Nipype: Level1Design ev_files outputs wrong

level1design
ev_files
nipype

#1

Hi everyone,

I’ve been stuck on my Level1Design node creating incorrect ev_files. Specifically, instead of generating 11 EV files, it generates 7: 1 for the first ev and then rest are txt files combined with the Outliers and realignment_parameters txt files that are input into SpecifyModel. When I run a single participant, node by node, on jupyter notebook, the correct ev_files are created. However, it just doesn’t seem to work on my terminal. I’m probably missing something super obvious and could use a fresh pair of eyes.

Thanks in advance!

CODE:

Level 1 TMS BLOCK Designs

###########################################

Purpose: Create TMS on and TMS off Model

for Individual then Normalize data

for group effects

METHOD:

1. Get dependencies

2. Set Experiment Parameters

1. Import Dependencies

#%matplotlib inline
from os.path import join as opj
import os.path
import json
from nipype.interfaces.fsl import (Level1Design, FEATModel, L2Model,
FEAT, ContrastMgr, FILMGLS, Merge, GLM, FEATRegister, Randomise, FLAMEO)
#from nipype.algorithms.modelgen import SpecifySPMModel
from nipype.interfaces.utility import Function, IdentityInterface
from nipype.interfaces.io import SelectFiles, DataSink
from nipype.pipeline.engine import Workflow, Node, MapNode
from nipype.algorithms.modelgen import SpecifyModel
import pandas as pd
from nipype.interfaces.base import Bunch

2.Set Experiment Specific Attributes

preproc_dir = ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2’
output_dir = ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/LEVELONE’ #need to make this
working_dir = ‘workingdir_LEVELONE’

#Could change this to be the actual name of the folders

subject_list= [‘P100’,‘P101’]

‘P101’, ‘P102’, ‘P103’, ‘P104’, ‘P105’,

‘P106’, ‘P107’, ‘P108’, ‘P109’, ‘P110’, ‘P111’,

‘P201’, ‘P202’, ‘P203’, ‘P204’, ‘P205’, ‘P206’,

‘P207’, ‘P208’, ‘P209’, ‘P210’, ‘P211’, ‘P212’,

‘P213’, ‘P214’, ‘P215’, ‘P216’]

ROI_list = [‘L_DLPFC_BLOCK_TMS’, ‘R_DLPFC_BLOCK_TMS’]
TR = 2.5
hpcutoff =100

3. ModelPrep Workflow

modelprep = Workflow(name = ‘modelprep’)

# Specify Model#
	# INPUTS NEEDED: event_files, functional_runs
			# 	high_pass_filter_cutoff, input_units: (u'secs' or u'scans')
			# 	subject_info (from getsubject info), time_repetition (TR = 2.5)
			# Need to add outlier file as part of workflow from selectFiles

modelspec = Node(SpecifyModel(high_pass_filter_cutoff= hpcutoff,
input_units = ‘secs’, time_repetition = TR),
name=“modelspec”) #multiple EV files so MapNode?

	#1)-Get subject specific BEHAVE Files (In this case its the same for everyone)-#
# Generate Design Matrix
	# INPUTS NEEDED: Interscan interval (TR), bases (gamma, hrf)
		#session info (from Specify Model)

level1design = Node(Level1Design(interscan_interval = TR, bases = {‘dgamma’: {‘derivs’: False}},
model_serial_correlations = True),name = “level1design”)
#contrasts can be gotten from infosource

# Generate MAT files
	#INPUT NEEDED: ev_files and fsf_file from LEVEL1Design

modelgen = Node(FEATModel(),name=‘modelgen’,
#iterfield=[‘fsf_file’, ‘ev_files’]
)
# Generate Contrast Files
condition_names = [‘OFF’, ‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’,‘B7’,‘B8’,‘B9’,‘B10’]

Contrast01= [‘OFF’, ‘T’, condition_names, [1,0,0,0,0,0,0,0,0,0,0]]
Contrast02= [‘Block1’,‘T’, condition_names, [0,1,0,0,0,0,0,0,0,0,0]]
Contrast03= [‘Block2’,‘T’, condition_names, [0,0,1,0,0,0,0,0,0,0,0]]
Contrast04= [‘Block3’,‘T’, condition_names, [0,0,0,1,0,0,0,0,0,0,0]]
Contrast05= [‘Block4’,‘T’, condition_names, [0,0,0,0,1,0,0,0,0,0,0]]
Contrast06= [‘Block5’,‘T’, condition_names, [0,0,0,0,0,1,0,0,0,0,0]]
Contrast07= [‘Block6’,‘T’, condition_names, [0,0,0,0,0,0,1,0,0,0,0]]
Contrast08= [‘Block7’,‘T’, condition_names, [0,0,0,0,0,0,0,1,0,0,0]]
Contrast09= [‘Block8’,‘T’, condition_names, [0,0,0,0,0,0,0,0,1,0,0]]
Contrast10= [‘Block9’,‘T’, condition_names, [0,0,0,0,0,0,0,0,0,1,0]]
Contrast11= [‘Block10’,‘T’, condition_names,[0,0,0,0,0,0,0,0,0,0,1]]
Contrast12= [‘ON’, ‘T’, condition_names,[0,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.]]

contrast_list= [Contrast01,Contrast02,Contrast03,Contrast04,Contrast05,Contrast06,
Contrast07,Contrast08,Contrast09,Contrast10,Contrast11]

    # Connect ModelPrep WorkLoad

modelprep.base_dir=preproc_dir
modelprep.connect([(modelspec, level1design, [(‘session_info’, ‘session_info’)]),
(level1design, modelgen, [(‘fsf_files’, ‘fsf_file’),(‘ev_files’, ‘ev_files’)]),
])

4 Model FIT workgflow (where stats gets done!)

#modelfit = Workflow(name = ‘modelfit’)

Generate model estimates based on fsf and mat files from above

# in_file fomes from SelectFiles (its the functional run)
#design file from Level1Design or FEATModel

modelestimate = MapNode(FILMGLS(smooth_autocorr=True, mask_size=5, threshold=1000),
name=‘modelestimate’,
iterfield=[‘design_file’, ‘in_file’, ‘tcon_file’])
# Generate contrast estimates DONT NEED THIS ANYMORE FILM DOES COPES

conestimate = MapNode(ContrastMgr(),name=‘conestimate’,

iterfield=[

‘tcon_file’, ‘param_estimates’, ‘sigmasquareds’,

‘dof_file’

])

# Link modefit workflow for Level 1

modelfit.connect([

(modelestimate, conestimate, [(‘param_estimates’, ‘param_estimates’),

(‘sigmasquareds’,‘sigmasquareds’),(‘dof_file’, ‘dof_file’)]),

])

5 Input and outputs

infosource = Node(IdentityInterface(fields=[‘subject_id’,
‘ROI_id’, ‘contrasts’], contrasts=contrast_list),
name=“infosource”)

infosource.iterables = [(‘subject_id’, subject_list),
(‘ROI_id’, ROI_list),]
##THIS NEEDS TO BE CHANGED TO PROPER NAMES####

anat_file = opj(‘preproc’,’{ROI_id}{subject_id}’, ‘Anat_Orient_brain.nii.gz’)
func_file = opj(‘preproc’,’{ROI_id}
{subject_id}’, ‘Func_Ready_mcf_st_flirt_smooth.nii.gz’)
art_file=opj(‘preproc’,’{ROI_id}{subject_id}’,‘art.Func_Ready_mcf_st_flirt_outliers.txt’)
motionCorrection_parameters=opj(‘preproc’,’{ROI_id}
{subject_id}’,‘Func_Ready_mcf.nii.par’)

templates = {‘anat’: anat_file,
‘func’: func_file,
‘art’: art_file,
‘mc_par’: motionCorrection_parameters}
selectfiles = Node(SelectFiles(templates,
base_directory=preproc_dir,
sort_filelist=True),
name=“selectfiles”)

# Get information for workflows

def subjectinfo(subject_id):
import pandas as pd
from nipype.interfaces.base import Bunch
from os.path import join as opj
#EV_FILE= opj(‘preproc’,‘TMS_Block_Timings.txt’)
trialinfo = pd.read_table(’~/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/preproc/TMS_Block_Timings.txt’)
trialinfo.head()
conditions = []
onsets = []
durations = []
for group in trialinfo.groupby(‘Event’):
conditions.append(group[0])
onsets.append(group[1].Onset.tolist())
durations.append(group[1].Duration.tolist())
subject_info = [Bunch(conditions=conditions,
onsets=onsets,durations=durations
#amplitudes=None,
#tmod=None,
#pmod=None,
#regressor_names=None,
#regressors=None
)]
return subject_info # this output will later be returned to infosource
# Function node to use getsubjectinfo
getsubjectinfo = Node(Function(input_names=[‘subject_id’],
output_names=[‘subject_info’],
function=subjectinfo),
name=‘getsubjectinfo’)

# Set outputs

datasink = Node(DataSink(base_directory=preproc_dir,
container=output_dir),
name=“datasink”)

substitutions = [(’_subject_id’, ‘’),
(‘ROI_id’, ‘’)]
datasink.inputs.substitutions = substitutions
#Figure out substitutions for functional and anatomicals

6 Level1Pipeline Workflow

LEVELONE = Workflow(name=‘LEVELONE’)
LEVELONE.base_dir= preproc_dir
LEVELONE.connect([
(infosource, selectfiles, [(‘subject_id’, ‘subject_id’),
(‘ROI_id’, ‘ROI_id’), (‘contrasts’, ‘contrasts’)]),
(infosource, getsubjectinfo, [(‘subject_id’, ‘subject_id’)]),
(getsubjectinfo, modelprep, [(‘subject_info’, ‘modelspec.subject_info’)]),
(selectfiles, modelprep, [(‘func’, ‘modelspec.functional_runs’)]),
(selectfiles, modelprep, [(‘art’,‘modelspec.outlier_files’),
(‘mc_par’, ‘modelspec.realignment_parameters’)]),
(infosource, modelprep, [(‘contrasts’, ‘level1design.contrasts’)]),#inputs contasts into level1design
(selectfiles, modelestimate, [(‘func’, ‘in_file’)]),
(modelprep, modelestimate, [(‘modelgen.design_file’, ‘design_file’)]),
(modelprep, modelestimate, [(‘modelgen.con_file’, ‘tcon_file’)]),

# (selectfiles, modelfit, [('func', 'modelestimate.in_file')]),
# (modelprep, modelfit, [('modelgen.design_file', 'modelestimate.design_file')]),# could put these two lines in pipelien alter
# (modelprep, modelfit, [('modelgen.con_file', 'conestimate.tcon_file')]),
(modelprep, datasink, [('level1design.fsf_files','LEVELONE.@fsf_files'),
	('level1design.ev_files', 'LEVELONE.@ev_files')]),
(modelestimate, datasink, [('logfile', 'LEVELONE.@logfile'),
	('param_estimates', 'LEVELONE.@param_estimates'),
	('residual4d', 'LEVELONE.@residual4d'),
	('thresholdac', 'LEVELONE.@thresholdac'),
	('copes', 'LEVELONE.@copes'),
	('varcopes', 'LEVELONE.@varcopes'),
	('zstats', 'LEVELONE.@zstats'),
	('tstats', 'LEVELONE.@tstats')]),
# (modelestimate, datasink, [('copes', 'LEVELONE.@copes'),
# 	('varcopes', 'LEVELONE.@varcopes'),
# 	('zstats', 'LEVELONE.@zstats'),
# 	('tstats', 'LEVELONE.@tstats')]),

])

Generate Graph and execute

LEVELONE.write_graph()
LEVELONE.run()

Crashfile:

iMac:fMRI_data marlengonzalez$ nipypecli crash crash-20180407-170629-marlengonzalez-modelgen.a3-8475f95e-b60b-4ed8-a9df-a418f4bafcea.pklz

File: /Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/crash-20180407-170629-marlengonzalez-modelgen.a3-8475f95e-b60b-4ed8-a9df-a418f4bafcea.pklz
Node: LEVELONE.modelprep.modelgen
Working directory: /Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen

Node inputs:

args =
environ = {‘FSLOUTPUTTYPE’: ‘NIFTI_GZ’}

WHERE EVERYTHING GOES WRONG. WHAT ARE THESE???]###

ev_files = [’/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/ev_B1_0_1.txt’, ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/ev_Realign1_0_2.txt’, ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/ev_Realign2_0_3.txt’, ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/ev_Realign3_0_4.txt’, ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/ev_Realign4_0_5.txt’, ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/ev_Realign5_0_6.txt’, ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/ev_Realign6_0_7.txt’, ‘/Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/ev_Outlier1_0_8.txt’]
fsf_file = /Users/marlengonzalez/Documents/Projects/spTMS-fMRI/fMRI_data/PREPROC_Try2/LEVELONE/modelprep/_ROI_id_R_DLPFC_BLOCK_TMS_subject_id_P101/modelgen/run0.fsf
ignore_exception = False
output_type = NIFTI_GZ
terminal_output =

Traceback:
Traceback (most recent call last):
File “/Users/marlengonzalez/anaconda3/lib/python3.6/site-packages/nipype/pipeline/plugins/linear.py”, line 44, in run
node.run(updatehash=updatehash)
File “/Users/marlengonzalez/anaconda3/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py”, line 487, in run
result = self._run_interface(execute=True)
File “/Users/marlengonzalez/anaconda3/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py”, line 571, in _run_interface
return self._run_command(execute)
File “/Users/marlengonzalez/anaconda3/lib/python3.6/site-packages/nipype/pipeline/engine/nodes.py”, line 650, in _run_command
result = self._interface.run(cwd=outdir)
File “/Users/marlengonzalez/anaconda3/lib/python3.6/site-packages/nipype/interfaces/base/core.py”, line 516, in run
runtime = self._run_interface(runtime)
File “/Users/marlengonzalez/anaconda3/lib/python3.6/site-packages/nipype/interfaces/base/core.py”, line 1023, in _run_interface
self.raise_exception(runtime)
File “/Users/marlengonzalez/anaconda3/lib/python3.6/site-packages/nipype/interfaces/base/core.py”, line 960, in raise_exception
).format(**runtime.dictcopy()))
RuntimeError: Command:
feat_model run0
Standard output:

Standard error:
Contrast 1 is empty!
Return code: 1


#2

So i’ve managed to get the Level1Design to create the appropriate text files. Yay! I believe adding absolute paths helped. Though i don’t fully understand why.

While the code runs through just fine now my contrast files generated by FEATModel are completely wrong .It duplicates conditions and I don’t know why. Below is my contrast list i make in my code and the image of the design contrast file generated by FEATModel.

Its a bit discouraging when something you could have done your previous way within an hour takes so much time to work. But I trying to move to python and opensource.

Thanks in advance,
Marlen

Code

condition_names = [‘OFF’, ‘B1’, ‘B2’, ‘B3’, ‘B4’, ‘B5’, ‘B6’,‘B7’,‘B8’,‘B9’,‘B10’]

Contrast01= [‘OFF’, ‘T’, condition_names, [1,0,0,0,0,0,0,0,0,0,0]]
Contrast02= [‘Block1’,‘T’, condition_names, [0,1,0,0,0,0,0,0,0,0,0]]
Contrast03= [‘Block2’,‘T’, condition_names, [0,0,1,0,0,0,0,0,0,0,0]]
Contrast04= [‘Block3’,‘T’, condition_names, [0,0,0,1,0,0,0,0,0,0,0]]
Contrast05= [‘Block4’,‘T’, condition_names, [0,0,0,0,1,0,0,0,0,0,0]]
Contrast06= [‘Block5’,‘T’, condition_names, [0,0,0,0,0,1,0,0,0,0,0]]
Contrast07= [‘Block6’,‘T’, condition_names, [0,0,0,0,0,0,1,0,0,0,0]]
Contrast08= [‘Block7’,‘T’, condition_names, [0,0,0,0,0,0,0,1,0,0,0]]
Contrast09= [‘Block8’,‘T’, condition_names, [0,0,0,0,0,0,0,0,1,0,0]]
Contrast10= [‘Block9’,‘T’, condition_names, [0,0,0,0,0,0,0,0,0,1,0]]
Contrast11= [‘Block10’,‘T’, condition_names,[0,0,0,0,0,0,0,0,0,0,1]]
Contrast12= [‘ON’, ‘T’, condition_names,[0,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.,1/11.]]

contrast_list= [Contrast01,Contrast02,Contrast03,Contrast04,Contrast05,Contrast06,
Contrast07,Contrast08,Contrast09,Contrast10,Contrast11]

Contrast Design Image (FEATModel)


#3

RST File for completeness

Node: modelprep (level1design (fsl)

Hierarchy : LEVELONE.modelprep.level1design
Exec ID : level1design.a0

Original Inputs

  • bases : {‘dgamma’: {‘derivs’: True}}
  • contrasts : [(u’OFF’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (u’B1’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (u’B2’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (u’B3’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (u’B4’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (u’B5’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]), (u’6’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0, 0.0]), (u’B7’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0, 0.0]), (u’B8’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0]), (u’B9’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0, 0.0]), (u’B10’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 1.0]), (u’ON’, ‘T’, [u’OFF’, u’B1’, u’B2’, u’B3’, u’B4’, u’B5’, u’B6’, u’B7’, u’B8’, u’B9’, u’B10’], [0.0, 0.09090909090909091, 0.09090909090909091, 0.09090909090909091, 0.09090909090909091, 0.09090909090909091, 0.09090909090909091, 0.09090909090909091, 0.09090909090909091, 0.09090909090909091, 0.09090909090909091])]
  • ignore_exception : False
  • interscan_interval : 2.5
  • model_serial_correlations : False
  • orthogonalization :
  • session_info : [{u’regress’: [{‘name’: u’Realign1’, ‘val’: [-0.00143538, -0.00166593, -0.00161013, -0.00173158, -0.00180482, -0.00201501, -0.00244749, -0.002519, -0.00285092, -0.00214747, -0.00203162, -0.00259772, -0.00291074, -0.00291929, -0.0018019, -0.000972909, -0.000952417, -0.000743421, -0.000697659, -0.000906172, -0.00177238, -0.00177238, -0.00147012, -0.00229579, -0.00250277, -0.00196542, -0.00200029, -0.00247341, -0.00225548, -0.00208341, -0.0012467, -0.00152893, -0.00206441, -0.00206441, -0.00206441, -0.00176348, -0.00254838, -0.00232721, -0.00226379, -0.00196737, -0.00186616, -0.00232686, -0.00230935, -0.0018479, -0.00177198, -0.00155338, -0.00193494, -0.00112403, -0.00138689, -0.00190233, -0.00173273, -0.00161546, -0.0014998, -0.00178082, -0.00199991, -0.00168994, -0.00181028, -0.00184502, -0.00210356, -0.00210356, -0.000937705, 0.000204887, 0.00209667, 0.00159064, -0.00309534, -0.00146384, -0.00175171, -0.000717458, -0.000604762, -0.00102191, -0.000424893, 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u’name’: ‘B10’}, {u’duration’: [17.5], u’onset’: [67.4], u’name’: ‘B2’}, {u’duration’: [17.5], u’onset’: [102.4], u’name’: ‘B3’}, {u’duration’: [17.5], u’onset’: [172.4], u’name’: ‘B4’}, {u’duration’: [17.5], u’onset’: [207.4], u’name’: ‘B5’}, {u’duration’: [17.5], u’onset’: [242.4], u’name’: ‘B6’}, {u’duration’: [17.5], u’onset’: [277.4], u’name’: ‘B7’}, {u’duration’: [17.5], u’onset’: [347.4], u’name’: ‘B8’}, {u’duration’: [17.5], u’onset’: [382.4], u’name’: ‘B9’}, {u’duration’: [32.4, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.5, 17.6], u’onset’: [0.0, 49.9, 84.9, 119.9, 189.9, 224.9, 259.9, 294.9, 364.9, 399.9, 434.9], u’name’: ‘OFF’}], u’hpf’: 50.0, u’scans’: u’/Volumes/BLUE_2TB/spTMS-fMRI/fMRI_data/NiTry/PREPROC/preproc/LEVELONE/modelprep/_ROI_id_L_DLPFC_BLOCK_TMS_subject_id_P100/modelspec/Low_Res_Func_mcf_st_flirt_smooth.nii.gz’}]

Execution Inputs (Removed for space)

Execution Outputs

  • ev_files : [u’ev_B1_0_1.txt’, u’ev_B10_0_3.txt’, u’ev_B2_0_5.txt’, u’ev_B3_0_7.txt’, u’ev_B4_0_9.txt’, u’ev_B5_0_11.txt’, u’ev_B6_0_13.txt’, u’ev_B7_0_15.txt’, u’ev_B8_0_17.txt’, u’ev_B9_0_19.txt’, u’ev_OFF_0_21.txt’, u’ev_Realign1_0_23.txt’, u’ev_Realign2_0_24.txt’, u’ev_Realign3_0_25.txt’, ev_Realign4_0_26.txt’, u’/Volumes/BLUE_2TB/spTMS-fev_Realign5_0_27.txt’, u’ev_Realign6_0_28.txt’, u’ev_Outlier1_0_29.txt’, u’/ev_Outlier2_0_30.txt’, u’ev_Outlier3_0_31.txt’]
  • fsf_files : level1design/run0.fsf

#4

Fixed! Had to do with improper mapnode at:

modelestimate = MapNode(FILMGLS(smooth_autocorr=True, mask_size=5, threshold=1000),
name=‘modelestimate’,
iterfield=[‘design_file’, ‘in_file’, ‘tcon_file’])

Not sure why it creates 3 artifact files though.