def runHelp(self):
#make local variable
conf = self.conf
logname = conf.logname
os.chdir(conf.subjfMRIDir)
print '-Spatially smooth data-'
run_shell_cmd('3dBlurInMask -input ' + conf.nextInputFilename[-2] + '+tlrc -FWHM ' + str(conf.FWHMSmoothing) + ' -mask ' + conf.subjMaskDir + conf.subjID + '_gmMask_func_dil1vox.nii.gz -prefix ' + conf.nextInputFilename[-1] + '.nii.gz', logname)
def set_structure(fname, structure, verbose=True):
cmd = 'wb_command -set-structure %s %s' % (fname, structure)
if verbose:
print cmd
output = run_shell_cmd(cmd)
if verbose:
print output
def set_structure(fname,structure,verbose=True):
cmd='wb_command -set-structure %s %s'%(fname,structure)
if verbose:
print cmd
output=run_shell_cmd(cmd)
if verbose:
print output
def run(self):
#make local variable
conf = self.conf
logname = conf.logname
os.chdir(conf.subjfMRIDir)
numRuns = len(conf.epi_series)
for runNum in range(1, numRuns + 1):
print ' - Slice Time Correction for Run', runNum, '-'
run_shell_cmd('3dTshift -overwrite -Fourier -TR ' + conf.TR + ' -tpattern ' + conf.tpattern + ' -prefix ' + conf.nextInputFilename[-1] + '_r' + str(runNum) + ' ' + conf.nextInputFilename[-2] + '_r' + str(runNum) + '+orig', logname)
rmFileName = glob.glob(conf.nextInputFilename[-1] + '_r' + str(runNum) + '????.????.gz')
if rmFileName:
run_shell_cmd('rm -v ' + rmFilename,logname)
def runHelp(self):
#make local variable
conf = self.conf
logname = conf.logname
os.chdir(conf.subjfMRIDir)
# Plotting motion parameters
if self.showPlot == True:
run_shell_cmd('1dplot -sep_scl -plabel ' + str(conf.subjID) + "Motion -volreg allruns_motion_params.1D'[0..5]'",logname)
run_shell_cmd('echo "Mean, standard deviation, and absolute deviation of subject\'s motion in mm (left to right), by x,y,z direction (top to bottom):" > MotionInfo.txt',logname)
run_shell_cmd("3dTstat -mean -stdev -absmax -prefix stdout: allruns_motion_params.1D'[0..2]'\\'" + " >> MotionInfo.txt",logname)
run_shell_cmd('cat MotionInfo.txt',logname)
def run(self):
#make local variable
conf = self.conf
logname = conf.logname
os.chdir(conf.subjfMRIDir)
# Get concat variables from conf object
runList = conf.runList
concatString = conf.concatString
numRuns = len(conf.epi_series)
print '- Concatenating Runs -'
print 'Run list:', runList
print 'Concatenation string (onset times of each run):', concatString
# Run command
run_shell_cmd('rm -v ' + conf.nextInputFilename[-1] + '+orig*',logname)
run_shell_cmd('3dTcat -prefix ' + conf.nextInputFilename[-1] + ' ' + runList,logname)
# Remove intermediate analysis file to save disk space
rm_file = glob.glob(conf.nextInputFilename[-1] + 'r_*+????.????.gz')
if rm_file:
run_shell_cmd('rm -v ' + rm_file,logname)
def runHelp(self):
# make local variable
conf = self.conf
logname = conf.logname
print '-Percent Signal Change Normalization-'
os.chdir(conf.subjfMRIDir)
print '-Deoblique whole brain mask to align with original EPI image-'
run_shell_cmd('3dWarp -card2oblique ' + conf.nextInputFilename[-2] + '+orig -prefix tmp_deoblique_wbm.nii.gz ' + conf.subjMaskDir + conf.subjID + '_wholebrainmask.nii.gz', logname)
run_shell_cmd('3dresample -overwrite -master ' + conf.subjfMRIDir + conf.nextInputFilename[-2] + '+orig -inset tmp_deoblique_wbm.nii.gz -prefix tmp_deoblique_wbm_func.nii.gz',logname)
print '-Run 3dTstat to get the mean activation for each voxel across time-'
run_shell_cmd('3dTstat -mean -prefix tmp_tmean.nii.gz ' + conf.nextInputFilename[-2] + '+orig', logname)
print '-Now run percent signal change, masking to deobliqued, whole brain mask-'
run_shell_cmd('3dcalc -a ' + conf.nextInputFilename[-2] + "+orig -b tmp_tmean.nii.gz -c tmp_deoblique_wbm_func.nii.gz -expr '100 * a/b * ispositive(c)' -prefix " + conf.nextInputFilename[-1], logname)
print '-Deleting intermediate files...-'
def run(self):
# Function to run the block
for line in self.commands:
if not line.startswith('#'):
run_shell_cmd(line, conf.logname)
def run_and_log(cmd, logfile='conversion_log.txt'):
run_shell_cmd(cmd)
logmsg(cmd, logfile)
def run(self):
#make local variable
conf = self.conf
logname = conf.logname
os.chdir(conf.subjfMRIDir)
numRuns = len(conf.epi_series)
# Getting raw fMRI data folder names
# Modify: How to identify fMRI series directory in raw data [regular expression used to get series order correct]
### Edit this, not very clean ###
rawDirRunList = []
for run in range(numRuns):
# find epi series
searchPath = glob.glob(conf.subjRawDataDir + conf.epi_series[run])[0]
# append to rawDirRunList
rawDirRunList.append(searchPath)
print 'Raw data folder order:', rawDirRunList
#For-loop for functions used across multiple runs (prior to run-concatenation)
for runNum in range(1,numRuns + 1):
print '--Run', runNum, '---'
runRawDir = rawDirRunList[runNum-1]
fileFindString = 'MR*' if glob.glob(runRawDir + '/*.dcm') == [] else '*.dcm'
runRawFile = runRawDir + '/' + fileFindString
# Sorting DICOM files (to make sure they will be read in the order they were collected in) using Freesurfer.
run_shell_cmd('dicom-rename ' + runRawFile + ' --o ' + conf.basedir + '/SortedDICOMs/Run' + str(runNum) + '/MR',logname)
# Converting from DICOM to NIFTI to AFNI format using Freesurfer
run_shell_cmd('mri_convert ' + conf.basedir + '/SortedDICOMs/Run' + str(runNum) + '/*00001.dcm --in_type siemens --out_type nii epi_r' + str(runNum) + '.nii.gz',logname)
run_shell_cmd('rm epi_r' + str(runNum) + '+orig*',logname)
run_shell_cmd('3dcopy epi_r' + str(runNum) +'.nii.gz epi_r' + str(runNum),logname)
run_shell_cmd('rm epi_r' + str(runNum) + '.nii.gz',logname)
# Remove sorted DICOMs
run_shell_cmd('rm -rf ' + conf.basedir + '/SortedDICOMs/Run' + str(runNum),logname)
# If numTRsToSkip > 0, remove the first couple TRs for every epi run
if conf.numTRsToSkip > '0':
run_shell_cmd('3dcalc -a epi_r' + str(runNum) + '+orig"[' + conf.numTRsToSkip + '..$]" -expr ' + "'a' -prefix epi_r" + str(runNum) + ' -overwrite', logname)
def run(self):
run_shell_cmd(self.cmd, self.conf.logname)
"""
convert orginal ontology to YAML
make two files:
- one that just contains the hierarchy
- another that just contains the mappings from ontology terms to search queries
"""
import pickle
from run_shell_cmd import run_shell_cmd
from matplotlib.cbook import flatten
# fix the ontology for mac
run_shell_cmd("cat PubBrainOntology.txt | tr '\r' '\n' > PubbrainOntology_fixed.txt")
# first need to run mk_combined_atlas.py to generate atlas terms
# *** getting rid of this by using consistent terms for atlas and ontology
term_voxkeys=pickle.load(open('term_voxkeys.cpkl','rb'))
lower_term_voxkeys=[i.lower() for i in term_voxkeys.keys()]
f = open('PubbrainOntology_fixed.txt')
lines=f.readlines()
f.close()
# find indention level of each line
level=[]
tree={}
ctr=0
print modclust_infomap.modularity
assgn = numpy.array([i + 1 for i in modclust_infomap.membership
]) # zero treated as unclustered
# exclude small modules by setting to zero
for c in numpy.unique(assgn):
if numpy.sum(assgn == c) < size_thresh:
assgn[assgn == c] = 0
print 'density %0.3f (%0.3f): %d clusters' % (densities[dens], pct,
len(numpy.unique(assgn)))
assignments[:, dens] = assgn
numpy.savetxt('assignments.txt', assignments)
#cons=pair_consensus(assignments.T.astype('int'))
run_shell_cmd(
'/Applications/MATLAB_R2015a.app/bin/matlab -nosplash -nodesktop < Consensus_infomap_simple.m'
)
# modules start at zero
# relabel all single-node modules as zero and ignore them
labels = numpy.loadtxt('assignments_minsize5_regularized_consensus.txt')
SHUFFLE = False
if SHUFFLE:
numpy.random.shuffle(labels)
# load parcel info
lines = open(
'/Users/poldrack/Dropbox/data/selftracking/rsfmri/module_assignments.txt'
).readlines()
modules = numpy.array([float(l.strip().split()[3]) for l in lines])
def run_and_log(cmd,logfile='conversion_log.txt'):
run_shell_cmd(cmd)
logmsg(cmd,logfile)
for c in copefiles:
copenum = int(c.split('/')[-1].split('.')[0].replace(filetype, ''))
for hemis in ['L', 'R']:
outfile = os.path.join(
newstatsdir, '%s%03d.%s.func.gii' % (filetype, copenum, hemis))
goodvoxmask = os.path.join(
'/corral-repl/utexas/poldracklab/data/selftracking/volume_goodvoxels',
'%s_%s_goodvoxels_333.nii.gz' %
(subcode, tasknames[runcode][taskcode]))
assert os.path.exists(goodvoxmask)
wb_command = 'wb_command -volume-to-surface-mapping %s %s %s -ribbon-constrained %s %s -volume-roi %s' % (
c, surfaces['midthickness'][hemis], outfile,
surfaces['white'][hemis], surfaces['pial'][hemis], goodvoxmask)
if not os.path.exists(outfile):
print wb_command
run_shell_cmd(wb_command)
if hemis == 'L':
structure = 'CORTEX_LEFT'
else:
structure = 'CORTEX_RIGHT'
wb_command = 'wb_command -set-structure %s %s' % (outfile,
structure)
print wb_command
run_shell_cmd(wb_command)
wb_command = 'wb_command -metric-smoothing %s %s 1.5 %s' % (
surfaces['midthickness'][hemis], outfile,
outfile.replace('func.', 'smoothed.func.'))
print wb_command
run_shell_cmd(wb_command)
def runHelp(self):
#make local variable
conf = self.conf
logname = conf.logname
os.chdir(conf.subjfMRIDir)
#### Talairach transform anatomical image
print '-Run @auto_tlrc to talairach transform anatomical T1 image-'
run_shell_cmd('@auto_tlrc -base ' + conf.atlasAnat + ' -input anat_mprage_skullstripped+orig -no_ss',logname)
run_shell_cmd('3dcopy anat_mprage_skullstripped+tlrc anat_mprage_skullstripped_tlrc.nii.gz', logname) #format into NIFTI format
# Create Mask
run_shell_cmd("3dcalc -overwrite -a anat_mprage_skullstripped+tlrc -expr 'ispositive(a)' -prefix " + conf.subjMaskDir + "/wholebrain_mask.nii.gz" ,logname)
# Link anatomical image to mask directory for checking alignment
run_shell_cmd('ln -s anat_mprage_skullstripped_tlrc.nii.gz ' + conf.subjMaskDir,logname)
print '-Run align_epi_anat.py to align EPIs to MPRAGE, motion correct, and Talairach transform EPIs (output in 222 space)-'
print 'Make sure Python is version 2.6 or greater (ColeLabMac should be version 2.7)'
run_shell_cmd('python -V',logname)
# Correcting for motion, aligning fMRI data to MPRAGE, and aligning fMRI data to Talairach template [applying all transformation at once reduces reslicing artifacts]
# [You could alternatively analyze all of the data, then Talairach transform the statistics (though this would make extraction of time series based on Talairached ROIs difficult)]
# Visit for more info: http://afni.nimh.nih.gov/pub/dist/doc/program_help/align_epi_anat.py.html
run_shell_cmd('align_epi_anat.py -overwrite -anat anat_mprage_skullstripped+orig -epi ' + conf.nextInputFilename[-2] + '+orig -epi_base 10 -epi2anat -anat_has_skull no -AddEdge -epi_strip 3dSkullStrip -ex_mode quiet -volreg on -deoblique on -tshift off -tlrc_apar anat_mprage_skullstripped+tlrc -master_tlrc ' + conf.atlasEPI,logname)
# Convert to NIFTI
run_shell_cmd('3dcopy ' + conf.nextInputFilename[-1] + '+tlrc ' + conf.nextInputFilename[-1] + '.nii.gz',logname)
run_shell_cmd('cp ' + conf.nextInputFilename[-2] + "_vr_motion.1D allruns_motion_params.1D",logname)
def run(self):
#make local variable
conf = self.conf
logname = conf.logname
os.chdir(conf.subjfMRIDir) #change working directory to fMRI directory
print 'Preparing MPRAGE file (anatomical image)'
##****## In the future, will want to adjust this to use os.walk to search subdirectories for files of *.dcm
dirName = glob.glob(conf.subjRawDataDir + conf.T1_image)[0]
# At this point, will only search for DICOMs with *.dcm extensions, or starting with MR*
fileFindString = 'MR*' if glob.glob(dirName + '/*.dcm') == [] else '*.dcm'
dicomRenameDir = dirName + '/' + fileFindString
#Sort DICOM files (to make sure they will be read in the order they were collected in) using Freesurfer
print 'Sorting DICOM...'
run_shell_cmd('dicom-rename ' + dicomRenameDir + ' --o ' + conf.basedir + 'SortedDICOMs/MPRAGE/MR',logname)
#Convert DICOM files to NIFTI format using Freesurfer
print 'Converting DICOM...'
run_shell_cmd('mri_convert ' + conf.basedir + 'SortedDICOMs/MPRAGE/*-00001.dcm --in_type siemens --out_type nii mprage.nii.gz',logname)
#Remove sorted DICOMs
run_shell_cmd('rm -rf ' + conf.basedir + 'SortedDICOMs/MPRAGE',logname)
####
# Skull strip MPRAGE
# Use Freesurfer's skullstripping (very slow, but more accurate)
if conf.runFreesurfer == True:
run_shell_cmd('recon-all -subject ' + conf.subjID + ' -all -sd ' + conf.freesurferDir + ' -i mprage.nii.gz',logname)
# Convert to NIFTI
run_shell_cmd('mri_convert --in_type mgz --out_type nii ' + conf.freesurferDir + '/mri/brain.mgz mprage_skullstripped.nii',logname)
# gzip files and removed uncompressed file
run_shell_cmd('3dcopy mprage_skullstripped.nii mprage_skullstripped.nii.gz',logname)
run_shell_cmd('rm mprage_skullstripped.nii',logname)
# Compressing file
run_shell_cmd('3dcopy mprage_skullstripped.nii.gz anat_mprage_skullstripped',logname)
run_shell_cmd('cp ' + conf.atlasAnat + '* .',logname)
def get_data_from_parcels(volfile, outfile, save_gii=True):
surfaces = {}
surfaces['midthickness'] = {
'L':
os.path.join(
basedir,
'fsaverage_LR32k/sub013.L.midthickness.32k_fs_LR.surf.gii'),
'R':
os.path.join(
basedir,
'fsaverage_LR32k/sub013.R.midthickness.32k_fs_LR.surf.gii')
}
surfaces['white'] = {
'L':
os.path.join(basedir,
'fsaverage_LR32k/sub013.L.white.32k_fs_LR.surf.gii'),
'R':
os.path.join(basedir,
'fsaverage_LR32k/sub013.R.white.32k_fs_LR.surf.gii')
}
surfaces['pial'] = {
'L':
os.path.join(basedir,
'fsaverage_LR32k/sub013.L.pial.32k_fs_LR.surf.gii'),
'R':
os.path.join(basedir,
'fsaverage_LR32k/sub013.R.pial.32k_fs_LR.surf.gii')
}
parcellations = {
'L':
os.path.join(
basedir,
'parcellation/all_selected_L_new_parcel_renumbered.func.gii'),
'R':
os.path.join(
basedir,
'parcellation/all_selected_R_new_parcel_renumbered.func.gii')
}
parcels = {}
parceldata = {}
for hemis in ['L', 'R']:
tmpfile = tempfile.mkstemp(suffix='.%s.func.gii' % hemis)
os.close(tmpfile[0])
outfile = tmpfile[1]
print outfile
wb_command = 'wb_command -volume-to-surface-mapping %s %s %s -ribbon-constrained %s %s' % (
volfile, surfaces['midthickness'][hemis], outfile,
surfaces['white'][hemis], surfaces['pial'][hemis])
print wb_command
run_shell_cmd(wb_command)
if hemis == 'L':
structure = 'CORTEX_LEFT'
else:
structure = 'CORTEX_RIGHT'
wb_command = 'wb_command -set-structure %s %s' % (outfile, structure)
print wb_command
run_shell_cmd(wb_command)
surfdata = nibabel.gifti.giftiio.read(outfile)
parcels[hemis] = nibabel.gifti.giftiio.read(parcellations[hemis])
for i in numpy.unique(parcels[hemis].darrays[0].data):
if i == 0:
continue
parcelvertices = parcels[hemis].darrays[0].data == i
parceldata[i] = []
for da in range(len(surfdata.darrays)):
parceldata[i].append(
numpy.mean(surfdata.darrays[da].data[parcelvertices[:,
0]]))
if save_gii:
shutil.move(outfile,
volfile.replace('nii.gz', '%s.func.gii' % hemis))
else:
os.remove(outfile)
nparcs = len(parceldata)
ntp = len(parceldata[parceldata.keys()[0]])
parcs = numpy.unique(parceldata.keys())
parcs.sort()
parcmtx = numpy.zeros((nparcs, ntp))
for p in range(len(parcs)):
parcmtx[p] = parceldata[parcs[p]]
return parcmtx
def run(self):
print 'Running:', self.input
run_shell_cmd(self.input, self.logname, cwd=self.pwd)
if numpy.sum(W) == 0:
continue
Y = cope[:, v].reshape((nsess, 1))
wls = sm.WLS(Y, X, weights=1.0 / varcope[:, v])
res = wls.fit()
#beta_hat = numpy.linalg.inv(X.T.dot(W).dot(X)).dot(X.T).dot(W).dot(Y) #(X'WX)^{-1} X'WY
#var_beta_hat = numpy.linalg.inv(X.T.dot(W).dot(X)) #(X'WX)^{-1}
#tstat[v]=beta_hat / numpy.sqrt(var_beta_hat)
tstat[v] = res.tvalues[0]
outdir = '/corral-repl/utexas/poldracklab/data/selftracking/surface_stats_333'
fname = os.path.join(
outdir, 'task%03d_cope%03d.%s.tstat.smoothed.func.gii' %
(task, copenum, hemis))
if hemis == 'L':
structure = 'CORTEX_LEFT'
else:
structure = 'CORTEX_RIGHT'
img = nibabel.gifti.GiftiImage()
img.add_gifti_data_array(
nibabel.gifti.GiftiDataArray.from_array(
tstat,
intent=f.darrays[0].intent,
datatype=f.darrays[0].datatype,
ordering='F',
meta={'Name': 'task%03d_cope%03d.tstat' % (task, copenum)}))
nibabel.gifti.giftiio.write(img, fname)
cmd = 'wb_command -set-structure %s %s' % (fname, structure)
run_shell_cmd.run_shell_cmd(cmd)
def runHelp(self):
#make local variable
conf = self.conf
logname = conf.logname
os.chdir(conf.subjfMRIDir)
print '--Extract time series from white matter, ventricle masks--'
run_shell_cmd('3dmaskave -quiet -mask ' + conf.subjMaskDir + conf.subjID + '_wmMask_func_eroded.nii.gz ' + conf.nextInputFilename[-1] + '.nii.gz > ' + conf.wm_timeseries + '.1D',logname)
run_shell_cmd('3dmaskave -quiet -mask ' + conf.subjMaskDir + conf.subjID + '_ventricles_func_eroded.nii.gz ' + conf.nextInputFilename[-1] + '.nii.gz > ' + conf.ventricles_timeseries + '.1D',logname)
print '--Extract whole brain signal--'
os.chdir(conf.subjMaskDir)
if conf.hcpData == False: # no need to run @auto_tlrc on hcpdata
# Transform aseg to TLRC space
run_shell_cmd('@auto_tlrc -apar ' + conf.subjfMRIDir + 'anat_mprage_skullstripped+tlrc -input ' + conf.subjID + '_fs_seg.nii.gz',logname)
run_shell_cmd("3dcalc -overwrite -a " + conf.subjID + "_fs_seg.nii.gz -expr 'ispositive(a)' -prefix " + conf.subjID + '_wholebrainmask.nii.gz',logname)
# Resample to functional space
run_shell_cmd('3dresample -overwrite -master ' + conf.subjfMRIDir + conf.nextInputFilename[-1] + '.nii.gz -inset ' + conf.subjID + '_wholebrainmask.nii.gz -prefix ' + conf.subjID + '_wholebrainmask_func.nii.gz',logname)
# Dilate mask by 1 functional voxel (just in case the resampled anatomical mask is off by a bit)
run_shell_cmd("3dLocalstat -overwrite -nbhd 'SPHERE(-1)' -stat 'max' -prefix " + conf.subjID + '_wholebrainmask_func_dil1vox.nii.gz ' + conf.subjID + '_wholebrainmask_func.nii.gz',logname)
os.chdir(conf.subjfMRIDir)
run_shell_cmd('3dmaskave -quiet -mask ' + conf.subjMaskDir + conf.subjID + '_wholebrainmask_func_dil1vox.nii.gz ' + conf.nextInputFilename[-1] + '.nii.gz > ' + conf.wholebrain_timeseries + '.1D',logname)
import os
import glob
import shutil
import run_shell_cmd
dirs=glob.glob('ds*/sub*/model/model001')
# make target dirs
for d in dirs:
print d
newdir=d+'_fsl4'
if not os.path.exists(newdir):
os.mkdir(newdir)
to_copy=glob.glob(os.path.join(d,'*.gfeat'))
#if 0:
for c in to_copy:
cmd='cp -r %s %s'%(c,newdir)
print cmd
run_shell_cmd.run_shell_cmd(cmd)
if 0:
for c in to_copy:
if os.path.exists(c.replace('model001','model001_fsl4')):
cmd='rm -rf %s'%c
print cmd
run_shell_cmd.run_shell_cmd(cmd)
def runHelp(self):
#make local variable
conf = self.conf
GLM = self.conf.GLM
logname = conf.logname
os.chdir(conf.subjfMRIDir)
run_shell_cmd('cp Movement_Regressors_Rest_allruns.1D allruns_motion_params.1D', logname)
run_shell_cmd('1d_tool.py -overwrite -infile ' + conf.wm_timeseries + '.1D -derivative -write ' + conf.wm_timeseries + '_deriv.1D', logname)
run_shell_cmd('1d_tool.py -overwrite -infile ' + conf.ventricles_timeseries + '.1D -derivative -write ' + conf.ventricles_timeseries + '_deriv.1D', logname)
run_shell_cmd('1d_tool.py -overwrite -infile ' + conf.wholebrain_timeseries + '.1D -derivative -write ' + conf.wholebrain_timeseries + '_deriv.1D', logname)
print 'Run GLM to remove nuisance time series (motion, white matter, ventricles)'
input = '-input ' + conf.nextInputFilename[-2] + '.nii.gz ' if GLM['input'] == None else GLM['input']
mask = '-mask ' + conf.subjMaskDir + conf.subjID + '_gmMask_func_dil1vox.nii.gz '
concat = '' if GLM['concat'] == False else '-concat ' + '"' + conf.concatString + '" '
polort = '-polort ' + str(GLM['polort']) + ' '
stimtimes = self.organizeStimTimes()
num_stimts = '-num_stimts ' + str(len(stimtimes)) + ' '
# make stimtimes into a single string
stimtimes = ''.join(stimtimes)
# stimfile1 = '-stim_file 1 ' + conf.wm_timeseries + '.1D -stim_label 1 WM '
# stimfile2 = '-stim_file 2 ' + conf.ventricles_timeseries + '.1D -stim_label 2 Vent '
# stimfile3 = '-stim_file 3 ' + conf.wm_timeseries + '_deriv.1D -stim_label 3 WMDeriv '
# stimfile4 = '-stim_file 4 ' + conf.ventricles_timeseries + '_deriv.1D -stim_label 4 VentDeriv '
# stimfile5 = "-stim_file 5 allruns_motion_params.1D'[0]' -stim_base 5 "
# stimfile6 = "-stim_file 6 allruns_motion_params.1D'[1]' -stim_base 6 "
# stimfile7 = "-stim_file 7 allruns_motion_params.1D'[2]' -stim_base 7 "
# stimfile8 = "-stim_file 8 allruns_motion_params.1D'[3]' -stim_base 8 "
# stimfile9 = "-stim_file 9 allruns_motion_params.1D'[4]' -stim_base 9 "
# stimfile10 = "-stim_file 10 allruns_motion_params.1D'[5]' -stim_base 10 "
# these stimfiles are commented out because this had to be adapted from analyzing the HCP rest data
# stimfile11 = "-stim_file 11 rest_allruns_motion_params.1D'[6]' -stim_base 11 "
# stimfile12 = "-stim_file 12 rest_allruns_motion_params.1D'[7]' -stim_base 12 "
# stimfile13 = "-stim_file 13 rest_allruns_motion_params.1D'[8]' -stim_base 13 "
# stimfile14 = "-stim_file 14 rest_allruns_motion_params.1D'[9]' -stim_base 14 "
# stimfile15 = "-stim_file 15 rest_allruns_motion_params.1D'[10]' -stim_base 15 "
# stimfile16 = "-stim_file 16 rest_allruns_motion_params.1D'[11]' -stim_base 16 "
# stimfile11 = "-stim_file 11 " + conf.wholebrain_timeseries + '.1D -stim_label 11 WholeBrain '
# stimfile12 = "-stim_file 12 " + conf.wholebrain_timeseries + '_deriv.1D -stim_label 12 WholeBrainDeriv '
xsave = '-xsave -x1D xmat_rall.x1D -xjpeg xmat_rall.jpg '
gltsym = '' if GLM['gltsym'] == None else GLM['gltsym'] + ' '
errts = '' if GLM['errts'] == None else '-errts ' + GLM['errts'] + ' '
fdr = '' if GLM['noFDR'] == False else '-noFDR '
fout = '-fout ' if GLM['fout'] == True else ''
tout = '-tout ' if GLM['tout'] == True else ''
if 'jobs' in GLM.keys():
jobs = '-jobs ' + str(GLM['jobs']) + ' -float '
else:
jobs = '-jobs 1 -float '
# change outbucket output name according to type of GLM run
if GLM['type'] == 'Activation':
bucket = '-bucket ' + conf.AnalysisName + '_outbucket -cbucket ' + conf.AnalysisName + '_cbucket'
else:
bucket = '-bucket NuissanceResid_outbucket_' + conf.nextInputFilename[-2] + '+tlrc -overwrite'
glm_command = '3dDeconvolve ' + input + mask + concat + polort + num_stimts + stimtimes + gltsym + fout + tout + xsave + errts + jobs + fdr + bucket
print 'Running the GLM command:', glm_command
run_shell_cmd(glm_command, logname)
if GLM['type'] == 'rsfcMRI':
run_shell_cmd('rm ' + conf.nextInputFilename[-1] + '*', logname)
run_shell_cmd('3dcopy NuissanceResid_Resids+tlrc ' + conf.nextInputFilename[-1] + '+tlrc', logname)
run_shell_cmd('rm NuissanceResid_Resids+tlrc', logname)
elif GLM['type'] == 'Activation':
run_shell_cmd('1dplot -sep_scl -plabel ' + conf.subjID + 'DesignMatrix xmat_rall.x1D &', logname)
