Note
Click here to download the full example code or to run this example in your browser via Binder
Running NiBetaSeries¶
This example runs through a basic call of NiBetaSeries using
the commandline entry point nibs.
While this example is using python, typically nibs will be
called directly on the commandline.
Import all the necessary packages¶
import tempfile # make a temporary directory for files
import os # interact with the filesystem
import urllib.request # grad data from internet
import tarfile # extract files from tar
from subprocess import Popen, PIPE, STDOUT # enable calling commandline
import matplotlib.pyplot as plt # manipulate figures
import seaborn as sns # display results
import pandas as pd # manipulate tabular data
import nibabel as nib # load the beta maps in python
from nilearn import plotting # plot nifti images
Download relevant data from ds000164 (and Atlas Files)¶
The subject data came from openneuro [notebook-1]. The atlas data came from a recently published parcellation in a publically accessible github repository.
# atlas github repo for reference:
"""https://github.com/ThomasYeoLab/CBIG/raw/master/stable_projects/\
brain_parcellation/Schaefer2018_LocalGlobal/Parcellations/MNI/"""
data_dir = tempfile.mkdtemp()
print('Our working directory: {}'.format(data_dir))
# download the tar data
url = "https://www.dropbox.com/s/fvtyld08srwl3x9/ds000164-test_v2.tar.gz?dl=1"
tar_file = os.path.join(data_dir, "ds000164.tar.gz")
u = urllib.request.urlopen(url)
data = u.read()
u.close()
# write tar data to file
with open(tar_file, "wb") as f:
f.write(data)
# extract the data
tar = tarfile.open(tar_file, mode='r|gz')
tar.extractall(path=data_dir)
os.remove(tar_file)
Out:
Our working directory: /tmp/tmphvcov12o
Display the minimal dataset necessary to run nibs¶
# https://stackoverflow.com/questions/9727673/list-directory-tree-structure-in-python
def list_files(startpath):
for root, dirs, files in os.walk(startpath):
level = root.replace(startpath, '').count(os.sep)
indent = ' ' * 4 * (level)
print('{}{}/'.format(indent, os.path.basename(root)))
subindent = ' ' * 4 * (level + 1)
for f in files:
print('{}{}'.format(subindent, f))
list_files(data_dir)
Out:
tmphvcov12o/
ds000164/
task-stroop_events.json
dataset_description.json
T1w.json
README
task-stroop_bold.json
CHANGES
derivatives/
fmriprep/
dataset_description.json
sub-001/
func/
sub-001_task-stroop_desc-confounds_regressors.tsv
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz
data/
Schaefer2018_100Parcels_7Networks_order.txt
Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz
sub-001/
func/
sub-001_task-stroop_bold.nii.gz
sub-001_task-stroop_events.tsv
anat/
sub-001_T1w.nii.gz
Manipulate events file so it satifies assumptions¶
1. the correct column has 1’s and 0’s corresponding to correct and incorrect, respectively. 2. the condition column is renamed to trial_type nibs currently depends on the “correct” column being binary and the “trial_type” column to contain the trial types of interest.
read the file¶
events_file = os.path.join(data_dir,
"ds000164",
"sub-001",
"func",
"sub-001_task-stroop_events.tsv")
events_df = pd.read_csv(events_file, sep='\t', na_values="n/a")
print(events_df.head())
Out:
onset duration correct condition response_time
0 0.342 1 Y neutral 1.186
1 3.345 1 Y congruent 0.667
2 12.346 1 Y congruent 0.614
3 15.349 1 Y neutral 0.696
4 18.350 1 Y neutral 0.752
replace condition with trial_type¶
events_df.rename({"condition": "trial_type"}, axis='columns', inplace=True)
print(events_df.head())
Out:
onset duration correct trial_type response_time
0 0.342 1 Y neutral 1.186
1 3.345 1 Y congruent 0.667
2 12.346 1 Y congruent 0.614
3 15.349 1 Y neutral 0.696
4 18.350 1 Y neutral 0.752
save the file¶
events_df.to_csv(events_file, sep="\t", na_rep="n/a", index=False)
Manipulate the region order file¶
There are several adjustments to the atlas file that need to be completed before we can pass it into nibs. Importantly, the relevant column names MUST be named “index” and “regions”. “index” refers to which integer within the file corresponds to which region in the atlas nifti file. “regions” refers the name of each region in the atlas nifti file.
read the atlas file¶
atlas_txt = os.path.join(data_dir,
"ds000164",
"derivatives",
"data",
"Schaefer2018_100Parcels_7Networks_order.txt")
atlas_df = pd.read_csv(atlas_txt, sep="\t", header=None)
print(atlas_df.head())
Out:
0 1 2 3 4 5
0 1 7Networks_LH_Vis_1 120 18 131 0
1 2 7Networks_LH_Vis_2 120 18 132 0
2 3 7Networks_LH_Vis_3 120 18 133 0
3 4 7Networks_LH_Vis_4 120 18 135 0
4 5 7Networks_LH_Vis_5 120 18 136 0
drop color coding columns¶
atlas_df.drop([2, 3, 4, 5], axis='columns', inplace=True)
print(atlas_df.head())
Out:
0 1
0 1 7Networks_LH_Vis_1
1 2 7Networks_LH_Vis_2
2 3 7Networks_LH_Vis_3
3 4 7Networks_LH_Vis_4
4 5 7Networks_LH_Vis_5
rename columns with the approved headings: “index” and “regions”¶
atlas_df.rename({0: 'index', 1: 'regions'}, axis='columns', inplace=True)
print(atlas_df.head())
Out:
index regions
0 1 7Networks_LH_Vis_1
1 2 7Networks_LH_Vis_2
2 3 7Networks_LH_Vis_3
3 4 7Networks_LH_Vis_4
4 5 7Networks_LH_Vis_5
remove prefix “7Networks”¶
atlas_df.replace(regex={'7Networks_(.*)': '\\1'}, inplace=True)
print(atlas_df.head())
Out:
index regions
0 1 LH_Vis_1
1 2 LH_Vis_2
2 3 LH_Vis_3
3 4 LH_Vis_4
4 5 LH_Vis_5
write out the file as .tsv¶
atlas_tsv = atlas_txt.replace(".txt", ".tsv")
atlas_df.to_csv(atlas_tsv, sep="\t", index=False)
Run nibs¶
This demonstration mimics how you would use nibs on the command line If you only wanted the beta maps and not the correlation matrices, do not include the atlas (-a) and lookup table options (-l)
out_dir = os.path.join(data_dir, "ds000164", "derivatives")
work_dir = os.path.join(out_dir, "work")
atlas_mni_file = os.path.join(data_dir,
"ds000164",
"derivatives",
"data",
"Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz")
cmd = """\
nibs -c WhiteMatter CSF \
--participant-label 001 \
--estimator lsa \
--hrf-model glover \
-w {work_dir} \
-a {atlas_mni_file} \
-l {atlas_tsv} \
{bids_dir} \
fmriprep \
{out_dir} \
participant
""".format(atlas_mni_file=atlas_mni_file,
atlas_tsv=atlas_tsv,
bids_dir=os.path.join(data_dir, "ds000164"),
out_dir=out_dir,
work_dir=work_dir)
# Since we cannot run bash commands inside this tutorial
# we are printing the actual bash command so you can see it
# in the output
print("The Example Command:\n", cmd)
# call nibs
p = Popen(cmd, shell=True, stdout=PIPE, stderr=STDOUT)
while True:
line = p.stdout.readline()
if not line:
break
print(line)
Out:
The Example Command:
nibs -c WhiteMatter CSF --participant-label 001 --estimator lsa --hrf-model glover -w /tmp/tmphvcov12o/ds000164/derivatives/work -a /tmp/tmphvcov12o/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz -l /tmp/tmphvcov12o/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order.tsv /tmp/tmphvcov12o/ds000164 fmriprep /tmp/tmphvcov12o/ds000164/derivatives participant
b'200320-20:15:14,98 nipype.utils INFO:\n'
b'\t No new version available.\n'
b'200320-20:15:14,228 nipype.workflow INFO:\n'
b"\t Workflow nibetaseries_participant_wf settings: ['check', 'execution', 'logging', 'monitoring']\n"
b'200320-20:15:14,300 nipype.workflow INFO:\n'
b'\t Running in parallel.\n'
b'200320-20:15:14,321 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b"/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/bids/layout/models.py:157: UserWarning: Accessing entities as attributes is deprecated as of 0.7. Please use the .entities dictionary instead (i.e., .entities['task'] instead of .task.\n"
b' % (attr, attr))\n'
b"/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/bids/layout/models.py:157: UserWarning: Accessing entities as attributes is deprecated as of 0.7. Please use the .entities dictionary instead (i.e., .entities['space'] instead of .space.\n"
b' % (attr, attr))\n'
b'200320-20:15:14,396 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.betaseries_wf.betaseries_node" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/betaseries_node".\n'
b'200320-20:15:14,402 nipype.workflow INFO:\n'
b'\t [Node] Running "betaseries_node" ("nibetaseries.interfaces.nistats.LSABetaSeries")\n'
b'200320-20:15:16,315 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 1 tasks, and 0 jobs ready. Free memory (GB): 6.79/6.99, Free processors: 3/4.\n'
b' Currently running:\n'
b' * nibetaseries_participant_wf.single_subject001_wf.betaseries_wf.betaseries_node\n'
b'200320-20:15:22,916 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.betaseries_wf.betaseries_node".\n'
b'200320-20:15:24,321 nipype.workflow INFO:\n'
b'\t [Job 0] Completed (nibetaseries_participant_wf.single_subject001_wf.betaseries_wf.betaseries_node).\n'
b'200320-20:15:24,329 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 2 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:26,324 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 6 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:26,380 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_betaseries_file/mapflow/_ds_betaseries_file0".\n'
b'200320-20:15:26,381 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_betaseries_file/mapflow/_ds_betaseries_file1".\n'
b'200320-20:15:26,384 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:26,385 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:26,393 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_betaseries_file/mapflow/_ds_betaseries_file2".\n'
b'200320-20:15:26,396 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_censor_volumes0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/censor_volumes/mapflow/_censor_volumes0".\n'
b'200320-20:15:26,398 nipype.workflow INFO:\n'
b'\t [Node] Running "_censor_volumes0" ("nibetaseries.interfaces.nilearn.CensorVolumes")\n'
b'200320-20:15:26,401 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file1".\n'
b'200320-20:15:26,401 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file0".\n'
b'200320-20:15:26,404 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:26,418 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file2".\n'
b'200320-20:15:26,459 nipype.workflow INFO:\n'
b'\t [Node] Finished "_censor_volumes0".\n'
b'200320-20:15:28,325 nipype.workflow INFO:\n'
b'\t [Job 7] Completed (_ds_betaseries_file0).\n'
b'200320-20:15:28,325 nipype.workflow INFO:\n'
b'\t [Job 8] Completed (_ds_betaseries_file1).\n'
b'200320-20:15:28,326 nipype.workflow INFO:\n'
b'\t [Job 9] Completed (_ds_betaseries_file2).\n'
b'200320-20:15:28,327 nipype.workflow INFO:\n'
b'\t [Job 10] Completed (_censor_volumes0).\n'
b'200320-20:15:28,328 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 3 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:28,382 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.ds_betaseries_file" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_betaseries_file".\n'
b'200320-20:15:28,384 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_censor_volumes1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/censor_volumes/mapflow/_censor_volumes1".\n'
b'200320-20:15:28,386 nipype.workflow INFO:\n'
b'\t [Node] Running "_censor_volumes1" ("nibetaseries.interfaces.nilearn.CensorVolumes")\n'
b'200320-20:15:28,387 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_betaseries_file/mapflow/_ds_betaseries_file0".\n'
b'200320-20:15:28,389 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_censor_volumes2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/censor_volumes/mapflow/_censor_volumes2".\n'
b'200320-20:15:28,389 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:28,391 nipype.workflow INFO:\n'
b'\t [Node] Running "_censor_volumes2" ("nibetaseries.interfaces.nilearn.CensorVolumes")\n'
b'200320-20:15:28,399 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file0".\n'
b'200320-20:15:28,401 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_betaseries_file/mapflow/_ds_betaseries_file1".\n'
b'200320-20:15:28,403 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:28,413 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file1".\n'
b'200320-20:15:28,414 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_betaseries_file/mapflow/_ds_betaseries_file2".\n'
b'200320-20:15:28,416 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:28,425 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file2".\n'
b'200320-20:15:28,427 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.ds_betaseries_file".\n'
b'200320-20:15:28,449 nipype.workflow INFO:\n'
b'\t [Node] Finished "_censor_volumes2".\n'
b'200320-20:15:28,451 nipype.workflow INFO:\n'
b'\t [Node] Finished "_censor_volumes1".\n'
b'200320-20:15:30,327 nipype.workflow INFO:\n'
b'\t [Job 1] Completed (nibetaseries_participant_wf.single_subject001_wf.ds_betaseries_file).\n'
b'200320-20:15:30,328 nipype.workflow INFO:\n'
b'\t [Job 11] Completed (_censor_volumes1).\n'
b'200320-20:15:30,329 nipype.workflow INFO:\n'
b'\t [Job 12] Completed (_censor_volumes2).\n'
b'200320-20:15:30,330 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:30,387 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.censor_volumes" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/censor_volumes".\n'
b'200320-20:15:30,391 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_censor_volumes0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/censor_volumes/mapflow/_censor_volumes0".\n'
b'200320-20:15:30,392 nipype.workflow INFO:\n'
b'\t [Node] Cached "_censor_volumes0" - collecting precomputed outputs\n'
b'200320-20:15:30,392 nipype.workflow INFO:\n'
b'\t [Node] "_censor_volumes0" found cached.\n'
b'200320-20:15:30,393 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_censor_volumes1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/censor_volumes/mapflow/_censor_volumes1".\n'
b'200320-20:15:30,394 nipype.workflow INFO:\n'
b'\t [Node] Cached "_censor_volumes1" - collecting precomputed outputs\n'
b'200320-20:15:30,394 nipype.workflow INFO:\n'
b'\t [Node] "_censor_volumes1" found cached.\n'
b'200320-20:15:30,395 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_censor_volumes2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/censor_volumes/mapflow/_censor_volumes2".\n'
b'200320-20:15:30,396 nipype.workflow INFO:\n'
b'\t [Node] Cached "_censor_volumes2" - collecting precomputed outputs\n'
b'200320-20:15:30,396 nipype.workflow INFO:\n'
b'\t [Node] "_censor_volumes2" found cached.\n'
b'200320-20:15:30,398 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.censor_volumes".\n'
b'200320-20:15:32,329 nipype.workflow INFO:\n'
b'\t [Job 2] Completed (nibetaseries_participant_wf.single_subject001_wf.censor_volumes).\n'
b'200320-20:15:32,331 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:32,386 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.check_beta_series_list" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/check_beta_series_list".\n'
b'200320-20:15:32,390 nipype.workflow INFO:\n'
b'\t [Node] Running "check_beta_series_list" ("nipype.interfaces.utility.wrappers.Function")\n'
b'200320-20:15:32,398 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.check_beta_series_list".\n'
b'200320-20:15:34,331 nipype.workflow INFO:\n'
b'\t [Job 3] Completed (nibetaseries_participant_wf.single_subject001_wf.check_beta_series_list).\n'
b'200320-20:15:34,333 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:36,334 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 3 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:36,381 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/atlas_corr_node/mapflow/_atlas_corr_node0".\n'
b'200320-20:15:36,382 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/atlas_corr_node/mapflow/_atlas_corr_node1".\n'
b'200320-20:15:36,384 nipype.workflow INFO:\n'
b'\t [Node] Running "_atlas_corr_node0" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'200320-20:15:36,384 nipype.workflow INFO:\n'
b'\t [Node] Running "_atlas_corr_node1" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'200320-20:15:36,384 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/atlas_corr_node/mapflow/_atlas_corr_node2".\n'
b'200320-20:15:36,387 nipype.workflow INFO:\n'
b'\t [Node] Running "_atlas_corr_node2" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'200320-20:15:38,335 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 3 tasks, and 0 jobs ready. Free memory (GB): 6.39/6.99, Free processors: 1/4.\n'
b' Currently running:\n'
b' * _atlas_corr_node2\n'
b' * _atlas_corr_node1\n'
b' * _atlas_corr_node0\n'
b'[NiftiLabelsMasker.fit_transform] loading data from /tmp/tmphvcov12o/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz\n'
b'Resampling labels\n'
b'[NiftiLabelsMasker.transform_single_imgs] Loading data from /tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/betaseries_node/desc-neutral_betase\n'
b'[NiftiLabelsMasker.transform_single_imgs] Extracting region signals\n'
b'[NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals\n'
b'200320-20:15:48,58 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node0".\n'
b'[NiftiLabelsMasker.fit_transform] loading data from /tmp/tmphvcov12o/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz\n'
b'Resampling labels\n'
b'[NiftiLabelsMasker.transform_single_imgs] Loading data from /tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/betaseries_node/desc-incongruent_be\n'
b'[NiftiLabelsMasker.transform_single_imgs] Extracting region signals\n'
b'[NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals\n'
b'200320-20:15:48,70 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node2".\n'
b'[NiftiLabelsMasker.fit_transform] loading data from /tmp/tmphvcov12o/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz\n'
b'Resampling labels\n'
b'[NiftiLabelsMasker.transform_single_imgs] Loading data from /tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/betaseries_node/desc-congruent_beta\n'
b'[NiftiLabelsMasker.transform_single_imgs] Extracting region signals\n'
b'[NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals\n'
b'200320-20:15:48,248 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node1".\n'
b'200320-20:15:48,345 nipype.workflow INFO:\n'
b'\t [Job 13] Completed (_atlas_corr_node0).\n'
b'200320-20:15:48,346 nipype.workflow INFO:\n'
b'\t [Job 14] Completed (_atlas_corr_node1).\n'
b'200320-20:15:48,346 nipype.workflow INFO:\n'
b'\t [Job 15] Completed (_atlas_corr_node2).\n'
b'200320-20:15:48,348 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:48,404 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/atlas_corr_node".\n'
b'200320-20:15:48,409 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/atlas_corr_node/mapflow/_atlas_corr_node0".\n'
b'200320-20:15:48,410 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node0" - collecting precomputed outputs\n'
b'200320-20:15:48,410 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node0" found cached.\n'
b'200320-20:15:48,411 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/atlas_corr_node/mapflow/_atlas_corr_node1".\n'
b'200320-20:15:48,412 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node1" - collecting precomputed outputs\n'
b'200320-20:15:48,412 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node1" found cached.\n'
b'200320-20:15:48,413 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/atlas_corr_node/mapflow/_atlas_corr_node2".\n'
b'200320-20:15:48,415 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node2" - collecting precomputed outputs\n'
b'200320-20:15:48,415 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node2" found cached.\n'
b'200320-20:15:48,416 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node".\n'
b'200320-20:15:50,347 nipype.workflow INFO:\n'
b'\t [Job 4] Completed (nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node).\n'
b'200320-20:15:50,349 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 2 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:52,350 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 6 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:52,405 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_fig/mapflow/_ds_correlation_fig0".\n'
b'200320-20:15:52,406 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_fig/mapflow/_ds_correlation_fig1".\n'
b'200320-20:15:52,407 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:52,408 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:52,409 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_fig/mapflow/_ds_correlation_fig2".\n'
b'200320-20:15:52,410 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_matrix/mapflow/_ds_correlation_matrix0".\n'
b'200320-20:15:52,411 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:52,412 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:52,427 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix0".\n'
b'200320-20:15:52,429 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig2".\n'
b'200320-20:15:52,435 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig0".\n'
b'200320-20:15:52,438 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig1".\n'
b'200320-20:15:54,351 nipype.workflow INFO:\n'
b'\t [Job 16] Completed (_ds_correlation_fig0).\n'
b'200320-20:15:54,352 nipype.workflow INFO:\n'
b'\t [Job 17] Completed (_ds_correlation_fig1).\n'
b'200320-20:15:54,352 nipype.workflow INFO:\n'
b'\t [Job 18] Completed (_ds_correlation_fig2).\n'
b'200320-20:15:54,353 nipype.workflow INFO:\n'
b'\t [Job 19] Completed (_ds_correlation_matrix0).\n'
b'200320-20:15:54,354 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 3 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:54,406 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_fig".\n'
b'200320-20:15:54,408 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_matrix/mapflow/_ds_correlation_matrix2".\n'
b'200320-20:15:54,410 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_fig/mapflow/_ds_correlation_fig0".\n'
b'200320-20:15:54,410 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:54,413 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:54,416 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_matrix/mapflow/_ds_correlation_matrix1".\n'
b'200320-20:15:54,418 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:54,426 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig0".\n'
b'200320-20:15:54,426 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix1".\n'
b'200320-20:15:54,427 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_fig/mapflow/_ds_correlation_fig1".\n'
b'200320-20:15:54,430 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:54,430 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix2".\n'
b'200320-20:15:54,441 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig1".\n'
b'200320-20:15:54,442 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_fig/mapflow/_ds_correlation_fig2".\n'
b'200320-20:15:54,445 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:54,456 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig2".\n'
b'200320-20:15:54,458 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig".\n'
b'200320-20:15:56,353 nipype.workflow INFO:\n'
b'\t [Job 5] Completed (nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig).\n'
b'200320-20:15:56,354 nipype.workflow INFO:\n'
b'\t [Job 20] Completed (_ds_correlation_matrix1).\n'
b'200320-20:15:56,355 nipype.workflow INFO:\n'
b'\t [Job 21] Completed (_ds_correlation_matrix2).\n'
b'200320-20:15:56,357 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b'200320-20:15:56,410 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_matrix".\n'
b'200320-20:15:56,414 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix0" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_matrix/mapflow/_ds_correlation_matrix0".\n'
b'200320-20:15:56,416 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:56,425 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix0".\n'
b'200320-20:15:56,426 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix1" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_matrix/mapflow/_ds_correlation_matrix1".\n'
b'200320-20:15:56,428 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:56,436 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix1".\n'
b'200320-20:15:56,437 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix2" in "/tmp/tmphvcov12o/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/be88f8479de851cec27ee0e444fe7a56a8598c6d/ds_correlation_matrix/mapflow/_ds_correlation_matrix2".\n'
b'200320-20:15:56,439 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'200320-20:15:56,447 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix2".\n'
b'200320-20:15:56,449 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix".\n'
b'200320-20:15:58,355 nipype.workflow INFO:\n'
b'\t [Job 6] Completed (nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix).\n'
b'200320-20:15:58,357 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 6.99/6.99, Free processors: 4/4.\n'
b"/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:17: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n"
b' from collections import Mapping, defaultdict\n'
b'/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:130: RuntimeWarning: invalid value encountered in greater\n'
b' n_lines = int(np.sum(connmat > 0) / 2)\n'
b"/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:17: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n"
b' from collections import Mapping, defaultdict\n'
b'/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:130: RuntimeWarning: invalid value encountered in greater\n'
b' n_lines = int(np.sum(connmat > 0) / 2)\n'
b'/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/nibetaseries/interfaces/nistats.py:216: DeprecationWarning: The parameter "mask" will be removed in next release of Nistats. Please use the parameter "mask_img" instead.\n'
b' minimize_memory=False,\n'
b'Computing run 1 out of 1 runs (go take a coffee, a big one)\n'
b'\n'
b'Computation of 1 runs done in 5 seconds\n'
b'\n'
b"/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/nistats/regression.py:339: FutureWarning: 'resid' from RegressionResults has been deprecated and will be removed. Please use 'residuals' instead.\n"
b' FutureWarning,\n'
b"/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/sklearn/feature_extraction/text.py:17: DeprecationWarning: Using or importing the ABCs from 'collections' instead of from 'collections.abc' is deprecated, and in 3.8 it will stop working\n"
b' from collections import Mapping, defaultdict\n'
b'/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:130: RuntimeWarning: invalid value encountered in greater\n'
b' n_lines = int(np.sum(connmat > 0) / 2)\n'
b'pandoc: Error running filter pandoc-citeproc:\n'
b"Could not find executable 'pandoc-citeproc'.\n"
b'Could not generate CITATION.html file:\n'
b'pandoc -s --bibliography /home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/nibetaseries/data/references.bib --filter pandoc-citeproc --metadata pagetitle="NiBetaSeries citation boilerplate" /tmp/tmphvcov12o/ds000164/derivatives/nibetaseries/logs/CITATION.md -o /tmp/tmphvcov12o/ds000164/derivatives/nibetaseries/logs/CITATION.html\n'
Observe generated outputs¶
list_files(data_dir)
Out:
tmphvcov12o/
ds000164/
task-stroop_events.json
dataset_description.json
T1w.json
README
task-stroop_bold.json
CHANGES
derivatives/
fmriprep/
fMRIPrep.sqlite
dataset_description.json
sub-001/
func/
sub-001_task-stroop_desc-confounds_regressors.tsv
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz
nibetaseries/
logs/
CITATION.bib
CITATION.md
CITATION.tex
sub-001/
func/
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-congruent_correlation.svg
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-incongruent_correlation.svg
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-neutral_correlation.tsv
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-neutral_betaseries.nii.gz
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-incongruent_correlation.tsv
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-incongruent_betaseries.nii.gz
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-neutral_correlation.svg
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-congruent_betaseries.nii.gz
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-congruent_correlation.tsv
work/
dbcache
NiBetaSeries_work/
nibetaseries_participant_wf/
graph1.json
index.html
graph.json
d3.js
single_subject001_wf/
be88f8479de851cec27ee0e444fe7a56a8598c6d/
censor_volumes/
_node.pklz
_inputs.pklz
_0x6f805cc53fd12d3ba5cae9b559315d9d.json
result_censor_volumes.pklz
_report/
report.rst
mapflow/
_censor_volumes0/
_0xc45174ba67a744b372bb745c0016a068.json
_node.pklz
result__censor_volumes0.pklz
_inputs.pklz
_report/
report.rst
_censor_volumes1/
_node.pklz
result__censor_volumes1.pklz
_0xb5cff9508d598e63fcb3b968877688de.json
_inputs.pklz
_report/
report.rst
_censor_volumes2/
_node.pklz
_0x698a29243ebb5f7848f0eaf91bbb25ae.json
result__censor_volumes2.pklz
_inputs.pklz
_report/
report.rst
ds_correlation_fig/
_0x5851564c514f9fe7f6cda313649cefa5.json
_node.pklz
result_ds_correlation_fig.pklz
_inputs.pklz
_report/
report.rst
mapflow/
_ds_correlation_fig0/
_node.pklz
_inputs.pklz
_0x3b90987315ef499f7da10e2acbf4aabb.json
result__ds_correlation_fig0.pklz
_report/
report.rst
_ds_correlation_fig2/
result__ds_correlation_fig2.pklz
_node.pklz
_inputs.pklz
_0x20ae5f1caf5398f07e8d200248f3849a.json
_report/
report.rst
_ds_correlation_fig1/
_node.pklz
_0x277e302dca216b38f17aa2e151c87110.json
result__ds_correlation_fig1.pklz
_inputs.pklz
_report/
report.rst
ds_correlation_matrix/
_node.pklz
result_ds_correlation_matrix.pklz
_inputs.pklz
_0x810fa1ca1227334fe993ec3c83b04468.json
_report/
report.rst
mapflow/
_ds_correlation_matrix0/
_node.pklz
_inputs.pklz
result__ds_correlation_matrix0.pklz
_0xc72c39bd65cc269df899c4152db65148.json
_report/
report.rst
_ds_correlation_matrix1/
_node.pklz
_0xbd0aba1914429e59c0e2fc933e219bf1.json
_inputs.pklz
result__ds_correlation_matrix1.pklz
_report/
report.rst
_ds_correlation_matrix2/
_node.pklz
_0x36230b4a1eb3ac6b03d35af098695e53.json
_inputs.pklz
result__ds_correlation_matrix2.pklz
_report/
report.rst
ds_betaseries_file/
_node.pklz
result_ds_betaseries_file.pklz
_0x9417fa4fd1f9a6b4dfbd236c32151909.json
_inputs.pklz
_report/
report.rst
mapflow/
_ds_betaseries_file0/
_node.pklz
result__ds_betaseries_file0.pklz
_0x09a283f5f6e38e10328513d5854ece1d.json
_inputs.pklz
_report/
report.rst
_ds_betaseries_file1/
_node.pklz
_0x669ed6ce51cb29702409d56a74a03dbe.json
result__ds_betaseries_file1.pklz
_inputs.pklz
_report/
report.rst
_ds_betaseries_file2/
result__ds_betaseries_file2.pklz
_node.pklz
_0xa8b44a752e96b1b030c94c154b6c7a97.json
_inputs.pklz
_report/
report.rst
check_beta_series_list/
_node.pklz
_inputs.pklz
_0x948d0594f2d57214d6c191f678765429.json
result_check_beta_series_list.pklz
_report/
report.rst
betaseries_wf/
be88f8479de851cec27ee0e444fe7a56a8598c6d/
betaseries_node/
desc-incongruent_betaseries.nii.gz
_node.pklz
desc-neutral_betaseries.nii.gz
desc-congruent_betaseries_censored.nii.gz
result_betaseries_node.pklz
desc-congruent_betaseries.nii.gz
_inputs.pklz
desc-neutral_betaseries_censored.nii.gz
_0x754c19a7c0e3a13c381d837c3a83fd6f.json
desc-incongruent_betaseries_censored.nii.gz
_report/
report.rst
correlation_wf/
be88f8479de851cec27ee0e444fe7a56a8598c6d/
atlas_corr_node/
_node.pklz
result_atlas_corr_node.pklz
_inputs.pklz
_0x98772d55b79ccbbb896c67ce72dbfd28.json
_report/
report.rst
mapflow/
_atlas_corr_node0/
_node.pklz
result__atlas_corr_node0.pklz
_inputs.pklz
desc-neutral_correlation.svg
desc-neutral_correlation.tsv
_0x90ee14ed3b6b965213d5e199829692b1.json
_report/
report.rst
_atlas_corr_node1/
_node.pklz
_0xd8fda64788f3b4369f3bcc0c4d315ca7.json
result__atlas_corr_node1.pklz
desc-congruent_correlation.tsv
desc-congruent_correlation.svg
_inputs.pklz
_report/
report.rst
_atlas_corr_node2/
_node.pklz
result__atlas_corr_node2.pklz
desc-incongruent_correlation.svg
desc-incongruent_correlation.tsv
_inputs.pklz
_0x146c3219579a52accc700e4eb1a86887.json
_report/
report.rst
data/
Schaefer2018_100Parcels_7Networks_order.tsv
Schaefer2018_100Parcels_7Networks_order.txt
Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz
sub-001/
func/
sub-001_task-stroop_bold.nii.gz
sub-001_task-stroop_events.tsv
anat/
sub-001_T1w.nii.gz
Collect correlation results¶
output_path = os.path.join(out_dir, "nibetaseries", "sub-001", "func")
trial_types = ['congruent', 'incongruent', 'neutral']
filename_template = ('sub-001_task-stroop_space-MNI152NLin2009cAsym_'
'desc-{trial_type}_{suffix}.{ext}')
pd_dict = {}
for trial_type in trial_types:
fname = filename_template.format(trial_type=trial_type, suffix='correlation', ext='tsv')
file_path = os.path.join(output_path, fname)
pd_dict[trial_type] = pd.read_csv(file_path, sep='\t', na_values="n/a", index_col=0)
# display example matrix
print(pd_dict[trial_type].head())
Out:
LH_Vis_1 LH_Vis_2 ... RH_Default_PCC_1 RH_Default_PCC_2
LH_Vis_1 NaN 0.218441 ... -0.229994 0.030440
LH_Vis_2 0.218441 NaN ... -0.272071 0.051983
LH_Vis_3 -0.193264 0.448418 ... 0.087879 0.083368
LH_Vis_4 0.122960 0.526585 ... -0.185481 0.188477
LH_Vis_5 0.284347 0.534536 ... -0.393536 -0.288098
[5 rows x 100 columns]
Graph the correlation results¶
fig, axes = plt.subplots(nrows=3, ncols=1, sharex=True, sharey=True, figsize=(10, 30),
gridspec_kw={'wspace': 0.025, 'hspace': 0.075})
cbar_ax = fig.add_axes([.91, .3, .03, .4])
r = 0
for trial_type, df in pd_dict.items():
g = sns.heatmap(df, ax=axes[r], vmin=-.5, vmax=1., square=True,
cbar=True, cbar_ax=cbar_ax)
axes[r].set_title(trial_type)
# iterate over rows
r += 1
plt.tight_layout()
Out:
/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.5.0/lib/python3.7/site-packages/matplotlib/figure.py:2299: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.
warnings.warn("This figure includes Axes that are not compatible "
Collect beta map results¶
nii_dict = {}
for trial_type in trial_types:
fname = filename_template.format(trial_type=trial_type, suffix='betaseries', ext='nii.gz')
file_path = os.path.join(output_path, fname)
nii_dict[trial_type] = nib.load(file_path)
# view incongruent beta_maps
nib.viewers.OrthoSlicer3D(nii_dict['incongruent'].get_fdata(),
title="Incongruent Betas").set_position(10, 13, 10)
Graph beta map standard deviation¶
We can find where the betas have the highest standard deviation for each trial type. Unsuprisingly, the largest deviations are near the edge of the brain mask and the subcortical regions.
# standard deviations for each trial type
std_dict = {tt: nib.Nifti1Image(img.get_fdata().std(axis=-1), img.affine, img.header)
for tt, img in nii_dict.items()}
fig, axes = plt.subplots(nrows=3, ncols=1, figsize=(10, 20))
for idx, (trial_type, nii) in enumerate(std_dict.items()):
plotting.plot_stat_map(nii, title=trial_type, cut_coords=(0, 0, 0),
threshold=5, vmax=20, axes=axes[idx])
References¶
- notebook-1
Timothy D Verstynen. The organization and dynamics of corticostriatal pathways link the medial orbitofrontal cortex to future behavioral responses. Journal of Neurophysiology, 112(10):2457–2469, 2014. URL: https://doi.org/10.1152/jn.00221.2014, doi:10.1152/jn.00221.2014.
Total running time of the script: ( 1 minutes 14.827 seconds)
