseries)[0])
df.loc[subject]['tha_hip'] = float(pearsonr(tha_timeseries, hip_timeseries)[0])
df.loc[subject]['tha_amg'] = float(pearsonr(tha_timeseries, amg_timeseries)[0])
df.loc[subject]['tha_acc'] = float(pearsonr(tha_timeseries, mACC_timeseries)[0])
df.loc[subject]['tha_accX'] = float(pearsonr(tha_timeseries, mACCX_timeseries)[0])
df.loc[subject]['tha_lins'] = float(pearsonr(tha_timeseries, lINS_timeseries)[0])
df.loc[subject]['tha_rins'] = float(pearsonr(tha_timeseries, rINS_timeseries)[0])
df.loc[subject]['tha_sma'] = float(pearsonr(tha_timeseries, SMA_timeseries)[0])
print '......calcualting THALAMUS RIGHT connectivity'
df.loc[subject]['thaR_cau'] = float(pearsonr(thaR_timeseries, cau_timeseries)[0])
df.loc[subject]['thaR_put'] = float(pearsonr(thaR_timeseries, put_timeseries)[0])
df.loc[subject]['thaR_pal'] = float(pearsonr(thaR_timeseries, pal_timeseries)[0])
df.loc[subject]['thaR_nac'] = float(pearsonr(thaR_timeseries, nac_timeseries)[0])
df.loc[subject]['thaR_hip'] = float(pearsonr(thaR_timeseries, hip_timeseries)[0])
df.loc[subject]['thaR_amg'] = float(pearsonr(thaR_timeseries, amg_timeseries)[0])
df.loc[subject]['thaR_acc'] = float(pearsonr(thaR_timeseries, mACC_timeseries)[0])
df.loc[subject]['thaR_accX'] = float(pearsonr(thaR_timeseries, mACCX_timeseries)[0])
df.loc[subject]['thaR_lins'] = float(pearsonr(thaR_timeseries, lINS_timeseries)[0])
df.loc[subject]['thaR_rins'] = float(pearsonr(thaR_timeseries, rINS_timeseries)[0])
df.loc[subject]['thaR_sma'] = float(pearsonr(thaR_timeseries, SMA_timeseries)[0])
print '......calcualting THALAMUS LEFT connectivity'
df.loc[subject]['thaL_cau'] = float(pearsonr(thaL_timeseries, cau_timeseries)[0])
df.loc[subject]['thaL_put'] = float(pearsonr(thaL_timeseries, put_timeseries)[0])
df.loc[subject]['thaL_pal'] = float(pearsonr(thaL_timeseries, pal_timeseries)[0])
df.loc[subject]['thaL_nac'] = float(pearsonr(thaL_timeseries, nac_timeseries)[0])
df.loc[subject]['thaL_hip'] = float(pearsonr(thaL_timeseries, hip_timeseries)[0])
df.loc[subject]['thaL_amg'] = float(pearsonr(thaL_timeseries, amg_timeseries)[0])
df.loc[subject]['thaL_acc'] = float(pearsonr(thaL_timeseries, mACC_timeseries)[0])
df.loc[subject]['thaL_accX'] = float(pearsonr(thaL_timeseries, mACCX_timeseries)[0])
df.loc[subject]['thaL_lins'] = float(pearsonr(thaL_timeseries, lINS_timeseries)[0])
df.loc[subject]['thaL_rins'] = float(pearsonr(thaL_timeseries, rINS_timeseries)[0])
df.loc[subject]['thaL_sma'] = float(pearsonr(thaL_timeseries, SMA_timeseries)[0])
print '......calcualting ACC connectivity'
df.loc[subject]['acc_lins'] = float(pearsonr(mACC_timeseries, lINS_timeseries)[0])
df.loc[subject]['acc_rins'] = float(pearsonr(mACC_timeseries, rINS_timeseries)[0])
df.loc[subject]['acc_sma'] = float(pearsonr(mACC_timeseries, rINS_timeseries)[0])
df.loc[subject]['accX_lins'] = float(pearsonr(mACCX_timeseries, lINS_timeseries)[0])
df.loc[subject]['accX_rins'] = float(pearsonr(mACCX_timeseries, rINS_timeseries)[0])
df.loc[subject]['accX_sma'] = float(pearsonr(mACCX_timeseries, SMA_timeseries)[0])
print '......calcualting SMA connectivity'
df.loc[subject]['sma_lins'] = float(pearsonr(mACCX_timeseries, lINS_timeseries)[0])
df.loc[subject]['sma_rins'] = float(pearsonr(mACCX_timeseries, rINS_timeseries)[0])
df.loc[subject]['fd'] = fd
df.loc[subject]['exclude'] = exclude
df.loc[subject]['dvars'] = dvars
df.to_csv(os.path.join(workspace_dir,'GluConnectivity' ,'x4_RSFC_df_%s_%s.csv'%(pop_name, mrs_datadir[-1])))
print 'done'
def create_group_dataframe(voxel_name, analysis_type, ppmst):
df_group =[]
for subject in population:
print subject
header = open(os.path.join(workspace_dir, subject, 'svs_rda', 'ACC', 'h2o', '%s%s_ACC_WATER.rda'%(subject, workspace_dir[-10:-9]))).read().splitlines()
gender = [i[11:15] for i in header if 'PatientSex' in i][0]
age = [i[13:15] for i in header if 'PatientAge' in i][0]
analysis_file = os.path.join(workspace_dir, subject, 'lcmodel_%s'%analysis_type, voxel_name, 'ppm_%s'%ppmst, 'table')
if not os.path.isfile(analysis_file):
print 'Subject %s has not %s data for %s' %(subject, analysis_type, voxel_name)
else:
print os.path.join(workspace_dir, subject, 'lcmodel_%s'%analysis_type , voxel_name, 'ppm_%s'%ppmst, 'snr.txt')
quality = np.genfromtxt(os.path.join(workspace_dir, subject, 'lcmodel_%s'%analysis_type , voxel_name, 'ppm_%s'%ppmst, 'snr.txt'), delimiter = ',')
# grab tissue proprotion data
prop_gm, prop_wm, prop_csf, prop_all = np.genfromtxt(os.path.join(workspace_dir,subject,'svs_voxel_stats', '%s_voxel_statistics_spm_opt.txt'%voxel_name), delimiter = ',')
# get lcmodel metabolites from spreadsheet csv
csv = pd.read_csv(os.path.join(workspace_dir, subject, 'lcmodel_%s'%analysis_type,voxel_name, 'ppm_%s'%ppmst, 'spreadsheet.csv'))
# create dataframe with subject demographics, frequency data and reliable metabolite concentrations
columns = ['Age' , 'Gender', 'FWHM' , 'SNR' , 'Shift', 'Ph0', 'Ph1', 'GM', 'WM', 'CSF', 'AllTissue',
'Cre' , 'Cre%' ,
'tCho' , 'tCho%' ,
'NAA' , 'NAA%' ,
'NAAG' , 'NAAG%' ,
'tNAA' , 'tNAA%' ,
'mIno' , 'mIno%' ,
'Glu' , 'Glu%' ,
'Gln' , 'Gln%' ,
'Glx' , 'Glx%' ,
'Glu_Cre', 'Gln_Cre' , 'Glx_Cre' ,
'GABA' , 'GABA%' ,
'Asp' , 'Asp%' ,
'Tau' , 'Tau%' ,
'Lac' , 'Lac%' ,
'Ala' , 'Ala%' ,
'Asp' , 'Asp%' ,
'Scy' , 'Scy%' ,
'Glc' , 'Glc%' ,
'Gua' , 'Gua%' ,]
df_subject = pd.DataFrame(columns = columns, index = ['%s'%subject])
df_subject.loc['%s'%subject] = pd.Series({'Age' : age,
'Gender' : gender,
'FWHM' : quality[1],
'SNR' : quality[2],
'Shift' : quality[3],
'Ph0' : quality[4],
'Ph1' : quality[5],
'GM' : prop_gm * 100.,
'WM' : prop_wm * 100.,
'CSF' : prop_csf * 100.,
'AllTissue' : prop_all * 100.,
'Cre' : float(csv[' Cre']), 'Cre%' : float(csv[' Cre %SD']),
'tCho' : float(csv[' GPC+PCh']), 'tCho%' : float(csv[' GPC+PCh %SD']),
'tNAA' : float(csv[' NAA+NAAG']), 'tNAA%' : float(csv[' NAA+NAAG %SD']),
'NAA' : float(csv[' NAA']), 'NAA%' : float(csv[' NAA %SD']),
'NAAG' : float(csv[' NAAG']), 'NAAG%' : float(csv[' NAAG %SD']),
'mIno' : float(csv[' mI']), 'mIno%' : float(csv[' mI %SD']),
'Glu' : float(csv[' Glu']), 'Glu%' : float(csv[' Glu %SD']),
'Gln' : float(csv[' Gln']), 'Gln%' : float(csv[' Gln %SD']),
'Glx' : float(csv[' Glu+Gln']), 'Glx%' : float(csv[' Glu+Gln %SD']),
'Glu_Cre' : float(csv[' Glu/Cre']),
'Gln_Cre' : float(csv[' Gln/Cre']),
'Glx_Cre' : float(csv[' Glu+Gln/Cre']),
'GABA' : float(csv[' GABA']), 'GABA%' : float(csv[' GABA %SD']),
'Asp' : float(csv[' Asp']), 'Asp%' : float(csv[' Asp %SD']),
'Ala' : float(csv[' Ala']), 'Ala%' : float(csv[' Ala %SD']),
'Lac' : float(csv[' Lac']), 'Lac%' : float(csv[' Lac %SD']),
'Tau' : float(csv[' Tau']), 'Tau%' : float(csv[' Tau %SD']),
#'Gua' : float(csv[' Gua']), 'Gua%' : float(csv[' Gua %SD']),
#'Glc' : float(csv[' Glc']), 'Glc%' : float(csv[' Glc %SD']),
'Scy' : float(csv[' Scyllo']), 'Scy%' : float(csv[' Scyllo %SD']),
})
# append subject data to list
df_group.append(df_subject)
group_dataframe = pd.concat(df_group, ignore_index = False).sort(columns='Age')
# create results directory and save group dataframe
mkdir_path(os.path.join(results_dir, voxel_name))
#group_dataframe.to_csv(os.path.join(results_dir, voxel_name, 'lcmodel_%s_%s_ppmst_%s_%s_%s.csv'%(voxel_name, analysis_type, ppmst, workspace_dir[-8:],workspace_dir[-10:-9])))
group_dataframe.to_csv(os.path.join(results_dir, voxel_name, 'v2_lcmodel_%s_%s_ppmst_%s_%s_%s.csv'%(voxel_name, analysis_type, ppmst, workspace_dir[-8:],workspace_dir[-10:-9])))
print 'NMR-093%s_ %s %s_%s Results here: %s'%( workspace_dir[-10:-9],workspace_dir[-8:], voxel_name,analysis_type, results_dir)
def scrub_data(population, workspace_dir):
count = 0
for subject in population:
count +=1
print '####################################'
print '%s. Running SCRUBBING FOR SUBJECT %s'%(count,subject)
#############################################################
#################### Input and output folders
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
mkdir_path( os.path.join(subject_dir , 'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp'))
pproc = os.path.join(subject_dir , 'functional_native_brain_preproc_FWHM_AROMA_residual_bp/bandpassed_demeaned_filtered.nii.gz')
pproc_2mm = os.path.join(subject_dir , 'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc.nii')
anat2mni = os.path.join(subject_dir , 'anatomical_MNI2mm_xfm/MP2RAGE_DESKULL_RPI_resample_ero_fieldwarp.nii.gz')
func2anat = os.path.join(subject_dir , 'functional_ANAT2mm_xfm/REST_calc_resample_corrected_volreg_maths_tstat_flirt.mat')
#############################################################
#################### WARPING PPROC TO MNI
print '.... warping pproc to MNI SPACE'
if not os.path.isfile(pproc_2mm):
if os.path.isfile(pproc):
print '... Warping to MNI'
os.system('FSLOUTPUTTYPE=NIFTI')
os.system(' '.join([ 'applywarp',
'--in=' + pproc,
'--ref=' + '/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain.nii.gz',
'--out=' + pproc_2mm,
'--warp=' + anat2mni,
'--premat=' + func2anat]))
#############################################################
#################### MAKE SCRUB
print '....scrubbing '
if os.path.isfile(os.path.join(subject_dir, 'functional_motion_FDPower/FD.1D' )):
FDs = np.loadtxt(os.path.join(subject_dir, 'functional_motion_FDPower/FD.1D' ))
# GET LIST OF GOOD FRAMES
in_frames = []
for frame, fd in enumerate(FDs):
if fd < 0.2:
in_frames.append(frame)
#print subject,'-----> GOOD FRAMES =', in_frames
if len(in_frames) > 130:
print '..........Scrubbing frames above FD=0.2 for subject [ %s ]' %subject
print '..........Subject has %s good frames' % len(in_frames)
print '...........taking first 100 good frames'
frames = str(in_frames[0:130]).replace(" ","")
# SCRUB DATA
pproc_2mm = os.path.join(subject_dir , 'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc.nii')
scrubbed = os.path.join(subject_dir , 'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_scrubbed.nii')
os.system("3dcalc -a %s%s -expr 'a' -prefix %s" %(pproc_2mm, frames, scrubbed))
else:
print '**** Subject [ %s ] has less than 100 frames with FD below the 0.2mm threshold'%subject
scrub_subs = []
for subject in population:
if os.path.isfile( os.path.join(workspace_dir, 'GluConnectivity', subject, 'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_scrubbed.nii')):
scrub_subs.append(subject)
print scrub_subs
def scrub_data(population, workspace_dir):
count = 0
for subject in population:
count += 1
print '####################################'
print '%s. Running SCRUBBING FOR SUBJECT %s' % (count, subject)
#############################################################
#################### Input and output folders
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
mkdir_path(
os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp'))
pproc = os.path.join(
subject_dir,
'functional_native_brain_preproc_FWHM_AROMA_residual_bp/bandpassed_demeaned_filtered.nii.gz'
)
pproc_2mm = os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc.nii'
)
anat2mni = os.path.join(
subject_dir,
'anatomical_MNI2mm_xfm/MP2RAGE_DESKULL_RPI_resample_ero_fieldwarp.nii.gz'
)
func2anat = os.path.join(
subject_dir,
'functional_ANAT2mm_xfm/REST_calc_resample_corrected_volreg_maths_tstat_flirt.mat'
)
#############################################################
#################### WARPING PPROC TO MNI
print '.... warping pproc to MNI SPACE'
if not os.path.isfile(pproc_2mm):
if os.path.isfile(pproc):
print '... Warping to MNI'
os.system('FSLOUTPUTTYPE=NIFTI')
os.system(' '.join([
'applywarp', '--in=' + pproc, '--ref=' +
'/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain.nii.gz',
'--out=' + pproc_2mm, '--warp=' + anat2mni,
'--premat=' + func2anat
]))
#############################################################
#################### MAKE SCRUB
print '....scrubbing '
if os.path.isfile(
os.path.join(subject_dir, 'functional_motion_FDPower/FD.1D')):
FDs = np.loadtxt(
os.path.join(subject_dir, 'functional_motion_FDPower/FD.1D'))
# GET LIST OF GOOD FRAMES
in_frames = []
for frame, fd in enumerate(FDs):
if fd < 0.2:
in_frames.append(frame)
#print subject,'-----> GOOD FRAMES =', in_frames
if len(in_frames) > 130:
print '..........Scrubbing frames above FD=0.2 for subject [ %s ]' % subject
print '..........Subject has %s good frames' % len(in_frames)
print '...........taking first 100 good frames'
frames = str(in_frames[0:130]).replace(" ", "")
# SCRUB DATA
pproc_2mm = os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc.nii'
)
scrubbed = os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_scrubbed.nii'
)
os.system("3dcalc -a %s%s -expr 'a' -prefix %s" %
(pproc_2mm, frames, scrubbed))
else:
print '**** Subject [ %s ] has less than 100 frames with FD below the 0.2mm threshold' % subject
scrub_subs = []
for subject in population:
if os.path.isfile(
os.path.join(
workspace_dir, 'GluConnectivity', subject,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/rest_pproc_scrubbed.nii'
)):
scrub_subs.append(subject)
print scrub_subs
def calc_overlap(population, population_name):
for subject in population:
print 'Calculating DICE METRIC for subject', subject
subdir_a = os.path.join(project_dir, 'study_a', population_name, subject)
subdir_b = os.path.join(project_dir, 'study_b', population_name, subject)
dice_dir = os.path.join(subdir_b, 'dice_metric')
mkdir_path(dice_dir)
os.chdir(dice_dir)
anat_a = os.path.join(subdir_a, 'anatomical_original/ANATOMICAL.nii')
anat_b = os.path.join(subdir_b, 'anatomical_original/ANATOMICAL.nii')
acc_a = os.path.join(subdir_a, 'svs_voxel_mask/%sa_ACC_RDA_MASK.nii'%subject)
tha_a = os.path.join(subdir_a, 'svs_voxel_mask/%sa_THA_RDA_MASK.nii'%subject)
str_a = os.path.join(subdir_a, 'svs_voxel_mask/%sa_STR_RDA_MASK.nii'%subject)
acc_b = os.path.join(subdir_b, 'svs_voxel_mask/%sb_ACC_RDA_MASK.nii'%subject)
tha_b = os.path.join(subdir_b, 'svs_voxel_mask/%sb_THA_RDA_MASK.nii'%subject)
str_b = os.path.join(subdir_b, 'svs_voxel_mask/%sb_STR_RDA_MASK.nii'%subject)
print '....running anat registration'
if not os.path.isfile('anat_a2b.mat'):
os.system('flirt -in %s -ref %s -omat anat_a2b.mat -out anat_a2b.nii.gz '
'-dof 6 -cost mutualinfo -finesearch 18'
%(anat_a, anat_b))
print '....applying transform to SVS'
if not os.path.isfile('overlap_ACC_a2b.nii.gz'):
os.system('flirt -in %s -ref %s -applyxfm -init anat_a2b.mat -out overlap_ACC_a2b.nii.gz' %(acc_a, anat_b))
os.system('flirt -in %s -ref %s -applyxfm -init anat_a2b.mat -out overlap_THA_a2b.nii.gz' %(tha_a, anat_b))
os.system('flirt -in %s -ref %s -applyxfm -init anat_a2b.mat -out overlap_STR_a2b.nii.gz' %(str_a, anat_b))
print '....calculating dice'
if not os.path.isfile('dice_metric_ACC.txt'):
calc_dice_metric(acc_b, os.path.join(dice_dir, 'overlap_ACC_a2b.nii.gz'), 'ACC')
calc_dice_metric(tha_b, os.path.join(dice_dir, 'overlap_THA_a2b.nii.gz'), 'THA')
calc_dice_metric(str_b, os.path.join(dice_dir, 'overlap_STR_a2b.nii.gz'), 'STR')
print '....plotting'
def plot_svs(svs_a, svs_b, anatomical, fname):
import os
import numpy as np
import nibabel as nb
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import colors
#get data into matrix
anat_load = nb.load(anatomical)
anat_data = anat_load.get_data()
svs_load_a = nb.load(svs_a)
svs_data_a = svs_load_a.get_data().astype(float)
svs_load_b = nb.load(svs_b)
svs_data_b = svs_load_b.get_data().astype(float)
# get svs cut coords
coords = find_cut_coords(svs_load_b)
# convert zeros to nans for visualization purposes
svs_data_a[svs_data_a==0]=np.nan
svs_data_b[svs_data_b==0]=np.nan
svs_x = svs_data_a * svs_data_b
# plot voxel on anat
fig =plt.figure()
fig.set_size_inches(15, 15)
fig.subplots_adjust(wspace=0.005)
red = colors.ListedColormap(['red'])
blue = colors.ListedColormap(['blue'])
mix = colors.ListedColormap(['purple'])
#1
ax1 = plt.subplot2grid((1,3), (0,0), colspan = 1, rowspan =1)
ax1.imshow(anat_data[coords[0],:,:], matplotlib.cm.bone_r)
ax1.imshow(svs_data_b[coords[0],:,:] , red, alpha = 0.7)
ax1.imshow(svs_data_a[coords[0],:,:] , blue, alpha = 0.7)
ax1.imshow(svs_x[coords[0],:,:] , mix , alpha = 1, interpolation='nearest')
ax1.set_xlim(23, 157)
ax1.set_ylim(101, 230)
ax1.axes.get_yaxis().set_visible(False)
ax1.axes.get_xaxis().set_visible(False)
ax1.axes.get_xaxis().set_visible(False)
#2
ax2 = plt.subplot2grid((1,3), (0,1), colspan = 1, rowspan =1)
ax2.imshow(np.rot90(anat_data[:,:,coords[2]]), matplotlib.cm.bone_r )
ax2.imshow(np.rot90(svs_data_b[:,:,coords[2]]) , red, alpha = 0.7 )
ax2.imshow(np.rot90(svs_data_a[:,:,coords[2]]) , blue, alpha = 0.7 )
ax2.imshow(np.rot90(svs_x[:,:,coords[2]]) , mix, alpha = 0.7 )
ax2.set_xlim(230, 20)
ax2.set_ylim(207, 4)
ax2.axes.get_yaxis().set_visible(False)
ax2.axes.get_xaxis().set_visible(False)
#3
ax3 = plt.subplot2grid((1,3), (0,2), colspan = 1, rowspan =1)
ax3.imshow(anat_data[:,coords[1],:], matplotlib.cm.bone_r, origin='lower')
ax3.imshow(svs_data_b[:,coords[1],:] , red, alpha = 0.7, origin='lower')
ax3.imshow(svs_data_a[:,coords[1],:] , blue, alpha = 0.7, origin='lower')
ax3.imshow(svs_x[:,coords[1],:] , mix, alpha = 0.7, origin='lower')
ax3.set_xlim(37, 140)
ax3.set_ylim(160, 61)
ax3.axes.get_yaxis().set_visible(False)
ax3.axes.get_xaxis().set_visible(False)
for ax in [ax1, ax2, ax3]:
for axis in ['top','bottom','left','right']:
ax.spines[axis].set_color('black')
ax.spines[axis].set_linewidth(3)
fig.tight_layout()
fig.savefig('plot_overlap_%s.png'%fname, dpi=200, bbox_inches='tight')
plot_svs('overlap_ACC_a2b.nii.gz', acc_b, anat_b, 'ACC')
plot_svs('overlap_THA_a2b.nii.gz', tha_b, anat_b, 'THA')
plot_svs('overlap_STR_a2b.nii.gz', str_b, anat_b, 'STR')
def preprocess_functional(population, workspace):
count = 0
for subject in population:
count +=1
#subject = population[subject_index]
print '========================================================================================'
print '%s. Preprocessing functional data for %s' %(count,subject)
raw_dir = mkdir_path(os.path.join(workspace, subject, 'RAW'))
func_dir = mkdir_path(os.path.join(workspace, subject, 'FUNCTIONAL'))
moco_dir = mkdir_path(os.path.join(func_dir, 'moco'))
edit_dir = mkdir_path(os.path.join(func_dir, 'edit'))
##### Minimal pre-processing
if not os.path.isfile(os.path.join(func_dir, 'REST_EDIT.nii.gz')):
print '.....Edit Functional Image (Slice-time-corr/Deoblique/Drop-TRs/Reorient) '
os.chdir(func_dir)
# get data
os.system('fslchfiletype NIFTI_GZ %s/REST.nii.gz %s/REST.nii.gz' %(raw_dir, func_dir))
# shutil.copy(os.path.join(raw_dir, 'REST.nii.gz'), os.path.join(func_dir, 'REST.nii.gz'))
# get params
img_hdr = nb.load('REST.nii.gz').header
TR = img_hdr['pixdim'][4]
nvols = img_hdr['dim'][4]
frames = '[%s..%s]' % (4, nvols -1)
print 'TR =', TR
print 'N-Vols=', nvols
# Deoblique
os.system('3drefit -deoblique REST.nii.gz')
# Slice time correction
os.chdir(edit_dir)
os.system('3dTshift -TR %s -tzero 0 -tpattern alt+z -prefix REST_slc.nii.gz ../REST.nii.gz' %(TR))
# Dropping TRs
os.system('3dcalc -a REST_slc.nii.gz%s -expr "a" -prefix REST_slc_drop.nii.gz' % frames)
# Reorient to RPI
os.system('3dresample -orient RPI -prefix ../REST_EDIT.nii.gz -inset REST_slc_drop.nii.gz')
##### Generate Motion Paramters
if not os.path.isfile(os.path.join(moco_dir, 'REST_EDIT_moco2.nii.gz')):
print '.... Running two-step motion correction'
os.chdir(moco_dir)
# run No.1
os.system('mcflirt -in ../REST_EDIT -out REST_EDIT_moco1 -mats -plots -stats -meanvol ')
# run No.2
os.system('mcflirt -in ../REST_EDIT -out REST_EDIT_moco2 -refvol REST_EDIT_moco1_meanvol -mats -plots -stats')
###### BET and Intensity normalization
if not os.path.isfile(os.path.join(func_dir, 'REST_EDIT_BRAIN_MEAN.nii.gz')):
print '....Brain extraction and intensity normalization'
os.chdir(func_dir)
# Create mask
os.system('bet moco/REST_EDIT_moco2_meanvol.nii.gz moco/REST_EDIT_moco2_meanvol_brain -m -R -f 0.35' )
os.system('cp moco/REST_EDIT_moco2_meanvol_brain_mask.nii.gz REST_BRAIN_MASK.nii.gz')
# Extract Brain
os.system('fslmaths REST_EDIT -mul REST_BRAIN_MASK REST_EDIT_BRAIN_nonorm')
# Intensity Normalization'
os.system('fslmaths REST_EDIT_BRAIN_nonorm -ing 1000 REST_EDIT_BRAIN -odt float')
os.system('rm -rf REST_EDIT_BRAIN_nonorm.nii.gz')
# Get Mean'
os.system('fslmaths REST_EDIT_BRAIN -Tmean REST_EDIT_BRAIN_MEAN.nii' )
os.system('cp moco/REST_EDIT_moco2_meanvol_brain.nii.gz REST_EDIT_MOCO_BRAIN_MEAN.nii.gz' )
def calc_overlap(population, population_name):
for subject in population:
print 'Calculating DICE METRIC for subject', subject
subdir_a = os.path.join(project_dir, 'study_a', population_name,
subject)
subdir_b = os.path.join(project_dir, 'study_b', population_name,
subject)
dice_dir = os.path.join(subdir_b, 'dice_metric')
mkdir_path(dice_dir)
os.chdir(dice_dir)
anat_a = os.path.join(subdir_a, 'anatomical_original/ANATOMICAL.nii')
anat_b = os.path.join(subdir_b, 'anatomical_original/ANATOMICAL.nii')
acc_a = os.path.join(subdir_a,
'svs_voxel_mask/%sa_ACC_RDA_MASK.nii' % subject)
tha_a = os.path.join(subdir_a,
'svs_voxel_mask/%sa_THA_RDA_MASK.nii' % subject)
str_a = os.path.join(subdir_a,
'svs_voxel_mask/%sa_STR_RDA_MASK.nii' % subject)
acc_b = os.path.join(subdir_b,
'svs_voxel_mask/%sb_ACC_RDA_MASK.nii' % subject)
tha_b = os.path.join(subdir_b,
'svs_voxel_mask/%sb_THA_RDA_MASK.nii' % subject)
str_b = os.path.join(subdir_b,
'svs_voxel_mask/%sb_STR_RDA_MASK.nii' % subject)
print '....running anat registration'
if not os.path.isfile('anat_a2b.mat'):
os.system(
'flirt -in %s -ref %s -omat anat_a2b.mat -out anat_a2b.nii.gz '
'-dof 6 -cost mutualinfo -finesearch 18' % (anat_a, anat_b))
print '....applying transform to SVS'
if not os.path.isfile('overlap_ACC_a2b.nii.gz'):
os.system(
'flirt -in %s -ref %s -applyxfm -init anat_a2b.mat -out overlap_ACC_a2b.nii.gz'
% (acc_a, anat_b))
os.system(
'flirt -in %s -ref %s -applyxfm -init anat_a2b.mat -out overlap_THA_a2b.nii.gz'
% (tha_a, anat_b))
os.system(
'flirt -in %s -ref %s -applyxfm -init anat_a2b.mat -out overlap_STR_a2b.nii.gz'
% (str_a, anat_b))
print '....calculating dice'
if not os.path.isfile('dice_metric_ACC.txt'):
calc_dice_metric(acc_b,
os.path.join(dice_dir, 'overlap_ACC_a2b.nii.gz'),
'ACC')
calc_dice_metric(tha_b,
os.path.join(dice_dir, 'overlap_THA_a2b.nii.gz'),
'THA')
calc_dice_metric(str_b,
os.path.join(dice_dir, 'overlap_STR_a2b.nii.gz'),
'STR')
print '....plotting'
def plot_svs(svs_a, svs_b, anatomical, fname):
import os
import numpy as np
import nibabel as nb
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
from matplotlib import colors
#get data into matrix
anat_load = nb.load(anatomical)
anat_data = anat_load.get_data()
svs_load_a = nb.load(svs_a)
svs_data_a = svs_load_a.get_data().astype(float)
svs_load_b = nb.load(svs_b)
svs_data_b = svs_load_b.get_data().astype(float)
# get svs cut coords
coords = find_cut_coords(svs_load_b)
# convert zeros to nans for visualization purposes
svs_data_a[svs_data_a == 0] = np.nan
svs_data_b[svs_data_b == 0] = np.nan
svs_x = svs_data_a * svs_data_b
# plot voxel on anat
fig = plt.figure()
fig.set_size_inches(15, 15)
fig.subplots_adjust(wspace=0.005)
red = colors.ListedColormap(['red'])
blue = colors.ListedColormap(['blue'])
mix = colors.ListedColormap(['purple'])
#1
ax1 = plt.subplot2grid((1, 3), (0, 0), colspan=1, rowspan=1)
ax1.imshow(anat_data[coords[0], :, :], matplotlib.cm.bone_r)
ax1.imshow(svs_data_b[coords[0], :, :], red, alpha=0.7)
ax1.imshow(svs_data_a[coords[0], :, :], blue, alpha=0.7)
ax1.imshow(svs_x[coords[0], :, :],
mix,
alpha=1,
interpolation='nearest')
ax1.set_xlim(23, 157)
ax1.set_ylim(101, 230)
ax1.axes.get_yaxis().set_visible(False)
ax1.axes.get_xaxis().set_visible(False)
ax1.axes.get_xaxis().set_visible(False)
#2
ax2 = plt.subplot2grid((1, 3), (0, 1), colspan=1, rowspan=1)
ax2.imshow(np.rot90(anat_data[:, :, coords[2]]),
matplotlib.cm.bone_r)
ax2.imshow(np.rot90(svs_data_b[:, :, coords[2]]), red, alpha=0.7)
ax2.imshow(np.rot90(svs_data_a[:, :, coords[2]]), blue, alpha=0.7)
ax2.imshow(np.rot90(svs_x[:, :, coords[2]]), mix, alpha=0.7)
ax2.set_xlim(230, 20)
ax2.set_ylim(207, 4)
ax2.axes.get_yaxis().set_visible(False)
ax2.axes.get_xaxis().set_visible(False)
#3
ax3 = plt.subplot2grid((1, 3), (0, 2), colspan=1, rowspan=1)
ax3.imshow(anat_data[:, coords[1], :],
matplotlib.cm.bone_r,
origin='lower')
ax3.imshow(svs_data_b[:, coords[1], :],
red,
alpha=0.7,
origin='lower')
ax3.imshow(svs_data_a[:, coords[1], :],
blue,
alpha=0.7,
origin='lower')
ax3.imshow(svs_x[:, coords[1], :], mix, alpha=0.7, origin='lower')
ax3.set_xlim(37, 140)
ax3.set_ylim(160, 61)
ax3.axes.get_yaxis().set_visible(False)
ax3.axes.get_xaxis().set_visible(False)
for ax in [ax1, ax2, ax3]:
for axis in ['top', 'bottom', 'left', 'right']:
ax.spines[axis].set_color('black')
ax.spines[axis].set_linewidth(3)
fig.tight_layout()
fig.savefig('plot_overlap_%s.png' % fname,
dpi=200,
bbox_inches='tight')
plot_svs('overlap_ACC_a2b.nii.gz', acc_b, anat_b, 'ACC')
plot_svs('overlap_THA_a2b.nii.gz', tha_b, anat_b, 'THA')
plot_svs('overlap_STR_a2b.nii.gz', str_b, anat_b, 'STR')
def segment_spm(population, workspace_dir):
print '#############################################################################'
print ''
print ' RUNNNING PROJECT NMR-093%s %s' %(workspace_dir[-10:-9], workspace_dir[-8:])
print ''
print '#############################################################################'
count= 0
for subject in population:
count +=1
print '========================================================================================'
print '%s- Runnning SPM12 NewSegment on subject %s_%s' %(count, subject, workspace_dir[-10:-9])
print ''
# define subject directory and anatomical file path
subject_dir = os.path.join(workspace_dir , subject)
anatomical_dir = os.path.join(subject_dir , 'anatomical_original')
anatomical_file = os.path.join(anatomical_dir, 'ANATOMICAL.nii')
# check if the file exists
if os.path.isfile(os.path.join(workspace_dir, subject, 'segmentation_spm', 'TISSUE_CLASS_1_GM_prob.nii')):
print 'Brain already segmented......... moving on'
else:
print '..... Segmenting Brain with SPM12-NewSegment'
# define destination directory for spm segmentation outputs
mkdir_path(os.path.join(subject_dir, 'segmentation_spm'))
out_seg_dir = str(os.path.join(subject_dir, 'segmentation_spm'))
# run SPM segmentation
print '..... Starting matlab no splash to run segmentation'
seg = spm.NewSegment()
seg.inputs.channel_files = anatomical_file
seg.inputs.channel_info = (0.0001, 60, (True, True))
seg.out_dir = out_seg_dir
seg.run()
# rename output files
print '..... Renaming outputs and dumping into SPM segmenation dir'
shutil.move(str(os.path.join(anatomical_dir, 'c1ANATOMICAL.nii')),
str(os.path.join(out_seg_dir, 'TISSUE_CLASS_1_GM_prob.nii')))
shutil.move(str(os.path.join(anatomical_dir, 'c2ANATOMICAL.nii')),
str(os.path.join(out_seg_dir, 'TISSUE_CLASS_2_WM_prob.nii')))
shutil.move(str(os.path.join(anatomical_dir, 'c3ANATOMICAL.nii')),
str(os.path.join(out_seg_dir, 'TISSUE_CLASS_3_CSF_prob.nii')))
shutil.move(str(os.path.join(anatomical_dir, 'c4ANATOMICAL.nii')),
str(os.path.join(out_seg_dir, '___Skull.nii')))
shutil.move(str(os.path.join(anatomical_dir, 'c5ANATOMICAL.nii')),
str(os.path.join(out_seg_dir, '___SoftTissue.nii')))
shutil.move((os.path.join(anatomical_dir, 'BiasField_ANATOMICAL.nii')),
(os.path.join(out_seg_dir, '___BiasFieldMap.nii')))
shutil.move((os.path.join(anatomical_dir, 'mANATOMICAL.nii')),
(os.path.join(out_seg_dir, '___mFile.nii')))
shutil.move((os.path.join(anatomical_dir, 'ANATOMICAL_seg8.mat')),
(os.path.join(out_seg_dir, '___seg8.mat')))
'###########################################'
# threshold and biniarize spm tissue masks
print '..... Thresholding and binazing tissue probablity maps '
out_seg_dir = str(os.path.join(subject_dir, 'segmentation_spm'))
gm_mask = str(os.path.join(out_seg_dir, 'TISSUE_CLASS_1_GM_prob.nii'))
wm_mask = str(os.path.join(out_seg_dir, 'TISSUE_CLASS_2_WM_prob.nii'))
csf_mask = str(os.path.join(out_seg_dir, 'TISSUE_CLASS_3_CSF_prob.nii'))
thr_hbin_GM1 = fsl.Threshold()
thr_hbin_GM1.inputs.in_file = gm_mask
thr_hbin_GM1.inputs.thresh = 0.5
thr_hbin_GM1.inputs.args = '-bin'
thr_hbin_GM1.inputs.ignore_exception = True
thr_hbin_GM1.inputs.out_file = str(os.path.join(out_seg_dir, 'TISSUE_CLASS_1_GM_BIN.nii.gz'))
thr_hbin_GM1.run()
thr_hbin_WM1 = fsl.Threshold()
thr_hbin_WM1.inputs.in_file = wm_mask
thr_hbin_WM1.inputs.thresh = 0.5
thr_hbin_WM1.inputs.args = '-bin'
thr_hbin_WM1.inputs.ignore_exception = True
thr_hbin_WM1.inputs.out_file = str(os.path.join(out_seg_dir, 'TISSUE_CLASS_2_WM_BIN.nii.gz'))
thr_hbin_WM1.run()
thr_hbin_CSF1 = fsl.Threshold()
thr_hbin_CSF1.inputs.in_file = csf_mask
thr_hbin_CSF1.inputs.thresh = 0.5
thr_hbin_CSF1.inputs.args = '-bin'
thr_hbin_CSF1.inputs.ignore_exception = True
thr_hbin_CSF1.inputs.out_file = str(os.path.join(out_seg_dir, 'TISSUE_CLASS_3_CSF_BIN.nii.gz'))
thr_hbin_CSF1.run()
'###########################################'
out_seg_dir = os.path.join(subject_dir, 'segmentation_spm')
if os.path.isfile(os.path.join(out_seg_dir, 'TISSUE_CLASS_1_GM_OPTIMIZED.nii.gz')):
print 'Optimized Tissue masks already created......... moving on'
else:
print '..... Segmentatiing Subcortex and creating optimized tissue masks'
# create brain mask from GM, WM, CSF
gm_bin = os.path.join(out_seg_dir, 'TISSUE_CLASS_1_GM_BIN.nii.gz')
wm_bin = os.path.join(out_seg_dir, 'TISSUE_CLASS_2_WM_BIN.nii.gz')
cm_bin = os.path.join(out_seg_dir, 'TISSUE_CLASS_3_CSF_BIN.nii.gz')
brain_mask = os.path.join(anatomical_dir, 'ANATOMICAL_brain_mask.nii.gz')
os.system('fslmaths %s -add %s -add %s -fillh -dilM %s'%(gm_bin,wm_bin, cm_bin,brain_mask))
# # deskull anatomical
anatomical_deskull = os.path.join(anatomical_dir, 'ANATOMICAL_DESKULL.nii.gz')
anatomical_deskull_rpi = os.path.join(anatomical_dir, 'ANATOMICAL_DESKULL_RPI.nii.gz')
os.system('fslmaths %s -mul %s %s' %(anatomical_file, brain_mask, anatomical_deskull))
os.system('fslswapdim %s RL PA IS %s'%(anatomical_deskull, anatomical_deskull_rpi))
# run FLIRT and FIRST
mkdir_path(os.path.join(out_seg_dir, 'FIRST_subcortical'))
out_first_dir = os.path.join(out_seg_dir, 'FIRST_subcortical')
first_seg = os.path.join(out_first_dir, 'FIRST_all_fast_firstseg.nii.gz')
if not os.path.isfile(first_seg):
#if not os.path.isfile(os.path.join(out_seg_dir, 'TISSUE_CLASS_1_GM_OPTIMIZED.nii.gz')):
ref = '/usr/share/fsl/5.0/data/standard/MNI152_T1_1mm_brain.nii.gz'
omat = os.path.join(anatomical_dir, 'ANATOMICAL_DESKULL_RPI_MNI.mat')
anat2mni = os.path.join(anatomical_dir, 'ANATOMICAL_DESKULL_RPI_MNI.nii.gz')
print 'running flirt'
os.system('flirt -in %s -ref %s -out %s -omat %s -cost mutualinfo -dof 12'%(anatomical_deskull_rpi, ref, anat2mni, omat))
print 'running first'
os.system('run_first_all -v -i %s -a %s -o %s/FIRST'%(anatomical_deskull_rpi, omat, out_first_dir))
# flip back to anatomical orientation
first_seg = os.path.join(out_first_dir, 'FIRST_all_fast_firstseg.nii.gz')
first_seg_ail = os.path.join(out_first_dir, 'FIRST_all_fast_firstseg_AIL.nii.gz')
os.system('fslswapdim %s AP IS LR %s' %(first_seg, first_seg_ail))
# create subcortically corrected tissue masks and flip them back to correct orientation
first_seg_ail_bin = os.path.join(out_first_dir, 'FIRST_all_fast_firstseg_AIL_BIN.nii.gz')
gm_combined = os.path.join(out_seg_dir, 'TISSUE_CLASS_1_GM_OPTIMIZED.nii.gz')
wm_combined = os.path.join(out_seg_dir, 'TISSUE_CLASS_2_WM_OPTIMIZED.nii.gz')
cm_combined = os.path.join(out_seg_dir, 'TISSUE_CLASS_3_CSF_OPTIMIZED.nii.gz')
os.system('fslmaths %s -bin %s' %(first_seg_ail, first_seg_ail_bin))
os.system('fslmaths %s -add %s -bin %s' %(first_seg_ail_bin, gm_bin, gm_combined))
os.system('fslmaths %s -sub %s -bin %s' %(wm_bin, first_seg_ail_bin, wm_combined))
os.system('fslmaths %s -sub %s -bin %s' %(cm_bin,first_seg_ail_bin, cm_combined))
print 'done'
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 preprocess_anatomical(population, workspace):
print '========================================================================================'
print ''
print ' Tourettome - 001.Anatomical Data PreProcessing '
print ''
print '========================================================================================'
for subject in population:
print '========================================================================================'
print '-Preprocessing anatomical data for %s' %subject
rawdir = os.path.join(workspace, subject, 'RAW')
anatdir = mkdir_path(os.path.join(workspace, subject, 'ANATOMICAL'))
spmdir = mkdir_path(os.path.join(anatdir, 'seg_spm'))
firstdir = mkdir_path(os.path.join(anatdir, 'seg_first'))
####### RUN SPM SEGMENTATION
if not os.path.isfile(os.path.join(spmdir, 'c1ANATOMICAL.nii')):
### Deoblique ### Replace transformation matrix in header with cardinal matrix.This option DOES NOT deoblique the volume.
os.system('3drefit -deoblique %s' %os.path.join(rawdir, 'ANATOMICAL.nii.gz'))
### SEGMENT
if not os.path.isfile(os.path.join(spmdir, 'mANATOMICAL.nii')):
print '..... Running SPM segmentation'
os.chdir(spmdir)
if not os.path.isfile(os.path.join(spmdir, 'ANATOMICAL.nii')):
shutil.copy(os.path.join(rawdir, 'ANATOMICAL.nii.gz'), os.path.join(spmdir, 'ANATOMICAL.nii.gz'))
os.system('fslchfiletype NIFTI %s' % os.path.join(spmdir, 'ANATOMICAL'))
seg = spm.NewSegment()
seg.inputs.channel_files = os.path.join(spmdir, 'ANATOMICAL.nii')
seg.inputs.channel_info = (0.0001, 60, (True, True))
seg.base_dir = spmdir
seg.run()
####### DESKULL data using segmentation
if not os.path.isfile(os.path.join(anatdir, 'ANATOMICAL_BRAIN.nii.gz')):
print '..... Deskulling'
os.chdir(spmdir)
os.system('fslmaths c1ANATOMICAL -thr 0.5 -bin c1ANATOMICAL_thr05')
os.system('fslmaths c2ANATOMICAL -thr 0.5 -bin c2ANATOMICAL_thr05')
os.system('fslmaths c3ANATOMICAL -thr 0.5 -bin c3ANATOMICAL_thr05')
os.system('fslmaths c1ANATOMICAL_thr05 -add c2ANATOMICAL_thr05 -add c3ANATOMICAL_thr05 -bin -fillh -s 3 -thr 0.5 -bin ../ANATOMICAL_BRAIN_MASK')
os.system('fslmaths mANATOMICAL -mas ../ANATOMICAL_BRAIN_MASK ../ANATOMICAL_BRAIN')
####### OPTIMIZE masks with FSL-FIRST subcortical segmentation
if not os.path.isfile(os.path.join(anatdir, 'ANATOMICAL_GM.nii.gz')):
print '..... Optimizing tissue masks'
if not os.path.isfile(os.path.join(firstdir, 'FIRST_all_fast_firstseg.nii.gz')):
os.chdir(firstdir)
print '.........flirt anat2mni for priors'
os.system('flirt -in ../ANATOMICAL_BRAIN.nii.gz -ref %s -omat anat2mni.mat -out anat2mni -cost mutualinfo -dof 12'%(mni_brain_1mm)) # no skulls. brain to brain.
print '......... fsl-first'
os.system('run_first_all -d -i ../ANATOMICAL_BRAIN -b -a anat2mni.mat -o FIRST')
os.system('fslmaths FIRST_all_fast_firstseg -sub FIRST-BrStem_corr -bin FIRST')
os.system('fslmaths %s/c1ANATOMICAL -thr 0.55 -bin -add FIRST -bin ../ANATOMICAL_GM' %spmdir)
os.system('fslmaths %s/c2ANATOMICAL -thr 0.9 -bin -sub FIRST -bin ../ANATOMICAL_WM' %spmdir)
os.system('fslmaths %s/c3ANATOMICAL -sub 0.9 -bin -sub FIRST -bin ../ANATOMICAL_CSF' %spmdir)
def run_lcmodel_raw(voxel_name, ppmst):
print ''
print 'PROCESSING SPECTRA WITH LCMODEL FOR %s PPMST = %s'%(voxel_name, ppmst)
#
#mkdir_path(os.path.join(workspace_dir, subject, 'lcmodel_twix', voxel_name, 'ppm_%s'%ppmst, 'met'))
#mkdir_path(os.path.join(workspace_dir, subject, 'lcmodel_twix', voxel_name, 'ppm_%s'%ppmst, 'h2o'))
#lcmodel_dir = os.path.join(workspace_dir, subject, 'lcmodel_twix',voxel_name, 'ppm_%s'%ppmst)
mkdir_path(os.path.join(workspace_dir, subject, 'lcmodel_twix_NMEACH', voxel_name, 'ppm_%s'%ppmst, 'met'))
mkdir_path(os.path.join(workspace_dir, subject, 'lcmodel_twix_NMEACH', voxel_name, 'ppm_%s'%ppmst, 'h2o'))
lcmodel_dir = os.path.join(workspace_dir, subject, 'lcmodel_twix_NMEACH',voxel_name, 'ppm_%s'%ppmst)
shutil.copy(os.path.join(twix_dir, '%s'%voxel_name, '%s'%voxel_name, '%s_lcm'%voxel_name),
os.path.join(lcmodel_dir, 'met', 'RAW'))
shutil.copy(os.path.join(twix_dir, '%s'%voxel_name, '%s_w'%voxel_name, '%s_w_lcm'%voxel_name),
os.path.join(lcmodel_dir, 'h2o', 'RAW'))
met = os.path.join(lcmodel_dir, 'met', 'RAW')
h2o = os.path.join(lcmodel_dir, 'h2o', 'RAW')
# read some data from the RDA header
rda_info = []
rda_header = open(os.path.join(workspace_dir, subject, 'lcmodel_rda', voxel_name, 'ppm_%s'%ppmst, 'rda_header.txt'), 'r')
for line in rda_header:
rda_info.append(line)
# define twix parameters
nunfil = 2078
hzpppm = 123.242398
echot = 30.0
deltat = 0.000417
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
Building the control file
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
print '...building control file'
file = open(os.path.join(lcmodel_dir, 'control'), "w")
file.write(" $LCMODL\n")
file.write(" title= 'TWIX - %s' \n" %rda_info[0])
file.write(" srcraw= '%s' \n" %met)
file.write(" srch2o= '%s' \n" %h2o)
file.write(" savdir= '%s' \n" %lcmodel_dir)
file.write(" ppmst= %s \n"%ppmst)
file.write(" ppmend= 0.3\n")
file.write(" nunfil= %s\n"%nunfil)
file.write(" ltable= 7\n")
file.write(" lps= 8\n")
file.write(" lprint= 6\n")
file.write(" lcsv= 11\n")
file.write(" lcoraw= 10\n")
file.write(" lcoord= 9\n")
file.write(" hzpppm= %s\n"%hzpppm)
file.write(" filtab= '%s/table'\n" %lcmodel_dir)
file.write(" filraw= '%s/met/RAW'\n" %lcmodel_dir)
file.write(" filps= '%s/ps'\n" %lcmodel_dir)
file.write(" filpri= '%s/print'\n" %lcmodel_dir)
file.write(" filh2o= '%s/h2o/RAW'\n" %lcmodel_dir)
file.write(" filcsv= '%s/spreadsheet.csv'\n" %lcmodel_dir)
file.write(" filcor= '%s/coraw'\n" %lcmodel_dir)
file.write(" filcoo= '%s/coord'\n" %lcmodel_dir)
file.write(" filbas= '/home/raid3/kanaan/.lcmodel/basis-sets/press_te30_3t_01a.basis'\n")
file.write(" echot= %s \n" %echot)
file.write(" dows= T \n")
file.write(" NEACH= 999 \n") # export met fits
#file.write(" DEGPPM =0 \n")
file.write(" doecc= T\n")
file.write(" deltat= %s\n"%deltat)
file.write(" $END\n")
file.close()
if os.path.isfile(os.path.join(lcmodel_dir, 'spreadsheet.csv')):
print 'Spectrum already processed .................moving on'
else:
print '...running standardA4pdf execution-script '
print ''
lcm_command = ['/bin/sh','/home/raid3/kanaan/.lcmodel/execution-scripts/standardA4pdfv3','%s' %lcmodel_dir,'30','%s' %lcmodel_dir,'%s' %lcmodel_dir]
print '... running execution script'
print subprocess.list2cmdline(lcm_command)
subprocess.call(lcm_command)
reader = open(os.path.join(lcmodel_dir, 'table'), 'r')
for line in reader:
if 'FWHM' in line:
fwhm = float(line[9:14])
snrx = line[29:31]
if 'Data shift' in line:
shift = line[15:21]
if 'Ph:' in line:
ph0 = line[6:10]
ph1 = line[19:24]
fwhm_hz = fwhm * 123.24
file = open(os.path.join(lcmodel_dir, 'snr.txt'), "w")
file.write('%s, %s, %s, %s, %s, %s' %(fwhm,fwhm_hz, snrx, shift, ph0, ph1))
file.close()
print '###############################################################################'
def create_group_dataframe(voxel_name, analysis_type, ppmst):
df_group = []
for subject in population:
print subject
header = open(
os.path.join(
workspace_dir, subject, 'svs_rda', 'ACC', 'h2o',
'%s%s_ACC_WATER.rda' %
(subject, workspace_dir[-10:-9]))).read().splitlines()
gender = [i[11:15] for i in header if 'PatientSex' in i][0]
age = [i[13:15] for i in header if 'PatientAge' in i][0]
analysis_file = os.path.join(workspace_dir, subject,
'lcmodel_%s' % analysis_type,
voxel_name, 'ppm_%s' % ppmst, 'table')
if not os.path.isfile(analysis_file):
print 'Subject %s has not %s data for %s' % (
subject, analysis_type, voxel_name)
else:
print os.path.join(workspace_dir, subject,
'lcmodel_%s' % analysis_type, voxel_name,
'ppm_%s' % ppmst, 'snr.txt')
quality = np.genfromtxt(os.path.join(
workspace_dir, subject, 'lcmodel_%s' % analysis_type,
voxel_name, 'ppm_%s' % ppmst, 'snr.txt'),
delimiter=',')
# grab tissue proprotion data
prop_gm, prop_wm, prop_csf, prop_all = np.genfromtxt(
os.path.join(
workspace_dir, subject, 'svs_voxel_stats',
'%s_voxel_statistics_spm_opt.txt' % voxel_name),
delimiter=',')
# get lcmodel metabolites from spreadsheet csv
csv = pd.read_csv(
os.path.join(workspace_dir, subject,
'lcmodel_%s' % analysis_type, voxel_name,
'ppm_%s' % ppmst, 'spreadsheet.csv'))
# create dataframe with subject demographics, frequency data and reliable metabolite concentrations
columns = [
'Age',
'Gender',
'FWHM',
'SNR',
'Shift',
'Ph0',
'Ph1',
'GM',
'WM',
'CSF',
'AllTissue',
'Cre',
'Cre%',
'tCho',
'tCho%',
'NAA',
'NAA%',
'NAAG',
'NAAG%',
'tNAA',
'tNAA%',
'mIno',
'mIno%',
'Glu',
'Glu%',
'Gln',
'Gln%',
'Glx',
'Glx%',
'Glu_Cre',
'Gln_Cre',
'Glx_Cre',
'GABA',
'GABA%',
'Asp',
'Asp%',
'Tau',
'Tau%',
'Lac',
'Lac%',
'Ala',
'Ala%',
'Asp',
'Asp%',
'Scy',
'Scy%',
'Glc',
'Glc%',
'Gua',
'Gua%',
]
df_subject = pd.DataFrame(columns=columns,
index=['%s' % subject])
df_subject.loc['%s' % subject] = pd.Series({
'Age':
age,
'Gender':
gender,
'FWHM':
quality[1],
'SNR':
quality[2],
'Shift':
quality[3],
'Ph0':
quality[4],
'Ph1':
quality[5],
'GM':
prop_gm * 100.,
'WM':
prop_wm * 100.,
'CSF':
prop_csf * 100.,
'AllTissue':
prop_all * 100.,
'Cre':
float(csv[' Cre']),
'Cre%':
float(csv[' Cre %SD']),
'tCho':
float(csv[' GPC+PCh']),
'tCho%':
float(csv[' GPC+PCh %SD']),
'tNAA':
float(csv[' NAA+NAAG']),
'tNAA%':
float(csv[' NAA+NAAG %SD']),
'NAA':
float(csv[' NAA']),
'NAA%':
float(csv[' NAA %SD']),
'NAAG':
float(csv[' NAAG']),
'NAAG%':
float(csv[' NAAG %SD']),
'mIno':
float(csv[' mI']),
'mIno%':
float(csv[' mI %SD']),
'Glu':
float(csv[' Glu']),
'Glu%':
float(csv[' Glu %SD']),
'Gln':
float(csv[' Gln']),
'Gln%':
float(csv[' Gln %SD']),
'Glx':
float(csv[' Glu+Gln']),
'Glx%':
float(csv[' Glu+Gln %SD']),
'Glu_Cre':
float(csv[' Glu/Cre']),
'Gln_Cre':
float(csv[' Gln/Cre']),
'Glx_Cre':
float(csv[' Glu+Gln/Cre']),
'GABA':
float(csv[' GABA']),
'GABA%':
float(csv[' GABA %SD']),
'Asp':
float(csv[' Asp']),
'Asp%':
float(csv[' Asp %SD']),
'Ala':
float(csv[' Ala']),
'Ala%':
float(csv[' Ala %SD']),
'Lac':
float(csv[' Lac']),
'Lac%':
float(csv[' Lac %SD']),
'Tau':
float(csv[' Tau']),
'Tau%':
float(csv[' Tau %SD']),
#'Gua' : float(csv[' Gua']), 'Gua%' : float(csv[' Gua %SD']),
#'Glc' : float(csv[' Glc']), 'Glc%' : float(csv[' Glc %SD']),
'Scy':
float(csv[' Scyllo']),
'Scy%':
float(csv[' Scyllo %SD']),
})
# append subject data to list
df_group.append(df_subject)
group_dataframe = pd.concat(df_group,
ignore_index=False).sort(columns='Age')
# create results directory and save group dataframe
mkdir_path(os.path.join(results_dir, voxel_name))
#group_dataframe.to_csv(os.path.join(results_dir, voxel_name, 'lcmodel_%s_%s_ppmst_%s_%s_%s.csv'%(voxel_name, analysis_type, ppmst, workspace_dir[-8:],workspace_dir[-10:-9])))
group_dataframe.to_csv(
os.path.join(
results_dir, voxel_name,
'v2_lcmodel_%s_%s_ppmst_%s_%s_%s.csv' %
(voxel_name, analysis_type, ppmst, workspace_dir[-8:],
workspace_dir[-10:-9])))
print 'NMR-093%s_ %s %s_%s Results here: %s' % (
workspace_dir[-10:-9], workspace_dir[-8:], voxel_name,
analysis_type, results_dir)
def run_freesurfer_mask_connectivity(
pop_name,
population,
freesurfer_dir,
workspace_dir,
mrs_datadir,
):
df = pd.DataFrame(index=[population], columns=columnx)
for subject in population:
print '####################### Subject %s' % subject
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
outdir = os.path.join(subject_dir, 'RSFC_CONNECTIVITY')
mkdir_path(outdir)
func_pproc = os.path.join(
subject_dir,
'functional_native_brain_preproc_FWHM_AROMA_residual_bp/bandpassed_demeaned_filtered.nii.gz'
) # 2.3 mm
func_mean = os.path.join(
subject_dir,
'functional_native_brain_preproc_mean/REST_calc_resample_corrected_volreg_maths_tstat.nii'
)
func_aroma = os.path.join(
subject_dir,
'functional_native_brain_preproc_FWHM_AROMA/denoised_func_data_nonaggr.nii.gz'
)
func_gm = os.path.join(
subject_dir,
'functional_native_gm/TISSUE_CLASS_1_GM_OPTIMIZED_resample_flirt_thresh_maths.nii.gz'
)
anat_func = os.path.join(
subject_dir,
'anatomical_FUNC2mm_brain/MP2RAGE_DESKULL_RPI_resample_ero_flirt_flirt.nii.gz'
) # 2.3mm
anat_func_xfm = os.path.join(
subject_dir,
'anatomical_FUNC2mm_xfm/REST_calc_resample_corrected_volreg_maths_tstat_flirt_inv.mat'
)
mni2natwarp = os.path.join(
subject_dir,
'MNI2mm_ANAT_xfm/MP2RAGE_DESKULL_RPI_resample_ero_fieldwarp_inverse.nii.gz'
)
####################################### Grab ATAG masks #######################################
STN_LEFT = os.path.join(outdir, 'ATAG_STN_LEFT.nii.gz')
SN_LEFT = os.path.join(outdir, 'ATAG_SN_LEFT.nii.gz')
GPe_LEFT = os.path.join(outdir, 'ATAG_GPE_left.nii.gz')
GPi_LEFT = os.path.join(outdir, 'ATAG_GPi_left.nii.gz')
os.system('applywarp -i %s -r %s -w %s --postmat=%s -o %s' %
(mni_stn_left_1mm, anat_func, mni2natwarp, anat_func_xfm,
STN_LEFT))
os.system(
'applywarp -i %s -r %s -w %s --postmat=%s -o %s' %
(mni_sn_left_1mm, anat_func, mni2natwarp, anat_func_xfm, SN_LEFT))
os.system('fslmaths %s -bin %s' % (STN_LEFT, STN_LEFT))
os.system('fslmaths %s -bin %s' % (SN_LEFT, SN_LEFT))
####################################### Grab Subcortical masks #######################################
print '1. grabbing FIRST Subcortical masks'
STR = os.path.join(subject_dir, 'functional_subcortical',
'left_str.nii.gz')
CAUx = os.path.join(subject_dir, 'functional_subcortical',
'left_caudate.nii.gz')
PUT = os.path.join(subject_dir, 'functional_subcortical',
'left_putamen.nii.gz')
PAL = os.path.join(subject_dir, 'functional_subcortical',
'left_pallidum.nii.gz')
NAC = os.path.join(subject_dir, 'functional_subcortical',
'left_nacc.nii.gz')
HIP = os.path.join(subject_dir, 'functional_subcortical',
'left_hipoocampus.nii.gz')
AMG = os.path.join(subject_dir, 'functional_subcortical',
'left_amygdala.nii.gz')
THA = os.path.join(subject_dir, 'functional_subcortical',
'thalamus.nii.gz')
lTHA = os.path.join(subject_dir, 'functional_subcortical',
'left_thalamus.nii.gz')
rTHA = os.path.join(subject_dir, 'functional_subcortical',
'right_thalamus.nii.gz')
####################################### Fill Caudate Holes #######################################
CAU = os.path.join(subject_dir, 'functional_subcortical',
'left_caudate_fill.nii.gz')
if not os.path.isfile(CAU):
os.system('fslmaths %s -fillh %s' % (CAUx, CAU))
####################################### Grab svs masks #######################################
print '2. grabbing SVS masks'
svs_acc_src = os.path.join(
mrs_datadir, pop_name, subject, 'svs_voxel_mask',
'%s%s_ACC_RDA_MASK.nii' % (subject, mrs_datadir[-1]))
svs_tha_src = os.path.join(
mrs_datadir, pop_name, subject, 'svs_voxel_mask',
'%s%s_THA_RDA_MASK.nii' % (subject, mrs_datadir[-1]))
svs_str_src = os.path.join(
mrs_datadir, pop_name, subject, 'svs_voxel_mask',
'%s%s_STR_RDA_MASK.nii' % (subject, mrs_datadir[-1]))
svs_acc = os.path.join(outdir, 'svs_acc.nii.gz')
svs_tha = os.path.join(outdir, 'svs_tha.nii.gz')
svs_str = os.path.join(outdir, 'svs_str.nii.gz')
svs_acc_func = os.path.join(outdir, 'svs_acc_func.nii.gz')
svs_tha_func = os.path.join(outdir, 'svs_tha_func.nii.gz')
svs_str_func = os.path.join(outdir, 'svs_str_func.nii.gz')
if not os.path.isfile(svs_acc_func):
os.system('fslswapdim %s RL PA IS %s' % (svs_acc_src, svs_acc))
os.system('fslswapdim %s RL PA IS %s' % (svs_tha_src, svs_tha))
os.system('fslswapdim %s RL PA IS %s' % (svs_str_src, svs_str))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %
(svs_acc, anat_func, anat_func_xfm, svs_acc_func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %
(svs_tha, anat_func, anat_func_xfm, svs_tha_func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %
(svs_str, anat_func, anat_func_xfm, svs_str_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %
(svs_acc_func, svs_acc_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %
(svs_tha_func, svs_tha_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %
(svs_str_func, svs_str_func))
####################################### Grab freesurfer masks #######################################
print '3. grabbing Freesurfer masks'
os.system('export SUBJECTS_DIR=%s' % (freesurfer_dir))
t1mgz = os.path.join(freesurfer_dir, subject, 'mri', 'T1.mgz')
segmgz = os.path.join(freesurfer_dir, subject, 'mri',
'aparc.a2009s+aseg.mgz')
t1nii = os.path.join(outdir, 'freesurfer_T1.nii.gz')
segnii = os.path.join(outdir, 'freesurfer_seg.nii.gz')
fs_la_acc = os.path.join(outdir, 'freesurfer_seg_la_MCC_11107.nii.gz'
) # 11107 ctx_lh_G_and_S_cingul-Mid-Ant
fs_ra_acc = os.path.join(outdir, 'freesurfer_seg_ra_MCC_12107.nii.gz'
) # 12107 ctx_lh_G_and_S_cingul-Mid-Ant
fs_acc = os.path.join(outdir, 'freesurfer_seg_aMCC_11107_12107.nii.gz')
fs_la_insula = os.path.join(outdir,
'freesurfer_seg_la_INS_11148.nii.gz'
) # 11148 ctx_lh_S_circular_insula_ant
fs_ra_insula = os.path.join(outdir,
'freesurfer_seg_ra_INS_12148.nii.gz'
) # 12148 ctx_lh_S_circular_insula_ant
if not os.path.isfile(fs_acc):
os.system('mri_convert %s %s' % (t1mgz, t1nii))
os.system('mri_convert %s %s' % (segmgz, segnii))
os.system('fslmaths %s -thr 11107 -uthr 11107 %s ' %
(segnii, fs_la_acc))
os.system('fslmaths %s -thr 12107 -uthr 12107 %s ' %
(segnii, fs_ra_acc))
os.system('fslmaths %s -add %s -dilM -bin %s' %
(fs_la_acc, fs_ra_acc, fs_acc))
os.system('fslmaths %s -thr 11148 -uthr 11148 -dilM -bin %s' %
(segnii, fs_la_insula))
os.system('fslmaths %s -thr 12148 -uthr 12148 -dilM -bin %s' %
(segnii, fs_ra_insula))
labels_dir = os.path.join(freesurfer_dir, subject, 'label')
fs_ba6_rh = os.path.join(outdir, 'freesurfer_seg_SMA_BA6_rh.nii.gz')
fs_ba6_lh = os.path.join(outdir, 'freesurfer_seg_SMA_BA6_lh.nii.gz')
fs_sma = os.path.join(outdir, 'freesurfer_seg_SMA_BA6.nii.gz')
if not os.path.isfile(fs_sma):
os.system(
'mri_label2vol --label %s/rh.BA6.thresh.label --subject %s --temp %s --regheader %s --o %s'
% (labels_dir, subject, t1mgz, t1mgz, fs_ba6_rh))
os.system(
'mri_label2vol --label %s/lh.BA6.thresh.label --subject %s --temp %s --regheader %s --o %s'
% (labels_dir, subject, t1mgz, t1mgz, fs_ba6_lh))
os.system('fslmaths %s -add %s -dilM -dilM %s' %
(fs_ba6_rh, fs_ba6_lh, fs_sma))
####################################### TRANSFORM Freesurfer masks to native func space #######################################
print '4. Transforming Freesurfer masks to native func space'
t1nii_rpi = os.path.join(outdir, 'freesurfer_T1_RPI.nii.gz')
fs_acc_rpi = os.path.join(
outdir, 'freesurfer_seg_aMCC_11107_12107_RPI.nii.gz')
fs_la_insula_rpi = os.path.join(
outdir, 'freesurfer_seg_la_INS_11148_RPI.nii.gz')
fs_ra_insula_rpi = os.path.join(
outdir, 'freesurfer_seg_ra_INS_12148_RPI.nii.gz')
fs_sma_rpi = os.path.join(outdir, 'freesurfer_seg_SMA_BA6_RPI.nii.gz')
fst1omat = os.path.join(outdir, 'freesurfer2func.mat')
fst1func = os.path.join(outdir, 'freesurfer_T1_func.nii.gz')
fs_acc_func = os.path.join(
outdir, 'freesurfer_seg_aMCC_11107_12107_func.nii.gz')
fs_la_insula_func = os.path.join(
outdir, 'freesurfer_seg_la_INS_11148_func.nii.gz')
fs_ra_insula_func = os.path.join(
outdir, 'freesurfer_seg_ra_INS_11148_func.nii.gz')
fs_sma_func = os.path.join(outdir,
'freesurfer_seg_SMA_BA6_func.nii.gz')
if not os.path.isfile(t1nii_rpi):
os.system('fslswapdim %s RL PA IS %s' % (t1nii, t1nii_rpi))
os.system('fslswapdim %s RL PA IS %s' % (fs_acc, fs_acc_rpi))
os.system('fslswapdim %s RL PA IS %s' %
(fs_la_insula, fs_la_insula_rpi))
os.system('fslswapdim %s RL PA IS %s' %
(fs_ra_insula, fs_ra_insula_rpi))
os.system('fslswapdim %s RL PA IS %s' % (fs_sma, fs_sma_rpi))
os.system(
'flirt -in %s -ref %s -omat %s -dof 6 -out %s -cost mutualinfo'
% (t1nii_rpi, anat_func, fst1omat, fst1func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %
(fs_acc_rpi, anat_func, fst1omat, fs_acc_func))
os.system(
'flirt -in %s -ref %s -init %s -applyxfm -out %s' %
(fs_la_insula_rpi, anat_func, fst1omat, fs_la_insula_func))
os.system(
'flirt -in %s -ref %s -init %s -applyxfm -out %s' %
(fs_ra_insula_rpi, anat_func, fst1omat, fs_ra_insula_func))
os.system('flirt -in %s -ref %s -init %s -applyxfm -out %s' %
(fs_sma_rpi, anat_func, fst1omat, fs_sma_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %
(fs_acc_func, fs_acc_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %
(fs_la_insula_func, fs_la_insula_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %
(fs_ra_insula_func, fs_ra_insula_func))
os.system('fslmaths %s -thr 0.5 -bin %s' %
(fs_sma_func, fs_sma_func))
if os.path.isfile(fs_sma_func):
sma_load = nb.load(fs_sma_func).get_data()
x, y, z = center_of_mass(sma_load)
sma_point = os.path.join(outdir, 'sma_point.nii.gz')
fs_sma_optimized = os.path.join(
outdir, 'freesurfer_seg_SMA_BA6_func_opt.nii.gz')
os.system(
'fslmaths %s -mul 0 -add 1 -roi %s 1 %s 1 %s 1 0 1 %s -odt float'
% (func_mean, x, y, z, sma_point))
os.system(
'fslmaths %s -kernel sphere 10 -fmean -dilM -dilM -ero -ero %s -odt float'
% (sma_point, fs_sma_optimized))
####################################### GET MOTION PARAMS #######################################
print '5. Grabbing motion paramaters'
motion = os.path.join(
subject_dir,
'functional_motion_statistics/motion_power_params.txt')
if os.path.isfile(motion):
power = pd.read_csv(motion) #n, ignore_index = True)
exclude = power.loc[subject][' FD_exclude']
fd = power.loc[subject]['Subject']
if os.path.isfile(func_aroma) and os.path.isfile(func_gm):
dvars = np.mean(return_DVARS(func_aroma, func_gm))
print dvars
####################################### GEN TIMESERIES OF ROIs #######################################
print '6. Extracting timeseries and calculating connectivity'
if os.path.isfile(func_pproc):
stn_timeseries = input_data.NiftiLabelsMasker(
labels_img=STN_LEFT,
standardize=True).fit_transform(func_pproc)
sn_timeseries = input_data.NiftiLabelsMasker(
labels_img=SN_LEFT, standardize=True).fit_transform(func_pproc)
str_timeseries = input_data.NiftiLabelsMasker(
labels_img=STR, standardize=True).fit_transform(func_pproc)
tha_timeseries = input_data.NiftiLabelsMasker(
labels_img=THA, standardize=True).fit_transform(func_pproc)
thaL_timeseries = input_data.NiftiLabelsMasker(
labels_img=lTHA, standardize=True).fit_transform(func_pproc)
thaR_timeseries = input_data.NiftiLabelsMasker(
labels_img=rTHA, standardize=True).fit_transform(func_pproc)
cau_timeseries = input_data.NiftiLabelsMasker(
labels_img=CAU, standardize=True).fit_transform(func_pproc)
put_timeseries = input_data.NiftiLabelsMasker(
labels_img=PUT, standardize=True).fit_transform(func_pproc)
pal_timeseries = input_data.NiftiLabelsMasker(
labels_img=PAL, standardize=True).fit_transform(func_pproc)
nac_timeseries = input_data.NiftiLabelsMasker(
labels_img=NAC, standardize=True).fit_transform(func_pproc)
hip_timeseries = input_data.NiftiLabelsMasker(
labels_img=HIP, standardize=True).fit_transform(func_pproc)
amg_timeseries = input_data.NiftiLabelsMasker(
labels_img=AMG, standardize=True).fit_transform(func_pproc)
mACC_timeseries = input_data.NiftiLabelsMasker(
labels_img=fs_acc_func,
standardize=True).fit_transform(func_pproc)
lINS_timeseries = input_data.NiftiLabelsMasker(
labels_img=fs_la_insula_func,
standardize=True).fit_transform(func_pproc)
rINS_timeseries = input_data.NiftiLabelsMasker(
labels_img=fs_ra_insula_func,
standardize=True).fit_transform(func_pproc)
SMA_timeseries = input_data.NiftiLabelsMasker(
labels_img=fs_sma_optimized,
standardize=True).fit_transform(func_pproc)
mACCX_timeseries = input_data.NiftiLabelsMasker(
labels_img=svs_acc_func,
standardize=True).fit_transform(func_pproc)
strX_timeseries = input_data.NiftiLabelsMasker(
labels_img=svs_str_func,
standardize=True).fit_transform(func_pproc)
thaX_timeseries = input_data.NiftiLabelsMasker(
labels_img=svs_tha_func,
standardize=True).fit_transform(func_pproc)
print '......calculating Subthalamic Nucleus connectivity'
df.loc[subject]['stn_pal'] = float(
pearsonr(stn_timeseries, pal_timeseries)[0])
df.loc[subject]['stn_acc'] = float(
pearsonr(stn_timeseries, mACC_timeseries)[0])
df.loc[subject]['stn_tha'] = float(
pearsonr(stn_timeseries, tha_timeseries)[0])
df.loc[subject]['stn_thaX'] = float(
pearsonr(stn_timeseries, thaX_timeseries)[0])
df.loc[subject]['stn_thaL'] = float(
pearsonr(stn_timeseries, thaL_timeseries)[0])
df.loc[subject]['stn_thaR'] = float(
pearsonr(stn_timeseries, thaR_timeseries)[0])
df.loc[subject]['stn_hip'] = float(
pearsonr(stn_timeseries, hip_timeseries)[0])
df.loc[subject]['stn_amg'] = float(
pearsonr(stn_timeseries, amg_timeseries)[0])
df.loc[subject]['stn_accX'] = float(
pearsonr(stn_timeseries, mACCX_timeseries)[0])
df.loc[subject]['stn_lins'] = float(
pearsonr(stn_timeseries, lINS_timeseries)[0])
df.loc[subject]['stn_rins'] = float(
pearsonr(stn_timeseries, rINS_timeseries)[0])
df.loc[subject]['stn_sma'] = float(
pearsonr(stn_timeseries, SMA_timeseries)[0])
df.loc[subject]['stn_strX'] = float(
pearsonr(stn_timeseries, strX_timeseries)[0])
df.loc[subject]['stn_str'] = float(
pearsonr(stn_timeseries, str_timeseries)[0])
df.loc[subject]['stn_cau'] = float(
pearsonr(stn_timeseries, cau_timeseries)[0])
df.loc[subject]['stn_put'] = float(
pearsonr(stn_timeseries, put_timeseries)[0])
df.loc[subject]['stn_nac'] = float(
pearsonr(stn_timeseries, nac_timeseries)[0])
print '......calculating Substantia Nigra connectivity'
df.loc[subject]['sn_pal'] = float(
pearsonr(sn_timeseries, pal_timeseries)[0])
df.loc[subject]['sn_acc'] = float(
pearsonr(sn_timeseries, mACC_timeseries)[0])
df.loc[subject]['sn_tha'] = float(
pearsonr(sn_timeseries, tha_timeseries)[0])
df.loc[subject]['sn_thaX'] = float(
pearsonr(sn_timeseries, thaX_timeseries)[0])
df.loc[subject]['sn_thaL'] = float(
pearsonr(sn_timeseries, thaL_timeseries)[0])
df.loc[subject]['sn_thaR'] = float(
pearsonr(sn_timeseries, thaR_timeseries)[0])
df.loc[subject]['sn_hip'] = float(
pearsonr(sn_timeseries, hip_timeseries)[0])
df.loc[subject]['sn_amg'] = float(
pearsonr(sn_timeseries, amg_timeseries)[0])
df.loc[subject]['sn_accX'] = float(
pearsonr(sn_timeseries, mACCX_timeseries)[0])
df.loc[subject]['sn_lins'] = float(
pearsonr(sn_timeseries, lINS_timeseries)[0])
df.loc[subject]['sn_rins'] = float(
pearsonr(sn_timeseries, rINS_timeseries)[0])
df.loc[subject]['sn_sma'] = float(
pearsonr(sn_timeseries, SMA_timeseries)[0])
df.loc[subject]['sn_strX'] = float(
pearsonr(sn_timeseries, strX_timeseries)[0])
df.loc[subject]['sn_str'] = float(
pearsonr(sn_timeseries, str_timeseries)[0])
df.loc[subject]['sn_cau'] = float(
pearsonr(sn_timeseries, cau_timeseries)[0])
df.loc[subject]['sn_put'] = float(
pearsonr(sn_timeseries, put_timeseries)[0])
df.loc[subject]['sn_nac'] = float(
pearsonr(sn_timeseries, nac_timeseries)[0])
print '......calculating STR_SVS connectivity'
df.loc[subject]['strX_acc'] = float(
pearsonr(strX_timeseries, mACC_timeseries)[0])
df.loc[subject]['strX_tha'] = float(
pearsonr(strX_timeseries, tha_timeseries)[0])
df.loc[subject]['strX_thaX'] = float(
pearsonr(strX_timeseries, thaX_timeseries)[0])
df.loc[subject]['strX_thaL'] = float(
pearsonr(strX_timeseries, thaL_timeseries)[0])
df.loc[subject]['strX_thaR'] = float(
pearsonr(strX_timeseries, thaR_timeseries)[0])
df.loc[subject]['strX_hip'] = float(
pearsonr(strX_timeseries, hip_timeseries)[0])
df.loc[subject]['strX_amg'] = float(
pearsonr(strX_timeseries, amg_timeseries)[0])
df.loc[subject]['strX_accX'] = float(
pearsonr(strX_timeseries, mACCX_timeseries)[0])
df.loc[subject]['strX_lins'] = float(
pearsonr(strX_timeseries, lINS_timeseries)[0])
df.loc[subject]['strX_rins'] = float(
pearsonr(strX_timeseries, rINS_timeseries)[0])
df.loc[subject]['strX_sma'] = float(
pearsonr(strX_timeseries, SMA_timeseries)[0])
print '......calculating STR connetivity'
df.loc[subject]['str_acc'] = float(
pearsonr(str_timeseries, mACC_timeseries)[0])
df.loc[subject]['str_tha'] = float(
pearsonr(str_timeseries, tha_timeseries)[0])
df.loc[subject]['str_thaX'] = float(
pearsonr(str_timeseries, thaX_timeseries)[0])
df.loc[subject]['str_thaL'] = float(
pearsonr(str_timeseries, thaL_timeseries)[0])
df.loc[subject]['str_thaR'] = float(
pearsonr(str_timeseries, thaR_timeseries)[0])
df.loc[subject]['str_hip'] = float(
pearsonr(str_timeseries, hip_timeseries)[0])
df.loc[subject]['str_amg'] = float(
pearsonr(str_timeseries, amg_timeseries)[0])
df.loc[subject]['str_accX'] = float(
pearsonr(str_timeseries, mACCX_timeseries)[0])
df.loc[subject]['str_lins'] = float(
pearsonr(str_timeseries, lINS_timeseries)[0])
df.loc[subject]['str_rins'] = float(
pearsonr(str_timeseries, rINS_timeseries)[0])
df.loc[subject]['str_sma'] = float(
pearsonr(str_timeseries, SMA_timeseries)[0])
print '......calculating CAUDATE connectivity'
df.loc[subject]['cau_acc'] = float(
pearsonr(cau_timeseries, mACC_timeseries)[0])
df.loc[subject]['cau_tha'] = float(
pearsonr(cau_timeseries, tha_timeseries)[0])
df.loc[subject]['cau_thaX'] = float(
pearsonr(cau_timeseries, thaX_timeseries)[0])
df.loc[subject]['cau_thaL'] = float(
pearsonr(cau_timeseries, thaL_timeseries)[0])
df.loc[subject]['cau_thaR'] = float(
pearsonr(cau_timeseries, thaR_timeseries)[0])
df.loc[subject]['cau_pal'] = float(
pearsonr(cau_timeseries, pal_timeseries)[0])
df.loc[subject]['cau_hip'] = float(
pearsonr(cau_timeseries, hip_timeseries)[0])
df.loc[subject]['cau_amg'] = float(
pearsonr(cau_timeseries, amg_timeseries)[0])
df.loc[subject]['cau_accX'] = float(
pearsonr(cau_timeseries, mACCX_timeseries)[0])
df.loc[subject]['cau_lins'] = float(
pearsonr(cau_timeseries, lINS_timeseries)[0])
df.loc[subject]['cau_rins'] = float(
pearsonr(cau_timeseries, rINS_timeseries)[0])
df.loc[subject]['cau_sma'] = float(
pearsonr(cau_timeseries, SMA_timeseries)[0])
print '......calculating PUTAMEN connectivity'
df.loc[subject]['put_tha'] = float(
pearsonr(put_timeseries, tha_timeseries)[0])
df.loc[subject]['put_thaX'] = float(
pearsonr(put_timeseries, thaX_timeseries)[0])
df.loc[subject]['put_thaL'] = float(
pearsonr(put_timeseries, thaL_timeseries)[0])
df.loc[subject]['put_thaR'] = float(
pearsonr(put_timeseries, thaR_timeseries)[0])
df.loc[subject]['put_pal'] = float(
pearsonr(put_timeseries, pal_timeseries)[0])
df.loc[subject]['put_hip'] = float(
pearsonr(put_timeseries, hip_timeseries)[0])
df.loc[subject]['put_amg'] = float(
pearsonr(put_timeseries, amg_timeseries)[0])
df.loc[subject]['put_acc'] = float(
pearsonr(put_timeseries, mACC_timeseries)[0])
df.loc[subject]['put_accX'] = float(
pearsonr(put_timeseries, mACCX_timeseries)[0])
df.loc[subject]['put_lins'] = float(
pearsonr(put_timeseries, lINS_timeseries)[0])
df.loc[subject]['put_rins'] = float(
pearsonr(put_timeseries, rINS_timeseries)[0])
df.loc[subject]['put_sma'] = float(
pearsonr(put_timeseries, SMA_timeseries)[0])
print '......calcualting NUCLESUS ACCUMBENS connectivity'
df.loc[subject]['nac_tha'] = float(
pearsonr(nac_timeseries, tha_timeseries)[0])
df.loc[subject]['nac_thaX'] = float(
pearsonr(nac_timeseries, thaX_timeseries)[0])
df.loc[subject]['nac_thaL'] = float(
pearsonr(nac_timeseries, thaL_timeseries)[0])
df.loc[subject]['nac_thaR'] = float(
pearsonr(nac_timeseries, thaR_timeseries)[0])
df.loc[subject]['nac_pal'] = float(
pearsonr(nac_timeseries, pal_timeseries)[0])
df.loc[subject]['nac_hip'] = float(
pearsonr(nac_timeseries, hip_timeseries)[0])
df.loc[subject]['nac_amg'] = float(
pearsonr(nac_timeseries, amg_timeseries)[0])
df.loc[subject]['nac_acc'] = float(
pearsonr(nac_timeseries, mACC_timeseries)[0])
df.loc[subject]['nac_accX'] = float(
pearsonr(nac_timeseries, mACCX_timeseries)[0])
df.loc[subject]['nac_lins'] = float(
pearsonr(nac_timeseries, lINS_timeseries)[0])
df.loc[subject]['nac_rins'] = float(
pearsonr(nac_timeseries, rINS_timeseries)[0])
df.loc[subject]['nac_sma'] = float(
pearsonr(nac_timeseries, SMA_timeseries)[0])
print '......calcualting PALLIDUM connectivity'
df.loc[subject]['pal_tha'] = float(
pearsonr(pal_timeseries, tha_timeseries)[0])
df.loc[subject]['pal_thaX'] = float(
pearsonr(pal_timeseries, thaX_timeseries)[0])
df.loc[subject]['pal_thaL'] = float(
pearsonr(pal_timeseries, thaL_timeseries)[0])
df.loc[subject]['pal_thaR'] = float(
pearsonr(pal_timeseries, thaR_timeseries)[0])
df.loc[subject]['pal_hip'] = float(
pearsonr(pal_timeseries, hip_timeseries)[0])
df.loc[subject]['pal_amg'] = float(
pearsonr(pal_timeseries, amg_timeseries)[0])
df.loc[subject]['pal_acc'] = float(
pearsonr(pal_timeseries, mACC_timeseries)[0])
df.loc[subject]['pal_accX'] = float(
pearsonr(pal_timeseries, mACCX_timeseries)[0])
df.loc[subject]['pal_lins'] = float(
pearsonr(pal_timeseries, lINS_timeseries)[0])
df.loc[subject]['pal_rins'] = float(
pearsonr(pal_timeseries, rINS_timeseries)[0])
df.loc[subject]['pal_sma'] = float(
pearsonr(pal_timeseries, SMA_timeseries)[0])
print '......calcualting THA_SVS connectivity'
df.loc[subject]['thaX_cau'] = float(
pearsonr(thaX_timeseries, cau_timeseries)[0])
df.loc[subject]['thaX_put'] = float(
pearsonr(thaX_timeseries, put_timeseries)[0])
df.loc[subject]['thaX_pal'] = float(
pearsonr(thaX_timeseries, pal_timeseries)[0])
df.loc[subject]['thaX_nac'] = float(
pearsonr(thaX_timeseries, nac_timeseries)[0])
df.loc[subject]['thaX_hip'] = float(
pearsonr(thaX_timeseries, hip_timeseries)[0])
df.loc[subject]['thaX_amg'] = float(
pearsonr(thaX_timeseries, amg_timeseries)[0])
df.loc[subject]['thaX_acc'] = float(
pearsonr(thaX_timeseries, mACC_timeseries)[0])
df.loc[subject]['thaX_accX'] = float(
pearsonr(thaX_timeseries, mACCX_timeseries)[0])
df.loc[subject]['thaX_lins'] = float(
pearsonr(thaX_timeseries, lINS_timeseries)[0])
df.loc[subject]['thaX_rins'] = float(
pearsonr(thaX_timeseries, rINS_timeseries)[0])
df.loc[subject]['thaX_sma'] = float(
pearsonr(thaX_timeseries, SMA_timeseries)[0])
print '......calcualting THALAMUS FULL connectivity'
df.loc[subject]['tha_cau'] = float(
pearsonr(tha_timeseries, cau_timeseries)[0])
df.loc[subject]['tha_put'] = float(
pearsonr(tha_timeseries, put_timeseries)[0])
df.loc[subject]['tha_pal'] = float(
pearsonr(tha_timeseries, pal_timeseries)[0])
df.loc[subject]['tha_nac'] = float(
pearsonr(tha_timeseries, nac_timeseries)[0])
df.loc[subject]['tha_hip'] = float(
pearsonr(tha_timeseries, hip_timeseries)[0])
df.loc[subject]['tha_amg'] = float(
pearsonr(tha_timeseries, amg_timeseries)[0])
df.loc[subject]['tha_acc'] = float(
pearsonr(tha_timeseries, mACC_timeseries)[0])
df.loc[subject]['tha_accX'] = float(
pearsonr(tha_timeseries, mACCX_timeseries)[0])
df.loc[subject]['tha_lins'] = float(
pearsonr(tha_timeseries, lINS_timeseries)[0])
df.loc[subject]['tha_rins'] = float(
pearsonr(tha_timeseries, rINS_timeseries)[0])
df.loc[subject]['tha_sma'] = float(
pearsonr(tha_timeseries, SMA_timeseries)[0])
print '......calcualting THALAMUS RIGHT connectivity'
df.loc[subject]['thaR_cau'] = float(
pearsonr(thaR_timeseries, cau_timeseries)[0])
df.loc[subject]['thaR_put'] = float(
pearsonr(thaR_timeseries, put_timeseries)[0])
df.loc[subject]['thaR_pal'] = float(
pearsonr(thaR_timeseries, pal_timeseries)[0])
df.loc[subject]['thaR_nac'] = float(
pearsonr(thaR_timeseries, nac_timeseries)[0])
df.loc[subject]['thaR_hip'] = float(
pearsonr(thaR_timeseries, hip_timeseries)[0])
df.loc[subject]['thaR_amg'] = float(
pearsonr(thaR_timeseries, amg_timeseries)[0])
df.loc[subject]['thaR_acc'] = float(
pearsonr(thaR_timeseries, mACC_timeseries)[0])
df.loc[subject]['thaR_accX'] = float(
pearsonr(thaR_timeseries, mACCX_timeseries)[0])
df.loc[subject]['thaR_lins'] = float(
pearsonr(thaR_timeseries, lINS_timeseries)[0])
df.loc[subject]['thaR_rins'] = float(
pearsonr(thaR_timeseries, rINS_timeseries)[0])
df.loc[subject]['thaR_sma'] = float(
pearsonr(thaR_timeseries, SMA_timeseries)[0])
print '......calcualting THALAMUS LEFT connectivity'
df.loc[subject]['thaL_cau'] = float(
pearsonr(thaL_timeseries, cau_timeseries)[0])
df.loc[subject]['thaL_put'] = float(
pearsonr(thaL_timeseries, put_timeseries)[0])
df.loc[subject]['thaL_pal'] = float(
pearsonr(thaL_timeseries, pal_timeseries)[0])
df.loc[subject]['thaL_nac'] = float(
pearsonr(thaL_timeseries, nac_timeseries)[0])
df.loc[subject]['thaL_hip'] = float(
pearsonr(thaL_timeseries, hip_timeseries)[0])
df.loc[subject]['thaL_amg'] = float(
pearsonr(thaL_timeseries, amg_timeseries)[0])
df.loc[subject]['thaL_acc'] = float(
pearsonr(thaL_timeseries, mACC_timeseries)[0])
df.loc[subject]['thaL_accX'] = float(
pearsonr(thaL_timeseries, mACCX_timeseries)[0])
df.loc[subject]['thaL_lins'] = float(
pearsonr(thaL_timeseries, lINS_timeseries)[0])
df.loc[subject]['thaL_rins'] = float(
pearsonr(thaL_timeseries, rINS_timeseries)[0])
df.loc[subject]['thaL_sma'] = float(
pearsonr(thaL_timeseries, SMA_timeseries)[0])
print '......calcualting ACC connectivity'
df.loc[subject]['acc_lins'] = float(
pearsonr(mACC_timeseries, lINS_timeseries)[0])
df.loc[subject]['acc_rins'] = float(
pearsonr(mACC_timeseries, rINS_timeseries)[0])
df.loc[subject]['acc_sma'] = float(
pearsonr(mACC_timeseries, rINS_timeseries)[0])
df.loc[subject]['accX_lins'] = float(
pearsonr(mACCX_timeseries, lINS_timeseries)[0])
df.loc[subject]['accX_rins'] = float(
pearsonr(mACCX_timeseries, rINS_timeseries)[0])
df.loc[subject]['accX_sma'] = float(
pearsonr(mACCX_timeseries, SMA_timeseries)[0])
print '......calcualting SMA connectivity'
df.loc[subject]['sma_lins'] = float(
pearsonr(mACCX_timeseries, lINS_timeseries)[0])
df.loc[subject]['sma_rins'] = float(
pearsonr(mACCX_timeseries, rINS_timeseries)[0])
df.loc[subject]['fd'] = fd
df.loc[subject]['exclude'] = exclude
df.loc[subject]['dvars'] = dvars
df.to_csv(
os.path.join(workspace_dir, 'GluConnectivity',
'x4_RSFC_df_%s_%s.csv' % (pop_name, mrs_datadir[-1])))
print 'done'
def run_lcmodel_raw(voxel_name, ppmst):
print ''
print 'PROCESSING SPECTRA WITH LCMODEL FOR %s PPMST = %s' % (
voxel_name, ppmst)
#
#mkdir_path(os.path.join(workspace_dir, subject, 'lcmodel_twix', voxel_name, 'ppm_%s'%ppmst, 'met'))
#mkdir_path(os.path.join(workspace_dir, subject, 'lcmodel_twix', voxel_name, 'ppm_%s'%ppmst, 'h2o'))
#lcmodel_dir = os.path.join(workspace_dir, subject, 'lcmodel_twix',voxel_name, 'ppm_%s'%ppmst)
mkdir_path(
os.path.join(workspace_dir, subject, 'lcmodel_twix_NMEACH',
voxel_name, 'ppm_%s' % ppmst, 'met'))
mkdir_path(
os.path.join(workspace_dir, subject, 'lcmodel_twix_NMEACH',
voxel_name, 'ppm_%s' % ppmst, 'h2o'))
lcmodel_dir = os.path.join(workspace_dir, subject,
'lcmodel_twix_NMEACH', voxel_name,
'ppm_%s' % ppmst)
shutil.copy(
os.path.join(twix_dir, '%s' % voxel_name, '%s' % voxel_name,
'%s_lcm' % voxel_name),
os.path.join(lcmodel_dir, 'met', 'RAW'))
shutil.copy(
os.path.join(twix_dir, '%s' % voxel_name, '%s_w' % voxel_name,
'%s_w_lcm' % voxel_name),
os.path.join(lcmodel_dir, 'h2o', 'RAW'))
met = os.path.join(lcmodel_dir, 'met', 'RAW')
h2o = os.path.join(lcmodel_dir, 'h2o', 'RAW')
# read some data from the RDA header
rda_info = []
rda_header = open(
os.path.join(workspace_dir, subject, 'lcmodel_rda', voxel_name,
'ppm_%s' % ppmst, 'rda_header.txt'), 'r')
for line in rda_header:
rda_info.append(line)
# define twix parameters
nunfil = 2078
hzpppm = 123.242398
echot = 30.0
deltat = 0.000417
'''''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' '''
Building the control file
''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' '''''' ''''''
print '...building control file'
file = open(os.path.join(lcmodel_dir, 'control'), "w")
file.write(" $LCMODL\n")
file.write(" title= 'TWIX - %s' \n" % rda_info[0])
file.write(" srcraw= '%s' \n" % met)
file.write(" srch2o= '%s' \n" % h2o)
file.write(" savdir= '%s' \n" % lcmodel_dir)
file.write(" ppmst= %s \n" % ppmst)
file.write(" ppmend= 0.3\n")
file.write(" nunfil= %s\n" % nunfil)
file.write(" ltable= 7\n")
file.write(" lps= 8\n")
file.write(" lprint= 6\n")
file.write(" lcsv= 11\n")
file.write(" lcoraw= 10\n")
file.write(" lcoord= 9\n")
file.write(" hzpppm= %s\n" % hzpppm)
file.write(" filtab= '%s/table'\n" % lcmodel_dir)
file.write(" filraw= '%s/met/RAW'\n" % lcmodel_dir)
file.write(" filps= '%s/ps'\n" % lcmodel_dir)
file.write(" filpri= '%s/print'\n" % lcmodel_dir)
file.write(" filh2o= '%s/h2o/RAW'\n" % lcmodel_dir)
file.write(" filcsv= '%s/spreadsheet.csv'\n" % lcmodel_dir)
file.write(" filcor= '%s/coraw'\n" % lcmodel_dir)
file.write(" filcoo= '%s/coord'\n" % lcmodel_dir)
file.write(
" filbas= '/home/raid3/kanaan/.lcmodel/basis-sets/press_te30_3t_01a.basis'\n"
)
file.write(" echot= %s \n" % echot)
file.write(" dows= T \n")
file.write(" NEACH= 999 \n") # export met fits
#file.write(" DEGPPM =0 \n")
file.write(" doecc= T\n")
file.write(" deltat= %s\n" % deltat)
file.write(" $END\n")
file.close()
if os.path.isfile(os.path.join(lcmodel_dir, 'spreadsheet.csv')):
print 'Spectrum already processed .................moving on'
else:
print '...running standardA4pdf execution-script '
print ''
lcm_command = [
'/bin/sh',
'/home/raid3/kanaan/.lcmodel/execution-scripts/standardA4pdfv3',
'%s' % lcmodel_dir, '30',
'%s' % lcmodel_dir,
'%s' % lcmodel_dir
]
print '... running execution script'
print subprocess.list2cmdline(lcm_command)
subprocess.call(lcm_command)
reader = open(os.path.join(lcmodel_dir, 'table'), 'r')
for line in reader:
if 'FWHM' in line:
fwhm = float(line[9:14])
snrx = line[29:31]
if 'Data shift' in line:
shift = line[15:21]
if 'Ph:' in line:
ph0 = line[6:10]
ph1 = line[19:24]
fwhm_hz = fwhm * 123.24
file = open(os.path.join(lcmodel_dir, 'snr.txt'), "w")
file.write('%s, %s, %s, %s, %s, %s' %
(fwhm, fwhm_hz, snrx, shift, ph0, ph1))
file.close()
print '###############################################################################'
def dicom_convert(population, data_dir, workspace_dir):
print '#############################################################################'
print ''
print ' RUNNNING PROJECT %s %s' % (data_dir[12:19],
workspace_dir[-8:])
print ''
print '#############################################################################'
count = 0
for subject in population:
count += 1
print '====================================================================='
print '%s- DICOM CONVERSION for %s' % (count, subject)
# define dicom directory for each subject
dicom_dir = os.path.join(data_dir, subject, 'DICOM')
# define destination directory for NIFTI outputs
mkdir_path(os.path.join(workspace_dir, subject, 'anatomical_original'))
out_nifti_dir = str(
os.path.join(workspace_dir, subject, 'anatomical_original'))
if not os.path.isfile(os.path.join(out_nifti_dir, 'ANATOMICAL.nii')):
# create a list of all dicoms with absolute paths for each file
dicom_list = []
for dicom in os.listdir(dicom_dir):
dicomstr = os.path.join(dicom_dir, dicom)
dicom_list.append(dicomstr)
# grab SeriesDescription and append T1 files to list
T1_list = []
print 'Reading dicom series descriptions'
for dicom in dicom_list:
try:
dcm_read = pydicom.read_file(dicom, force=True)
sequence = dcm_read.SeriesDescription
except AttributeError:
continue
if 'mp2rage_p3_602B_UNI_Images' in sequence:
T1_list.append(dicom)
# convert T1 anatomical to NIFTI with SPM
print 'Converting Dicom to Nifti for %s' % subject
spm_dicom_convert = spmu.DicomImport()
spm_dicom_convert.inputs.format = 'nii'
spm_dicom_convert.inputs.in_files = T1_list
spm_dicom_convert.inputs.output_dir = out_nifti_dir
spm_dicom_convert.run()
#rename output file
for file in os.listdir(out_nifti_dir):
if file.endswith('nii'):
os.rename(
str(os.path.join(out_nifti_dir, file)),
str(os.path.join(out_nifti_dir, 'ANATOMICAL.nii')))
else:
print 'subject already processed.......moving on'
print '====================================================================='
print ''
def quantiation_correction(population, workspace_dir, analysis_type):
print "#############################################################################"
print ""
print " RUNNNING PROJECT NMR-093%s %s" % (workspace_dir[-10:-9], workspace_dir[-8:])
print ""
print "#############################################################################"
# output dir
mkdir_path(os.path.join(workspace_dir[:-8], "group_statistics"))
results_dir = os.path.join(workspace_dir[:-8], "group_statistics")
def save_reliable_concentrations(population, workspace_dir, voxel_name, analysis_type):
csv_list = []
for subject in population:
# get metabolite data for each subject and append to a list
csv = os.path.join(workspace_dir, subject, "lcmodel_%s" % analysis_type, voxel_name, "spreadsheet.csv")
if os.path.isfile(csv):
reader = pd.read_csv(csv)
reader.insert(0, "Subject", subject)
csv_list.append(reader)
# creat a dataframe and place reliable metabolite data for every subject
df = pd.concat(csv_list, ignore_index=True)
reliable = df.loc[
:,
[
"Subject",
" Cre",
" Cre %SD",
" GPC+PCh",
" GPC+PCh %SD",
" NAA+NAAG",
" NAA+NAAG %SD",
" mI",
" mI %SD",
" Glu",
" Glu %SD",
" Gln",
" Gln %SD",
" Glu+Gln",
" Glu+Gln %SD",
],
]
# sort subjects alphabetically and reset index....
reliable.sort(columns="Subject", inplace=True)
reliable.reset_index(drop=True, inplace=True)
# save reliable dataframe
reliable.to_csv(
os.path.join(
results_dir,
"lcmodel_%s_%s_%s_%s.csv" % (analysis_type, voxel_name, workspace_dir[-8:], workspace_dir[-10:-9]),
)
)
return reliable
print "1. Creating new dataframe with reliable LC-Model concentrations for ACC,THA,STR"
acc_reliable = save_reliable_concentrations(population, workspace_dir, "ACC", analysis_type)
tha_reliable = save_reliable_concentrations(population, workspace_dir, "THA", analysis_type)
str_reliable = save_reliable_concentrations(population, workspace_dir, "STR", analysis_type)
def save_tissue_proportions(population, workspace_dir, voxel_name):
list_spm = []
for subject in population:
# grab tissue proportion data for all subjects and dump into list
spm = pd.read_csv(
os.path.join(workspace_dir, subject, "svs_voxel_stats", "%s_voxel_statistics_spm.txt" % voxel_name),
header=None,
)
spm.insert(0, "SUBJECT", subject)
list_spm.append(spm)
# create concatenated dataframe for all tissue data in list
df_spm = pd.concat(list_spm, ignore_index=True)
df_spm.columns = [
"SUBJECT",
"%s_GM" % voxel_name,
"%s_WM" % voxel_name,
"%s_CSF" % voxel_name,
"%s_SUM" % voxel_name,
]
df_spm.to_csv(
os.path.join(
results_dir, "proportions_%s_%s_%s.csv" % (voxel_name, workspace_dir[-8:], workspace_dir[-10:-9])
)
)
return df_spm
print "2. Creating new dataframe with SPM tissue proportions for ACC,THA,STR"
acc_props = save_tissue_proportions(population, workspace_dir, "ACC")
tha_prpos = save_tissue_proportions(population, workspace_dir, "THA")
str_props = save_tissue_proportions(population, workspace_dir, "STR")
def calc_asbolute(lcmodel, frac_gm, frac_wm, frac_csf):
import math
# lcmodel correction factor
factor = 55.55 / (35.88 * 0.7)
# relative water content in tissue.. determined experimentally.
alpha_gm = 0.81 # 0.78
alpha_wm = 0.71 # 0.65
alpha_csf = 1.0 # 1.0
# attentuation factor for water
R_H2O_GM = (1.0 - math.e ** (-3000.0 / 1820.0)) * math.e ** (-30.0 / 99.0)
R_H2O_WM = (1.0 - math.e ** (-3000.0 / 1084.0)) * math.e ** (-30.0 / 69.0)
R_H2O_CSF = (1.0 - math.e ** (-3000.0 / 4163.0)) * math.e ** (-30.0 / 503.0)
######### Correction Equations #######
# tissel equation
Cmet1 = lcmodel * (((frac_csf * 1.0 * (1.0 - frac_csf)) + (frac_gm * 0.81 + frac_wm * 0.71)) / (1.0 - frac_csf))
# gusseuw equation
Cmet2 = (
(lcmodel)
* (
(
(frac_gm * alpha_gm * R_H2O_GM + frac_wm * alpha_wm * R_H2O_WM + frac_csf * alpha_csf * R_H2O_CSF)
/ (frac_gm * 1.0 + frac_wm * 1.0)
)
)
* factor
)
# gusseew csf equation
Cmet3 = (lcmodel) * (1 / (1 - frac_csf))
return Cmet2
def create_absolute_df(reliable, proportions, voxel_name):
cre = calc_asbolute(
reliable[" Cre"],
proportions["%s_GM" % voxel_name],
proportions["%s_WM" % voxel_name],
proportions["%s_CSF" % voxel_name],
)
cho = calc_asbolute(
reliable[" GPC+PCh"],
proportions["%s_GM" % voxel_name],
proportions["%s_WM" % voxel_name],
proportions["%s_CSF" % voxel_name],
)
naa = calc_asbolute(
reliable[" NAA+NAAG"],
proportions["%s_GM" % voxel_name],
proportions["%s_WM" % voxel_name],
proportions["%s_CSF" % voxel_name],
)
ino = calc_asbolute(
reliable[" mI"],
proportions["%s_GM" % voxel_name],
proportions["%s_WM" % voxel_name],
proportions["%s_CSF" % voxel_name],
)
glu = calc_asbolute(
reliable[" Glu"],
proportions["%s_GM" % voxel_name],
proportions["%s_WM" % voxel_name],
proportions["%s_CSF" % voxel_name],
)
gln = calc_asbolute(
reliable[" Gln"],
proportions["%s_GM" % voxel_name],
proportions["%s_WM" % voxel_name],
proportions["%s_CSF" % voxel_name],
)
glx = calc_asbolute(
reliable[" Glu+Gln"],
proportions["%s_GM" % voxel_name],
proportions["%s_WM" % voxel_name],
proportions["%s_CSF" % voxel_name],
)
absolute = pd.DataFrame(
{
"Subjects": reliable["Subject"],
"Cre": cre,
"GPC+PCh": cho,
"NAA+NAAG": naa,
"mI": ino,
"Glu": glu,
"Gln": gln,
"Glu+Gln": glx,
}
)
column_order = ["Subjects", "Cre", "GPC+PCh", "NAA+NAAG", "mI", "Glu", "Gln", "Glu+Gln"]
absolute = absolute.reindex(columns=column_order)
absolute.to_csv(
os.path.join(
results_dir,
"absolute_%s_%s_%s_%s.csv" % (analysis_type, voxel_name, workspace_dir[-8:], workspace_dir[-10:-9]),
)
)
print "3. Creating new dataframe with Absolute Concentrations for ACC,THA,STR"
create_absolute_df(acc_reliable, acc_props, "ACC")
create_absolute_df(tha_reliable, tha_prpos, "THA")
create_absolute_df(str_reliable, str_props, "STR")
def calculate_voxel_statistics(population, workspace_dir):
print '#############################################################################'
print ''
print ' RUNNNING PROJECT NMR-093%s %s' % (
workspace_dir[-10:-9], workspace_dir[-8:])
print ''
print '#############################################################################'
count = 0
for subject in population:
count += 1
print '========================================================================================'
print '%s- Calculating Voxel statistics for subject %s_%s' % (
count, subject, workspace_dir[-10:-9])
print '.'
# input files
spm_dir = os.path.join(workspace_dir, subject, 'segmentation_spm')
acc_mask = os.path.join(
workspace_dir, subject, 'svs_voxel_mask',
'%s%s_ACC_RDA_MASK.nii' % (subject, workspace_dir[-10:-9]))
tha_mask = os.path.join(
workspace_dir, subject, 'svs_voxel_mask',
'%s%s_THA_RDA_MASK.nii' % (subject, workspace_dir[-10:-9]))
str_mask = os.path.join(
workspace_dir, subject, 'svs_voxel_mask',
'%s%s_STR_RDA_MASK.nii' % (subject, workspace_dir[-10:-9]))
spm_gm = os.path.join(spm_dir, 'TISSUE_CLASS_1_GM_OPTIMIZED.nii.gz')
spm_wm = os.path.join(spm_dir, 'TISSUE_CLASS_2_WM_OPTIMIZED.nii.gz')
spm_cm = os.path.join(spm_dir, 'TISSUE_CLASS_3_CSF_OPTIMIZED.nii.gz')
# output folders
mkdir_path(os.path.join(workspace_dir, subject, 'svs_voxel_stats'))
stats_dir = os.path.join(workspace_dir, subject, 'svs_voxel_stats')
print 'Calculating tissue proportions'
def calc_props(mask_file, seg_gm, seg_wm, seg_cm, voxel_name):
if not os.path.isfile(mask_file):
print 'IOError: [Errno 2] SVS %s mask Does not exist, create masks and come back' % voxel_name
else:
if os.path.isfile(
os.path.join(
stats_dir,
'%s_voxel_statistics_spm_opt.txt' % voxel_name)):
print 'Voxel statistics already calculated ... moving on'
else:
#grab data
spm_gm_data = nb.load(seg_gm).get_data().squeeze()
spm_wm_data = nb.load(seg_wm).get_data().squeeze()
spm_cm_data = nb.load(seg_cm).get_data().squeeze()
vox_data = nb.load(mask_file).get_data().squeeze()
#multiply SVS ROI with segmented data for ACC
vox_spm_gm = vox_data * spm_gm_data
vox_spm_wm = vox_data * spm_wm_data
vox_spm_cm = vox_data * spm_cm_data
#extract stats from segmentation for acc
vox_total_svs = np.sum(vox_data)
vox_total_spm_gm = np.sum(vox_spm_gm)
vox_total_spm_wm = np.sum(vox_spm_wm)
vox_total_spm_cm = np.sum(vox_spm_cm)
percent_svs = float(vox_total_svs) / float(vox_total_svs)
percent_spm_gm = np.round(
float(vox_total_spm_gm) / float(vox_total_svs), 3)
percent_spm_wm = np.round(
float(vox_total_spm_wm) / float(vox_total_svs), 3)
percent_spm_cm = np.round(
float(vox_total_spm_cm) / float(vox_total_svs), 3)
sum_spm = np.round(
float(percent_spm_gm + percent_spm_wm +
percent_spm_cm), 3)
print '%s.....' % voxel_name
print '...%s SPM NewSegment Tissue Proportions = %s%% GM, %s%% WM, %s%% CSF = %s' % (
voxel_name, percent_spm_gm, percent_spm_wm,
percent_spm_cm, sum_spm)
spm_txt = os.path.join(
stats_dir,
'%s_voxel_statistics_spm_opt.txt' % voxel_name)
write_spm = open(spm_txt, 'w')
write_spm.write('%s, %s, %s, %s' %
(percent_spm_gm, percent_spm_wm,
percent_spm_cm, sum_spm))
write_spm.close()
calc_props(acc_mask, spm_gm, spm_wm, spm_cm, 'ACC')
calc_props(tha_mask, spm_gm, spm_wm, spm_cm, 'THA')
calc_props(str_mask, spm_gm, spm_wm, spm_cm, 'STR')
print '========================================================================================'
def scrub_data(population, workspace_dir):
scub_subjects = []
for subject in population:
print '###############################################################################'
print 'Scrubbing Denoised Data for subject %s' %subject
print ''
#input
subject_dir = os.path.join(workspace_dir, subject)
native_residual_compor = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_NATIVE_detrend_compcor_friston_bp_fwhm.nii.gz')
native_residual_wmcsf = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_NATIVE_detrend_wmcsf_friston_bp_fwhm.nii.gz')
native_residual_global = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_NATIVE_detrend_global_wmcsf_friston_bp_fwhm.nii.gz')
mni_residual_compor = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_MNI2mm_detrend_compcor_friston_bp_fwhm.nii.gz')
mni_residual_wmcsf = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_MNI2mm_detrend_wmcsf_friston_bp_fwhm.nii.gz')
mni_residual_global = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_MNI2mm_detrend_global_wmcsf_friston_bp_fwhm.nii.gz')
mni_aroma_compcor = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_MNI2mm_FWHM_AROMA_detrend_compcor_friston_bp.nii.gz')
mni_aroma_wmcsf = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_MNI2mm_FWHM_AROMA_detrend_wmcsf_friston_bp.nii.gz')
mni_aroma_global = os.path.join(subject_dir, 'FUNC_DENOISE/RESIDUAL_MNI2mm_FWHM_AROMA_detrend_global_wmcsf_friston_bp.nii.gz')
#output
scrub_dir = os.path.join(subject_dir,'FUNC_DENOISE_SCRUB')
mkdir_path(scrub_dir)
os.chdir(scrub_dir)
# scrubbing function
def scrub(denoised_img, denoised_name):
print '..Scrubbing Denoised image'
print '----->', denoised_name[9:]
scrubbed = os.path.join(scrub_dir , '%s_scrubbed.nii.gz'%denoised_name)
if not os.path.isfile(scrubbed):
os.system("3dcalc -a %s%s -expr 'a' -prefix %s" %(denoised_img, in_frames_string, scrubbed))
# get good frames amd limit to 150
in_frames = []
fd1d = np.genfromtxt(os.path.join(subject_dir,'QUALITY_CONTROL/FD.1D'))
for frame, fd in enumerate(fd1d):
if fd < 0.2:
in_frames.append(frame)
if len(in_frames) > 150:
print '..Subject has more than 150 Good Frames (3.6 mins)'
in_frames_string = str(in_frames[0:150]).replace(" ","")
else:
print 'Subject has less than 150 Good frames'
# run scrubbing
scrub(native_residual_compor, 'RESIDUAL_NATIVE_detrend_compcor_friston_bp_fwhm')
scrub(native_residual_wmcsf, 'RESIDUAL_NATIVE_detrend_wmcsf_friston_bp_fwhm')
scrub(native_residual_global, 'RESIDUAL_NATIVE_detrend_global_wmcsf_friston_bp_fwhm')
scrub(mni_residual_compor, 'RESIDUAL_MNI2mm_detrend_compcor_friston_bp_fwhm')
scrub(mni_residual_wmcsf, 'RESIDUAL_MNI2mm_detrend_wmcsf_friston_bp_fwhm')
scrub(mni_residual_global, 'RESIDUAL_MNI2mm_detrend_global_wmcsf_friston_bp_fwhm')
scrub(mni_aroma_compcor, 'RESIDUAL_MNI2mm_FWHM_AROMA_detrend_compcor_friston_bp')
scrub(mni_aroma_wmcsf, 'RESIDUAL_MNI2mm_FWHM_AROMA_detrend_wmcsf_friston_bp')
scrub(mni_aroma_global, 'RESIDUAL_MNI2mm_FWHM_AROMA_detrend_global_wmcsf_friston_bp')
def create_voxel_plots(voxel_name, ppmst):
print '...Working on %s %s ' % (analysis_type, ppmst)
#create output QC directory
mkdir_path(
os.path.join(subject_dir, 'quality_control', analysis_type,
voxel_name, 'ppm_%s' % ppmst, 'tmp'))
qc_dir = os.path.join(subject_dir, 'quality_control',
analysis_type, voxel_name, 'ppm_%s' % ppmst)
tmp_dir = os.path.join(qc_dir, 'tmp')
#grab lcmodel plots
svs_lcmodel = os.path.join(subject_dir,
'lcmodel_%s' % analysis_type,
voxel_name, 'ppm_%s' % ppmst, 'ps.pdf')
# create localization pngs
make_png = [
'convert', '-density', '300', '-trim',
'%s' % svs_lcmodel, '-quality', '300', '-sharpen', '0x1.0',
'%s/%s_lcmodel.png' % (tmp_dir, voxel_name)
]
subprocess.call(make_png)
lcm_plot = os.path.join(tmp_dir, '%s_lcmodel-0.png' % voxel_name)
#grab snr/fwhm data
svs_snr = np.genfromtxt(os.path.join(subject_dir,
'lcmodel_%s' % analysis_type,
voxel_name, 'ppm_%s' % ppmst,
'snr.txt'),
delimiter=',')
#grab voxel mask
svs = os.path.join(
subject_dir, 'svs_voxel_mask', '%s%s_%s_RDA_MASK.nii' %
(subject, workspace_dir[-10:-9], voxel_name))
#get data into matrix
anat_load = nb.load(anatomical)
svs_load = nb.load(svs)
anat_data = anat_load.get_data()
svs_data = svs_load.get_data()
# get svs cut coords
coords = find_cut_coords(svs_load)
# convert zeros to nans for visualization purposes
svs_data[svs_data == 0] = np.nan
# plot voxel on anat
fig = plt.figure()
fig.set_size_inches(6.5, 6.5)
fig.subplots_adjust(wspace=0.005)
#1
ax1 = plt.subplot2grid((1, 3), (0, 0), colspan=1, rowspan=1)
ax1.imshow(anat_data[coords[0], :, :], matplotlib.cm.bone_r)
ax1.imshow(svs_data[coords[0], :, :],
matplotlib.cm.rainbow_r,
alpha=0.7)
ax1.set_xlim(23, 157)
ax1.set_ylim(101, 230)
ax1.axes.get_yaxis().set_visible(False)
ax1.axes.get_xaxis().set_visible(False)
#2
ax2 = plt.subplot2grid((1, 3), (0, 1), colspan=1, rowspan=1)
ax2.imshow(np.rot90(anat_data[:, :, coords[2]]),
matplotlib.cm.bone_r)
ax2.imshow(np.rot90(svs_data[:, :, coords[2]]),
matplotlib.cm.rainbow_r,
alpha=0.7)
ax2.set_xlim(230, 20)
ax2.set_ylim(207, 4)
ax2.axes.get_yaxis().set_visible(False)
ax2.axes.get_xaxis().set_visible(False)
#3
ax3 = plt.subplot2grid((1, 3), (0, 2), colspan=1, rowspan=1)
ax3.imshow(anat_data[:, coords[1], :],
matplotlib.cm.bone_r,
origin='lower')
ax3.imshow(svs_data[:, coords[1], :],
matplotlib.cm.rainbow_r,
alpha=0.7,
origin='lower')
ax3.set_xlim(38, 140)
ax3.set_ylim(160, 60)
ax3.axes.get_yaxis().set_visible(False)
ax3.axes.get_xaxis().set_visible(False)
fig.tight_layout()
fig.savefig('%s/localization_%s.png' % (qc_dir, voxel_name),
dpi=200,
bbox_inches='tight')
# create qc report
report = canvas.Canvas(os.path.join(
qc_dir, 'QC_REPORT_%s.pdf' % voxel_name),
pagesize=(1280, 1556))
report.setFont("Helvetica", 40)
report.drawImage(
os.path.join(qc_dir, 'localization_%s.png' % voxel_name), 1,
inch * 13.5)
report.drawImage(lcm_plot, 30, inch * 1, width=1200, height=800)
report.drawString(
230, inch * 20, ' %s%s, %s_%s , SNR=%s FWHM=%s ' %
(subject, workspace_dir[-10:-9], voxel_name, analysis_type,
svs_snr[2], svs_snr[1]))
report.showPage()
if analysis_type is 'twix':
fig_f6 = os.path.join(subject_dir, 'svs_twix', voxel_name,
'f6_frequency_drift_correction.png')
fig_f7 = os.path.join(subject_dir, 'svs_twix', voxel_name,
'f7_estimated_phase_phase_drift.png')
fig_f8 = os.path.join(subject_dir, 'svs_twix', voxel_name,
'f8_estimated_frequency_drift.png')
reader = open(
os.path.join(subject_dir, 'svs_twix', voxel_name,
voxel_name, 'readme.txt'), 'r')
for line in reader:
if 'bad' in line:
badavn = line[34:38]
report.drawImage(fig_f6, 1, inch * 7.2)
report.drawImage(fig_f7, 90, inch * 1, width=540, height=450)
report.drawImage(fig_f8, 590, inch * 1, width=540, height=450)
report.setFont("Helvetica", 40)
report.drawString(350, inch * 20,
'Number of Bad Averages =%s' % (badavn))
report.save()
else:
report.save()
def run_lcmodel_on_voxel(voxel_name, ppmst):
#define input and output directories
svs_dir = os.path.join(workspace_dir, subject, 'svs_rda')
rda_met = os.path.join(svs_dir, voxel_name, 'met', '%s%s_%s_SUPPRESSED.rda' %(subject, workspace_dir[-10:-9], voxel_name))
rda_h2o = os.path.join(svs_dir, voxel_name, 'h2o', '%s%s_%s_WATER.rda' %(subject, workspace_dir[-10:-9], voxel_name))
mkdir_path(os.path.join(workspace_dir, subject, 'lcmodel_rda', voxel_name, 'ppm_%s'%ppmst, 'met'))
mkdir_path(os.path.join(workspace_dir, subject, 'lcmodel_rda', voxel_name, 'ppm_%s'%ppmst, 'h2o'))
lcmodel_dir = os.path.join(workspace_dir, subject, 'lcmodel_rda', voxel_name, 'ppm_%s'%ppmst)
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
RUN LCMODEL BIN2RAW
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
if os.path.isfile(os.path.join(lcmodel_dir, 'met', 'RAW')) and os.path.isfile(os.path.join(lcmodel_dir, 'h2o', 'RAW')):
pass
#print 'Bin2raw already run.........................moving on '
else:
print ' Generating RAW frequency files with BIN2RAW'
met_bin2raw = ['/home/raid3/kanaan/.lcmodel/siemens/bin2raw', '%s'%rda_met, '%s/'%lcmodel_dir, 'met']
h2o_bin2raw = ['/home/raid3/kanaan/.lcmodel/siemens/bin2raw', '%s'%rda_h2o, '%s/'%lcmodel_dir, 'h2o']
subprocess.call(met_bin2raw)
subprocess.call(h2o_bin2raw)
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
Read Scan parameters from RDA file
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
reader = open(rda_met, 'r')
for line in reader:
if 'SeriesDescription' in line:
Series = line[19:30]
elif 'TR:' in line:
TR = line[4:8]
elif 'TE:' in line:
TE = line[4:6]
elif 'VectorSize' in line:
nfil = line[12:16]
elif 'NumberOfAverages' in line:
NS = line[18:21]
elif 'AcquisitionNumber' in line:#
ACQ = line[19:21]
elif 'PatientSex' in line:
Sex = line[12:13]
elif 'SeriesNumber' in line:
Seriesnum = line[14:16]
elif 'PatientAge' in line:#
Age = line[12:15]
elif 'PatientWeight' in line:
Weight = line[15:17]
elif 'PixelSpacingRow' in line:
PSR = float(line[17:20])
elif 'PixelSpacingCol' in line:
PSC = float(line[17:20])
elif 'PixelSpacing3D:' in line:
PS3d = float(line[16:19])
elif 'StudyDate' in line:
datex = line[0:19]
volume = np.round(((PSR * PSC * PS3d) / 1000),2)
header = open(os.path.join(lcmodel_dir, 'rda_header.txt'), 'w')
header.write('%s(%s %s %skg); %s; %s %sx%sx%s=%s; TR/TE/NS=%s/%s/%s'
%(subject, Sex, Age, Weight, datex, voxel_name, PSR,PSC,PS3d,volume, TR,TE, NS))
header.close()
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
Building the control file
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
if os.path.isfile(os.path.join(lcmodel_dir, 'control')):
pass
#print 'Control file already created................moving on'
else:
print 'Processing Spectra with LCMODEL'
print '...building control file'
file = open(os.path.join(lcmodel_dir, 'control'), "w")
file.write(" $LCMODL\n")
file.write(" title= 'RDA - %s(%s %s %skg), %s, %s %sx%sx%s, TR/TE/NS=%s/%s/%s' \n" %(subject, Sex, Age, Weight, datex, voxel_name, PSR,PSC,PS3d, TR,TE, NS ))
file.write(" srcraw= '%s' \n" %rda_met)
file.write(" srch2o= '%s' \n" %rda_h2o)
file.write(" savdir= '%s' \n" %lcmodel_dir)
file.write(" ppmst= %s \n"%ppmst)
file.write(" ppmend= 0.3\n")
file.write(" nunfil= %s\n"%nfil)
file.write(" ltable= 7\n")
file.write(" lps= 8\n")
file.write(" lcsv= 11\n")
file.write(" lcoraw= 10\n")
file.write(" lcoord= 9\n")
file.write(" hzpppm= 1.2328e+02\n")
file.write(" filtab= '%s/table'\n" %lcmodel_dir)
file.write(" filraw= '%s/met/RAW'\n" %lcmodel_dir)
file.write(" filps= '%s/ps'\n" %lcmodel_dir)
file.write(" filh2o= '%s/h2o/RAW'\n" %lcmodel_dir)
file.write(" filcsv= '%s/spreadsheet.csv'\n" %lcmodel_dir)
file.write(" filcor= '%s/coraw'\n" %lcmodel_dir)
file.write(" filcoo= '%s/coord'\n" %lcmodel_dir)
file.write(" filbas= '/home/raid3/kanaan/.lcmodel/basis-sets/press_te30_3t_01a.basis'\n")
file.write(" echot= %s.00 \n" %TE)
file.write(" dows= T \n")
file.write(" doecc= T\n")
file.write(" deltat= 8.330e-04\n")
file.write(" $END\n")
file.close()
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
Execute quantitation.... run standardA4pdf
'''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''''
if os.path.isfile(os.path.join(lcmodel_dir, 'spreadsheet.csv')):
print 'Spectrum already processed .................moving on'
else:
print '...running standardA4pdf execution-script '
print ''
lcmodel_command = ['/bin/sh','/home/raid3/kanaan/.lcmodel/execution-scripts/standardA4pdfv3',
'%s' %lcmodel_dir,'19','%s' %lcmodel_dir, '%s' %lcmodel_dir]
print subprocess.list2cmdline(lcmodel_command)
print ''
subprocess.call(lcmodel_command)
reader = open(os.path.join(lcmodel_dir, 'table'), 'r')
for line in reader:
if 'FWHM' in line:
fwhm = float(line[9:14])
snrx = line[29:31]
fwhm_hz = fwhm * 123.24
if 'Data shift' in line:
shift = line[15:21]
if 'Ph:' in line:
ph0 = line[6:10]
ph1 = line[19:24]
filex = open(os.path.join(lcmodel_dir, 'snr.txt'), "w")
filex.write('%s, %s, %s, %s, %s, %s' %(fwhm,fwhm_hz, snrx, shift, ph0, ph1))
filex.close()
def calc_ecm(population, workspace_dir):
# all_gm = []
#
# if not os.path.isfile(os.path.join(workspace_dir, 'GluConnectivity', 'COMBINED_GM_MASK.nii.gz')):
# print 'Creating Group GM mask'
# for subject in population:
#
# # input and output folders
# subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
# out_mask = os.path.join(workspace_dir, 'GluConnectivity', 'COMBINED_GM_MASK.nii.gz')
# MNI2mm_gm = os.path.join(subject_dir , 'anatomical_MNI2mm_tissue_gm/TISSUE_CLASS_1_GM_OPTIMIZED_resample_warp_thresh.nii.gz')
#
# if os.path.isfile(MNI2mm_gm):
# all_gm.append(MNI2mm_gm)
#
# maths_input = []
# for i in all_gm:
# x = '-add %s'%i
# maths_input.append(x)
#
# maths_string = ' '.join(maths_input)[5:]
# os.system('fslmaths %s -mul /usr/share/fsl/data/standard/MNI152_T1_2mm_brain_mask.nii.gz %s'%(maths_string, out_mask))
#
# out_mask_4mm = os.path.join(workspace_dir, 'GluConnectivity', 'COMBINED_GM_MASK_4mm.nii.gz')
# mni_4mm = '/SCR/ROI/brain_4mm.nii.gz'
# os.system('flirt -in %s -ref %s -out %s -applyisoxfm 4' %(out_mask, mni_4mm, out_mask_4mm ))
for subject in population:
print 'Running Subject %s' % subject
# input and output folders
subject_dir = os.path.join(workspace_dir, 'GluConnectivity', subject)
outdir = os.path.join(subject_dir, 'ECM_LIPSIA')
mkdir_path(outdir)
os.chdir(outdir)
# TRANSFORM NATIVE IMAGE TO MNI2mm
mkdir_path(
os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp'))
pproc = os.path.join(
subject_dir,
'functional_native_brain_preproc_FWHM_AROMA_residual_bp/bandpassed_demeaned_filtered.nii.gz'
)
pproc_2mm = os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/pproc.nii.gz'
)
anat2mni = os.path.join(
subject_dir,
'anatomical_MNI2mm_xfm/MP2RAGE_DESKULL_RPI_resample_ero_fieldwarp.nii.gz'
)
func2anat = os.path.join(
subject_dir,
'functional_ANAT2mm_xfm/REST_calc_resample_corrected_volreg_maths_tstat_flirt.mat'
)
if not os.path.isfile(pproc_2mm):
if os.path.isfile(pproc):
print '... Warping to MNI'
os.system(' '.join([
'applywarp', '--in=' + pproc, '--ref=' +
'/usr/share/fsl/5.0/data/standard/MNI152_T1_2mm_brain.nii.gz',
'--out=' + pproc_2mm, '--warp=' + anat2mni,
'--premat=' + func2anat
]))
# Convert VISTA to NIFTI
pproc_2mmv = os.path.join(
subject_dir,
'functional_MNI2mm_brain_preproc_FWHM_AROMA_residual_bp/pproc.v')
if not os.path.isfile(pproc_2mmv):
print '...Converting NIFTI to VISTA.. make sure you are running this on telemann'
os.system('isisconv -in %s -out %s' % (pproc_2mm, pproc_2mmv))
def construct_features_dataframe(derivatives_dir, control_outliers, patients_outliers, rsfc_seeds):
print '========================================================================================'
print ''
print ' Tourettome - 009. Create Feature Dataframes '
print ''
print '========================================================================================'
print ''
#I/O
features_dir = mkdir_path(os.path.join(derivatives_dir, 'feature_matrices'))
########################################################################################################
print '###########################################################'
print ' Inspecting sample size'
df_pheno = pd.read_csv(os.path.join(tourettome_phenotypic, 'tourettome_phenotypic.csv'),
index_col=0).drop(control_outliers+patients_outliers,axis =0)
population = df_pheno.index
# Extract groups
patients = sorted([i for i in population if df_pheno.loc[i]['Group'] == 'patients'])
controls = sorted([i for i in population if df_pheno.loc[i]['Group'] == 'controls'])
tourettome_subjects = sorted(controls + patients)
# create group phenotypic dataframes
df_pheno_controls = df_pheno.drop([i for i in df_pheno.index if i not in controls], axis=0)
df_pheno_patients = df_pheno.drop([i for i in df_pheno.index if i not in patients], axis=0)
df_pheno_controls = df_pheno_controls.drop([i for i in df_pheno_controls.columns if i not in terms], axis=1)
df_pheno_patients = df_pheno_patients.drop([i for i in df_pheno_patients.columns if i not in terms], axis=1)
df_pheno_controls.to_csv(os.path.join(tourettome_phenotypic, 'tourettome_phenotypic_controls.csv'))
df_pheno_patients.to_csv(os.path.join(tourettome_phenotypic, 'tourettome_phenotypic_patients.csv'))
# Included subjects
print 'n_controls=', len(controls)
print 'n_patients=', len(patients)
print 'n_total =', len(tourettome_subjects)
print ''
# Outliers
print 'n_control_outliers=', len(control_outliers)
print 'n_patients_outliers=', len(patient_outliers)
# print 'n_total_outliers =', len([i for i in control_outliers+patient_outliers if i not in hamburg])
print 'n_total_outliers =', len(control_outliers+patient_outliers)
print ''
#######################################################################################################
print '###########################################################'
print '... Extracting SCA, CT & ECM data for QCd tourettome population'
###################
# SCA
if not os.path.isfile(os.path.join(features_dir, 'sca_tourettome_raw.csv')):
print '...... Checking SCA data'
sca_tourettome_raw = []
for seed_name in rsfc_seeds:
print '......... %s'%seed_name
sca_tourettome_raw.append(return_sca_data(seed_name, tourettome_subjects, derivatives_dir))
# Save raw dataframes
sca_tourettome_raw = pd.concat(sca_tourettome_raw)
sca_tourettome_raw.to_csv(os.path.join(features_dir, 'sca_tourettome_raw.csv'))
plt_features_heatmap(sca_tourettome_raw, os.path.join(features_dir, 'sca_tourettome_raw.png'), vmin=-1,vmax=1)
else:
sca_tourettome_raw = pd.read_csv(os.path.join(features_dir, 'sca_tourettome_raw.csv'), index_col=0)
###################
# CT
if not os.path.isfile(os.path.join(features_dir, 'ct_tourettome_raw.csv')):
print '...... Checking CT data'
# only take subjects that have ct and are not outliers
tourettome_subjects_ct = np.unique([i[0:5] for i in os.listdir(os.path.join(tourettome_derivatives, 'struct_cortical_thickness'))])
tourettome_subjects_ct = [i for i in tourettome_subjects_ct if i not in control_outliers + patient_outliers]
#check data and save
ct_tourettome_raw= return_ct_data(tourettome_subjects_ct, tourettome_derivatives)
ct_tourettome_raw.to_csv(os.path.join(features_dir, 'ct_tourettome_raw.csv'))
plt_features_heatmap(ct_tourettome_raw, os.path.join(features_dir, 'ct_tourettome_raw.png'), vmin=-0, vmax=4)
else:
ct_tourettome_raw = pd.read_csv(os.path.join(features_dir, 'ct_tourettome_raw.csv'),index_col=0)
###################
# ECM
if not os.path.isfile(os.path.join(features_dir, 'ecm_tourettome_raw.csv')):
print '...... Checking ECM data'
# check data and save
ecm_tourettome_raw = return_ecm_data(tourettome_subjects, tourettome_derivatives)
ecm_tourettome_raw.to_csv(os.path.join(features_dir, 'ecm_tourettome_raw.csv'))
plt_features_heatmap(ecm_tourettome_raw, os.path.join(features_dir, 'ecm_tourettome_raw.png'), vmin=-1, vmax=1)
else:
ecm_tourettome_raw = pd.read_csv(os.path.join(features_dir, 'ecm_tourettome_raw.csv'), index_col=0)
############################################################################################################
print '###########################################################'
print '... Building Design Matrix'
if not os.path.isfile(os.path.join(features_dir, 'design_matrix_tourettome.csv')):
# Create design matrix
design_matrix = pd.DataFrame(index=sca_tourettome_raw.columns)
design_matrix['Age'] = df_pheno['Age']
def make_dmat_category(old_col, new_col):
for subject in design_matrix.index:
if df_pheno.loc[subject][old_col] == new_col:
design_matrix.loc[subject, new_col] = 1
else:
design_matrix.loc[subject, new_col] = 0
make_dmat_category('Sex', 'male')
make_dmat_category('Sex', 'female')
make_dmat_category('Site', 'HANNOVER_A')
make_dmat_category('Site', 'HANNOVER_B')
#make_dmat_category('Site', 'HAMBURG')
make_dmat_category('Site', 'Leipzig')
make_dmat_category('Site', 'PARIS')
design_matrix['FD'] = df_pheno['qc_func_fd']
#design_matrix['CJV'] = df_pheno['qc_anat_cjv']
# design_matrix['DVARS'] = df_pheno['qc_func_dvars']
# design_matrix['TSNR'] = df_pheno['qc_func_tsnr']
# Save design matrix data
design_matrix.to_csv(os.path.join(features_dir, 'design_matrix_tourettome.csv'))
# Plot design matrix
f = plt.figure(figsize=(12, 8))
for i in ['Age', 'FD']:
design_matrix[i] = preprocessing.scale(design_matrix[i])
sns.heatmap(design_matrix, yticklabels=False, cmap=cmap_gradient, vmin=-2.5, vmax=2.5)
plt.xticks(size=20, rotation=90, weight='bold')
f.savefig(os.path.join(features_dir, 'design_matrix_tourettome.png'), dpi = 300, bbox_inches='tight')
design_matrix = pd.read_csv(os.path.join(features_dir, 'design_matrix_tourettome.csv'), index_col = 0)
########################################################################################################
print '###########################################################'
print '... Denoising SCA features'
if not os.path.isfile(os.path.join(features_dir, 'sca_patients_resid_z.csv')):
#####################
# Regress
if not os.path.isfile(os.path.join(features_dir, 'sca_tourettome_resid.csv')):
print '...... Regressing nuisace variables for SCA dataframes'
sca_tourettome_resid = regress_nuisance_covariates(sca_tourettome_raw, design_matrix, formula)
# save residual data
sca_tourettome_resid = pd.concat(sca_tourettome_resid, axis=1).T # transpose here to get back to RAW shape
sca_tourettome_resid.to_csv(os.path.join(features_dir, 'sca_tourettome_resid.csv'))
# plot sca residuals
plt_features_heatmap(sca_tourettome_resid, os.path.join(features_dir, 'sca_tourettome_resid.png'),
vmin=-2,vmax=2)
#####################
# Break down sca_tourettome_resid to patient and control dataframes
if not os.path.isfile(os.path.join(features_dir, 'sca_patients_resid.csv')):
sca_tourettome_resid = pd.read_csv(os.path.join(features_dir, 'sca_tourettome_resid.csv'), index_col =0)
sca_patients_resid = sca_tourettome_resid.drop(controls, axis=1)
sca_controls_resid = sca_tourettome_resid.drop(patients, axis=1)
# save separately
sca_patients_resid.to_csv(os.path.join(features_dir, 'sca_patients_resid.csv'))
sca_controls_resid.to_csv(os.path.join(features_dir, 'sca_controls_resid.csv'))
# plot separate sca residuals
plt_features_heatmap(sca_controls_resid, os.path.join(features_dir, 'sca_controls_resid.png'),
vmin=-1, vmax=1, figsize=(17.5, 10))
plt_features_heatmap(sca_patients_resid, os.path.join(features_dir, 'sca_patients_resid.png'),
vmin=-1, vmax=1, figsize=(17.5, 10))
else:
sca_controls_resid = pd.read_csv(os.path.join(features_dir, 'sca_controls_resid.csv'), index_col=0)
sca_patients_resid = pd.read_csv(os.path.join(features_dir, 'sca_patients_resid.csv'), index_col=0)
#####################
# Z-Score
if not os.path.isfile(os.path.join(features_dir, 'sca_patients_resid_z.csv')):
print ' ... Z-scoring SCA dataframes'
sca_controls_resid_z, sca_patients_resid_z = z_score_features(sca_controls_resid, sca_patients_resid)
# save data
sca_controls_resid_z.to_csv(os.path.join(features_dir, 'sca_controls_resid_z.csv'))
sca_patients_resid_z.to_csv(os.path.join(features_dir, 'sca_patients_resid_z.csv'))
plt_features_heatmap(sca_controls_resid_z, os.path.join(features_dir, 'sca_controls_resid_z.png'),
vmin=-4, vmax=4, figsize=(17.5, 10))
plt_features_heatmap(sca_patients_resid_z, os.path.join(features_dir, 'sca_patients_resid_z.png'),
vmin=-4, vmax=4, figsize=(17.5, 10))
else:
print 'SCA features already denoised'
########################################################################################################
print '###########################################################'
print '... Denoising CT features'
if not os.path.isfile(os.path.join(features_dir, 'ct_patients_resid_z.csv')):
#####################
# Drop CT subjects from design_matrix
print '...... Dropping subjects with no freesurfer segmentation'
print '...... Regressing nuisace variables for CT dataframes'
# first drop subjects from design matrix that dont have CT----- due to freesurfer failure. get those subs back
design_matrix = pd.read_csv(os.path.join(features_dir, 'design_matrix_tourettome.csv'), index_col=0)
design_matrix = design_matrix.drop([i for i in design_matrix.index if i not in ct_tourettome_raw.columns],
axis=0)
#####################
# Regress
if not os.path.isfile(os.path.join(features_dir, 'ct_tourettome_resid.csv')):
print '...... Regressing nuisace variables for CT dataframes'
ct_tourettome_resid = regress_nuisance_covariates(ct_tourettome_raw, design_matrix, formula)
# save residual data
ct_tourettome_resid = pd.concat(ct_tourettome_resid, axis=1).T # transpose here to get back to RAW shape
ct_tourettome_resid.to_csv(os.path.join(features_dir, 'ct_tourettome_resid.csv'))
# plot ct residuals
plt_features_heatmap(ct_tourettome_resid, os.path.join(features_dir, 'ct_tourettome_resid.png'),
vmin=-2, vmax=2)
#####################
# Break Down mats into patients and controls
if not os.path.isfile(os.path.join(features_dir, 'ct_patients_resid.csv')):
# Break down ct_tourettome_resid to patient and control dataframes
ct_tourettome_resid = pd.read_csv(os.path.join(features_dir, 'ct_tourettome_resid.csv'), index_col=0)
ct_patients_resid = ct_tourettome_resid.drop([i for i in controls if i in ct_tourettome_resid.columns], axis=1)
ct_controls_resid = ct_tourettome_resid.drop([i for i in patients if i in ct_tourettome_resid.columns], axis=1)
# save separately
ct_patients_resid.to_csv(os.path.join(features_dir, 'ct_patients_resid.csv'))
ct_controls_resid.to_csv(os.path.join(features_dir, 'ct_controls_resid.csv'))
# plot separate sca residuals
plt_features_heatmap(ct_controls_resid, os.path.join(features_dir, 'ct_controls_resid.png'),
vmin=-1, vmax=1, figsize=(17.5, 10))
plt_features_heatmap(ct_patients_resid, os.path.join(features_dir, 'ct_patients_resid.png'),
vmin=-1, vmax=1, figsize=(17.5, 10))
else:
ct_controls_resid = pd.read_csv(os.path.join(features_dir, 'ct_controls_resid.csv'), index_col=0)
ct_patients_resid = pd.read_csv(os.path.join(features_dir, 'ct_patients_resid.csv'), index_col=0)
#####################
# Z-Score
if not os.path.isfile(os.path.join(features_dir, 'ct_patients_resid_z.csv')):
print ' ... Z-scoring SCA dataframes'
ct_controls_resid_z, ct_patients_resid_z = z_score_features(ct_controls_resid, ct_patients_resid)
# save data
ct_controls_resid_z.to_csv(os.path.join(features_dir, 'ct_controls_resid_z.csv'))
ct_patients_resid_z.to_csv(os.path.join(features_dir, 'ct_patients_resid_z.csv'))
plt_features_heatmap(ct_controls_resid_z, os.path.join(features_dir, 'ct_controls_resid_z.png'),
vmin=-4, vmax=4, figsize=(17.5, 10))
plt_features_heatmap(ct_patients_resid_z, os.path.join(features_dir, 'ct_patients_resid_z.png'),
vmin=-4, vmax=4, figsize=(17.5, 10))
else:
print 'CT features already denoised'
########################################################################################################
print '###########################################################'
print '... Denoising ECM features'
if not os.path.isfile(os.path.join(features_dir, 'ecm_patients_resid_z.csv')):
#####################
# Regress
if not os.path.isfile(os.path.join(features_dir, 'ecm_tourettome_resid.csv')):
print '...... Regressing nuisace variables for ECM dataframes'
# must read dmat again since we dropped missing subjects from ct
design_matrix = pd.read_csv(os.path.join(features_dir, 'design_matrix_tourettome.csv'), index_col=0)
ecm_tourettome_resid = regress_nuisance_covariates(ecm_tourettome_raw, design_matrix, formula)
# save residual data
ecm_tourettome_resid = pd.concat(ecm_tourettome_resid, axis=1).T # transpose here to get back to RAW shape
ecm_tourettome_resid.to_csv(os.path.join(features_dir, 'ecm_tourettome_resid.csv'))
plt_features_heatmap(ecm_tourettome_resid, os.path.join(features_dir, 'ecm_tourettome_resid.png'),vmin=-2,vmax=2)
#####################
# Break Down mats into patients and controls
if not os.path.isfile(os.path.join(features_dir, 'ecm_patients_resid.csv')):
ecm_patients_resid = ecm_tourettome_resid.drop(controls, axis=1)
ecm_controls_resid = ecm_tourettome_resid.drop(patients, axis=1)
# save separately
ecm_patients_resid.to_csv(os.path.join(features_dir, 'ecm_patients_resid.csv'))
ecm_controls_resid.to_csv(os.path.join(features_dir, 'ecm_controls_resid.csv'))
# plot separate sca residuals
plt_features_heatmap(ecm_controls_resid, os.path.join(features_dir, 'ecm_controls_resid.png'),
vmin=-1, vmax=1, figsize=(17.5, 10))
plt_features_heatmap(ecm_patients_resid, os.path.join(features_dir, 'ecm_patients_resid.png'),
vmin=-1, vmax=1, figsize=(17.5, 10))
else:
ecm_controls_resid = pd.read_csv(os.path.join(features_dir, 'ecm_controls_resid.csv'), index_col=0)
ecm_patients_resid = pd.read_csv(os.path.join(features_dir, 'ecm_patients_resid.csv'), index_col=0)
#####################
# Z-Score
if not os.path.isfile(os.path.join(features_dir, 'ecm_patients_resid_z.csv')):
print ' ... Z-scoring ECM dataframes'
ecm_controls_resid_z, ecm_patients_resid_z = z_score_features(ecm_controls_resid, ecm_patients_resid)
ecm_controls_resid_z.to_csv(os.path.join(features_dir, 'ecm_controls_resid_z.csv'))
ecm_patients_resid_z.to_csv(os.path.join(features_dir, 'ecm_patients_resid_z.csv'))
# plot separate sca residuals
plt_features_heatmap(ecm_controls_resid_z, os.path.join(features_dir, 'ecm_controls_resid_z.png'),
vmin=-4, vmax=4, figsize=(17.5, 10))
plt_features_heatmap(ecm_patients_resid_z, os.path.join(features_dir, 'ecm_patients_resid_z.png'),
vmin=-4, vmax=4, figsize=(17.5, 10))
else:
print 'ECM features already denoised'
def create_voxel_plots(voxel_name, ppmst):
print '...Working on %s %s ' %(analysis_type, ppmst)
#create output QC directory
mkdir_path(os.path.join(subject_dir, 'quality_control', analysis_type, voxel_name, 'ppm_%s' %ppmst, 'tmp'))
qc_dir = os.path.join(subject_dir, 'quality_control', analysis_type, voxel_name, 'ppm_%s' %ppmst)
tmp_dir = os.path.join(qc_dir, 'tmp')
#grab lcmodel plots
svs_lcmodel = os.path.join(subject_dir, 'lcmodel_%s'%analysis_type, voxel_name, 'ppm_%s'%ppmst, 'ps.pdf')
# create localization pngs
make_png = ['convert', '-density', '300', '-trim', '%s'%svs_lcmodel, '-quality', '300', '-sharpen', '0x1.0', '%s/%s_lcmodel.png'%(tmp_dir, voxel_name)]
subprocess.call(make_png)
lcm_plot = os.path.join(tmp_dir, '%s_lcmodel-0.png'%voxel_name)
#grab snr/fwhm data
svs_snr = np.genfromtxt(os.path.join(subject_dir, 'lcmodel_%s'%analysis_type, voxel_name, 'ppm_%s'%ppmst, 'snr.txt'), delimiter = ',')
#grab voxel mask
svs = os.path.join(subject_dir, 'svs_voxel_mask','%s%s_%s_RDA_MASK.nii'%(subject,workspace_dir[-10:-9], voxel_name))
#get data into matrix
anat_load = nb.load(anatomical)
svs_load = nb.load(svs)
anat_data = anat_load.get_data()
svs_data = svs_load.get_data()
# get svs cut coords
coords = find_cut_coords(svs_load)
# convert zeros to nans for visualization purposes
svs_data[svs_data==0]=np.nan
# plot voxel on anat
fig =plt.figure()
fig.set_size_inches(6.5, 6.5)
fig.subplots_adjust(wspace=0.005)
#1
ax1 = plt.subplot2grid((1,3), (0,0), colspan = 1, rowspan =1)
ax1.imshow(anat_data[coords[0],:,:], matplotlib.cm.bone_r)
ax1.imshow(svs_data[coords[0],:,:] , matplotlib.cm.rainbow_r, alpha = 0.7)
ax1.set_xlim(23, 157)
ax1.set_ylim(101, 230)
ax1.axes.get_yaxis().set_visible(False)
ax1.axes.get_xaxis().set_visible(False)
#2
ax2 = plt.subplot2grid((1,3), (0,1), colspan = 1, rowspan =1)
ax2.imshow(np.rot90(anat_data[:,:,coords[2]]), matplotlib.cm.bone_r )
ax2.imshow(np.rot90(svs_data[:,:,coords[2]]) , matplotlib.cm.rainbow_r, alpha = 0.7 )
ax2.set_xlim(230, 20)
ax2.set_ylim(207, 4)
ax2.axes.get_yaxis().set_visible(False)
ax2.axes.get_xaxis().set_visible(False)
#3
ax3 = plt.subplot2grid((1,3), (0,2), colspan = 1, rowspan =1)
ax3.imshow(anat_data[:,coords[1],:], matplotlib.cm.bone_r, origin='lower')
ax3.imshow(svs_data[:,coords[1],:] , matplotlib.cm.rainbow_r, alpha = 0.7, origin='lower')
ax3.set_xlim(38, 140)
ax3.set_ylim(160, 60)
ax3.axes.get_yaxis().set_visible(False)
ax3.axes.get_xaxis().set_visible(False)
fig.tight_layout()
fig.savefig('%s/localization_%s.png'%(qc_dir, voxel_name), dpi=200, bbox_inches='tight')
# create qc report
report = canvas.Canvas(os.path.join(qc_dir,'QC_REPORT_%s.pdf'%voxel_name), pagesize=(1280, 1556))
report.setFont("Helvetica", 40)
report.drawImage(os.path.join(qc_dir,'localization_%s.png'%voxel_name), 1, inch*13.5)
report.drawImage(lcm_plot, 30, inch*1, width = 1200, height = 800)
report.drawString(230, inch*20, ' %s%s, %s_%s , SNR=%s FWHM=%s ' %(subject,workspace_dir[-10:-9], voxel_name, analysis_type, svs_snr[2],svs_snr[1]) )
report.showPage()
if analysis_type is 'twix':
fig_f6 = os.path.join(subject_dir, 'svs_twix', voxel_name, 'f6_frequency_drift_correction.png')
fig_f7 = os.path.join(subject_dir, 'svs_twix', voxel_name, 'f7_estimated_phase_phase_drift.png')
fig_f8 = os.path.join(subject_dir, 'svs_twix', voxel_name, 'f8_estimated_frequency_drift.png')
reader = open(os.path.join(subject_dir, 'svs_twix', voxel_name, voxel_name, 'readme.txt'), 'r')
for line in reader:
if 'bad' in line:
badavn = line[34:38]
report.drawImage(fig_f6, 1, inch*7.2)
report.drawImage(fig_f7, 90, inch*1, width = 540, height = 450)
report.drawImage(fig_f8, 590, inch*1, width = 540, height = 450)
report.setFont("Helvetica", 40)
report.drawString(350, inch*20, 'Number of Bad Averages =%s' %(badavn))
report.save()
else:
report.save()
def calculate_voxel_statistics(population, workspace_dir):
print '#############################################################################'
print ''
print ' RUNNNING PROJECT NMR-093%s %s' %(workspace_dir[-10:-9], workspace_dir[-8:])
print ''
print '#############################################################################'
count = 0
for subject in population:
count +=1
print '========================================================================================'
print '%s- Calculating Voxel statistics for subject %s_%s' %(count,subject, workspace_dir[-10:-9])
print '.'
# input files
spm_dir = os.path.join(workspace_dir, subject, 'segmentation_spm')
acc_mask = os.path.join(workspace_dir, subject, 'svs_voxel_mask', '%s%s_ACC_RDA_MASK.nii'%(subject,workspace_dir[-10:-9]))
tha_mask = os.path.join(workspace_dir, subject, 'svs_voxel_mask', '%s%s_THA_RDA_MASK.nii'%(subject,workspace_dir[-10:-9]))
str_mask = os.path.join(workspace_dir, subject, 'svs_voxel_mask', '%s%s_STR_RDA_MASK.nii'%(subject,workspace_dir[-10:-9]))
spm_gm = os.path.join(spm_dir, 'TISSUE_CLASS_1_GM_OPTIMIZED.nii.gz')
spm_wm = os.path.join(spm_dir, 'TISSUE_CLASS_2_WM_OPTIMIZED.nii.gz')
spm_cm = os.path.join(spm_dir, 'TISSUE_CLASS_3_CSF_OPTIMIZED.nii.gz')
# output folders
mkdir_path(os.path.join(workspace_dir, subject, 'svs_voxel_stats'))
stats_dir = os.path.join(workspace_dir, subject, 'svs_voxel_stats')
print 'Calculating tissue proportions'
def calc_props(mask_file, seg_gm, seg_wm, seg_cm, voxel_name):
if not os.path.isfile(mask_file):
print 'IOError: [Errno 2] SVS %s mask Does not exist, create masks and come back' %voxel_name
else:
if os.path.isfile(os.path.join(stats_dir, '%s_voxel_statistics_spm_opt.txt'%voxel_name)):
print 'Voxel statistics already calculated ... moving on'
else:
#grab data
spm_gm_data = nb.load(seg_gm).get_data().squeeze()
spm_wm_data = nb.load(seg_wm).get_data().squeeze()
spm_cm_data = nb.load(seg_cm).get_data().squeeze()
vox_data = nb.load(mask_file).get_data().squeeze()
#multiply SVS ROI with segmented data for ACC
vox_spm_gm = vox_data * spm_gm_data
vox_spm_wm = vox_data * spm_wm_data
vox_spm_cm = vox_data * spm_cm_data
#extract stats from segmentation for acc
vox_total_svs = np.sum(vox_data)
vox_total_spm_gm = np.sum(vox_spm_gm)
vox_total_spm_wm = np.sum(vox_spm_wm)
vox_total_spm_cm = np.sum(vox_spm_cm)
percent_svs = float(vox_total_svs)/ float(vox_total_svs)
percent_spm_gm = np.round(float(vox_total_spm_gm) / float(vox_total_svs), 3)
percent_spm_wm = np.round(float(vox_total_spm_wm) / float(vox_total_svs), 3)
percent_spm_cm = np.round(float(vox_total_spm_cm) / float(vox_total_svs), 3)
sum_spm = np.round(float(percent_spm_gm + percent_spm_wm + percent_spm_cm), 3)
print '%s.....' %voxel_name
print '...%s SPM NewSegment Tissue Proportions = %s%% GM, %s%% WM, %s%% CSF = %s' %(voxel_name, percent_spm_gm, percent_spm_wm, percent_spm_cm, sum_spm)
spm_txt = os.path.join(stats_dir, '%s_voxel_statistics_spm_opt.txt'%voxel_name)
write_spm = open(spm_txt, 'w')
write_spm.write('%s, %s, %s, %s'%(percent_spm_gm, percent_spm_wm, percent_spm_cm, sum_spm))
write_spm.close()
calc_props(acc_mask, spm_gm, spm_wm, spm_cm, 'ACC')
calc_props(tha_mask, spm_gm, spm_wm, spm_cm, 'THA')
calc_props(str_mask, spm_gm, spm_wm, spm_cm, 'STR')
print '========================================================================================'
