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
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/tmpm8mit_gv
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:
tmpm8mit_gv/
ds000164/
T1w.json
README
task-stroop_bold.json
dataset_description.json
task-stroop_events.json
CHANGES
derivatives/
data/
Schaefer2018_100Parcels_7Networks_order.txt
Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz
fmriprep/
dataset_description.json
sub-001/
func/
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
sub-001_task-stroop_desc-confounds_regressors.tsv
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz
sub-001/
anat/
sub-001_T1w.nii.gz
func/
sub-001_task-stroop_bold.nii.gz
sub-001_task-stroop_events.tsv
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¶
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 lss \
-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 lss -w /tmp/tmpm8mit_gv/ds000164/derivatives/work -a /tmp/tmpm8mit_gv/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz -l /tmp/tmpm8mit_gv/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order.tsv /tmp/tmpm8mit_gv/ds000164 fmriprep /tmp/tmpm8mit_gv/ds000164/derivatives participant
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b'\t [Node] Setting-up "_ds_betaseries_file1" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_betaseries_file/mapflow/_ds_betaseries_file1".\n'
b'191227-05:56:39,244 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file2" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_betaseries_file/mapflow/_ds_betaseries_file2".\n'
b'191227-05:56:39,247 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node0" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/atlas_corr_node/mapflow/_atlas_corr_node0".\n'
b'191227-05:56:39,254 nipype.workflow INFO:\n'
b'\t [Node] Running "_atlas_corr_node0" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'191227-05:56:39,255 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:39,255 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:39,255 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:39,337 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file0".\n'
b'191227-05:56:39,337 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file1".\n'
b'191227-05:56:39,337 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file2".\n'
b'191227-05:56:41,202 nipype.workflow INFO:\n'
b'\t [Job 5] Completed (_ds_betaseries_file0).\n'
b'191227-05:56:41,203 nipype.workflow INFO:\n'
b'\t [Job 6] Completed (_ds_betaseries_file1).\n'
b'191227-05:56:41,204 nipype.workflow INFO:\n'
b'\t [Job 7] Completed (_ds_betaseries_file2).\n'
b'191227-05:56:41,205 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 1 tasks, and 3 jobs ready. Free memory (GB): 6.81/7.01, Free processors: 3/4.\n'
b' Currently running:\n'
b' * _atlas_corr_node0\n'
b'191227-05:56:41,246 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.ds_betaseries_file" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_betaseries_file".\n'
b'191227-05:56:41,247 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node1" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/atlas_corr_node/mapflow/_atlas_corr_node1".\n'
b'191227-05:56:41,248 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node2" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/atlas_corr_node/mapflow/_atlas_corr_node2".\n'
b'191227-05:56:41,249 nipype.workflow INFO:\n'
b'\t [Node] Running "_atlas_corr_node1" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'191227-05:56:41,250 nipype.workflow INFO:\n'
b'\t [Node] Running "_atlas_corr_node2" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'191227-05:56:41,250 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file0" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_betaseries_file/mapflow/_ds_betaseries_file0".\n'
b'191227-05:56:41,253 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:41,262 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file0".\n'
b'191227-05:56:41,263 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file1" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_betaseries_file/mapflow/_ds_betaseries_file1".\n'
b'191227-05:56:41,265 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:41,274 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file1".\n'
b'191227-05:56:41,275 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_betaseries_file2" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_betaseries_file/mapflow/_ds_betaseries_file2".\n'
b'191227-05:56:41,277 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_betaseries_file2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:41,286 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_betaseries_file2".\n'
b'191227-05:56:41,287 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.ds_betaseries_file".\n'
b'191227-05:56:43,205 nipype.workflow INFO:\n'
b'\t [Job 1] Completed (nibetaseries_participant_wf.single_subject001_wf.ds_betaseries_file).\n'
b'191227-05:56:43,207 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 3 tasks, and 0 jobs ready. Free memory (GB): 6.41/7.01, 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/tmpm8mit_gv/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/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/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'191227-05:56:50,322 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node1".\n'
b'[NiftiLabelsMasker.fit_transform] loading data from /tmp/tmpm8mit_gv/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/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/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'191227-05:56:50,343 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node0".\n'
b'[NiftiLabelsMasker.fit_transform] loading data from /tmp/tmpm8mit_gv/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/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/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'191227-05:56:50,445 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node2".\n'
b'191227-05:56:51,212 nipype.workflow INFO:\n'
b'\t [Job 8] Completed (_atlas_corr_node0).\n'
b'191227-05:56:51,213 nipype.workflow INFO:\n'
b'\t [Job 9] Completed (_atlas_corr_node1).\n'
b'191227-05:56:51,213 nipype.workflow INFO:\n'
b'\t [Job 10] Completed (_atlas_corr_node2).\n'
b'191227-05:56:51,215 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 7.01/7.01, Free processors: 4/4.\n'
b'191227-05:56:51,253 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/atlas_corr_node".\n'
b'191227-05:56:51,257 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node0" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/atlas_corr_node/mapflow/_atlas_corr_node0".\n'
b'191227-05:56:51,268 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node0" - collecting precomputed outputs\n'
b'191227-05:56:51,268 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node0" found cached.\n'
b'191227-05:56:51,269 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node1" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/atlas_corr_node/mapflow/_atlas_corr_node1".\n'
b'191227-05:56:51,270 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node1" - collecting precomputed outputs\n'
b'191227-05:56:51,270 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node1" found cached.\n'
b'191227-05:56:51,271 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node2" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/atlas_corr_node/mapflow/_atlas_corr_node2".\n'
b'191227-05:56:51,272 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node2" - collecting precomputed outputs\n'
b'191227-05:56:51,272 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node2" found cached.\n'
b'191227-05:56:51,274 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node".\n'
b'191227-05:56:53,214 nipype.workflow INFO:\n'
b'\t [Job 2] Completed (nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node).\n'
b'191227-05:56:53,216 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 2 jobs ready. Free memory (GB): 7.01/7.01, Free processors: 4/4.\n'
b'191227-05:56:55,217 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 6 jobs ready. Free memory (GB): 7.01/7.01, Free processors: 4/4.\n'
b'191227-05:56:55,256 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig0" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_fig/mapflow/_ds_correlation_fig0".\n'
b'191227-05:56:55,257 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig1" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_fig/mapflow/_ds_correlation_fig1".\n'
b'191227-05:56:55,258 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:55,258 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig2" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_fig/mapflow/_ds_correlation_fig2".\n'
b'191227-05:56:55,259 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:55,260 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:55,268 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix0" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_matrix/mapflow/_ds_correlation_matrix0".\n'
b'191227-05:56:55,270 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig0".\n'
b'191227-05:56:55,270 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:55,271 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig2".\n'
b'191227-05:56:55,273 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig1".\n'
b'191227-05:56:55,278 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix0".\n'
b'191227-05:56:57,216 nipype.workflow INFO:\n'
b'\t [Job 11] Completed (_ds_correlation_fig0).\n'
b'191227-05:56:57,217 nipype.workflow INFO:\n'
b'\t [Job 12] Completed (_ds_correlation_fig1).\n'
b'191227-05:56:57,217 nipype.workflow INFO:\n'
b'\t [Job 13] Completed (_ds_correlation_fig2).\n'
b'191227-05:56:57,218 nipype.workflow INFO:\n'
b'\t [Job 14] Completed (_ds_correlation_matrix0).\n'
b'191227-05:56:57,219 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 3 jobs ready. Free memory (GB): 7.01/7.01, Free processors: 4/4.\n'
b'191227-05:56:57,258 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_fig".\n'
b'191227-05:56:57,259 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix1" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_matrix/mapflow/_ds_correlation_matrix1".\n'
b'191227-05:56:57,261 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix2" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_matrix/mapflow/_ds_correlation_matrix2".\n'
b'191227-05:56:57,262 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:57,262 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig0" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_fig/mapflow/_ds_correlation_fig0".\n'
b'191227-05:56:57,263 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:57,264 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:57,281 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix2".\n'
b'191227-05:56:57,281 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix1".\n'
b'191227-05:56:57,284 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig0".\n'
b'191227-05:56:57,285 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig1" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_fig/mapflow/_ds_correlation_fig1".\n'
b'191227-05:56:57,287 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:57,297 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig1".\n'
b'191227-05:56:57,299 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig2" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_fig/mapflow/_ds_correlation_fig2".\n'
b'191227-05:56:57,301 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:57,312 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig2".\n'
b'191227-05:56:57,314 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig".\n'
b'191227-05:56:59,218 nipype.workflow INFO:\n'
b'\t [Job 3] Completed (nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig).\n'
b'191227-05:56:59,220 nipype.workflow INFO:\n'
b'\t [Job 15] Completed (_ds_correlation_matrix1).\n'
b'191227-05:56:59,220 nipype.workflow INFO:\n'
b'\t [Job 16] Completed (_ds_correlation_matrix2).\n'
b'191227-05:56:59,222 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 1 jobs ready. Free memory (GB): 7.01/7.01, Free processors: 4/4.\n'
b'191227-05:56:59,264 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_matrix".\n'
b'191227-05:56:59,268 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix0" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_matrix/mapflow/_ds_correlation_matrix0".\n'
b'191227-05:56:59,270 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:59,278 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix0".\n'
b'191227-05:56:59,279 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix1" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_matrix/mapflow/_ds_correlation_matrix1".\n'
b'191227-05:56:59,281 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:59,288 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix1".\n'
b'191227-05:56:59,289 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix2" in "/tmp/tmpm8mit_gv/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d52fbffc20dfb329279857aaad08e9be07dd2b1a/ds_correlation_matrix/mapflow/_ds_correlation_matrix2".\n'
b'191227-05:56:59,291 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'191227-05:56:59,299 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix2".\n'
b'191227-05:56:59,301 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix".\n'
b'191227-05:57:01,221 nipype.workflow INFO:\n'
b'\t [Job 4] Completed (nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix).\n'
b'191227-05:57:01,222 nipype.workflow INFO:\n'
b'\t [MultiProc] Running 0 tasks, and 0 jobs ready. Free memory (GB): 7.01/7.01, Free processors: 4/4.\n'
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b'/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.4.1/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:85: 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/v0.4.1/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:85: 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/v0.4.1/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:85: 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/v0.4.1/lib/python3.7/site-packages/nibetaseries/data/references.bib --filter pandoc-citeproc --metadata pagetitle="NiBetaSeries citation boilerplate" /tmp/tmpm8mit_gv/ds000164/derivatives/nibetaseries/logs/CITATION.md -o /tmp/tmpm8mit_gv/ds000164/derivatives/nibetaseries/logs/CITATION.html\n'
Observe generated outputs¶
list_files(data_dir)
Out:
tmpm8mit_gv/
ds000164/
T1w.json
README
task-stroop_bold.json
dataset_description.json
task-stroop_events.json
CHANGES
derivatives/
nibetaseries/
sub-001/
func/
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-neutral_betaseries.nii.gz
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-congruent_correlation.tsv
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-neutral_correlation.tsv
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-incongruent_betaseries.nii.gz
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-incongruent_correlation.tsv
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.svg
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-incongruent_correlation.svg
logs/
CITATION.tex
CITATION.md
CITATION.bib
data/
Schaefer2018_100Parcels_7Networks_order.tsv
Schaefer2018_100Parcels_7Networks_order.txt
Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz
work/
NiBetaSeries_work/
nibetaseries_participant_wf/
graph1.json
d3.js
index.html
graph.json
single_subject001_wf/
correlation_wf/
d52fbffc20dfb329279857aaad08e9be07dd2b1a/
atlas_corr_node/
_node.pklz
result_atlas_corr_node.pklz
_inputs.pklz
_0xdc722c20aafd7540c69976531a5c0583.json
_report/
report.rst
mapflow/
_atlas_corr_node2/
_node.pklz
_inputs.pklz
desc-incongruent_correlation.tsv
desc-incongruent_correlation.svg
result__atlas_corr_node2.pklz
_0x845e408579345916304e10731dda00d3.json
_report/
report.rst
_atlas_corr_node0/
_node.pklz
_inputs.pklz
_0x15b14c6e38f799ebf9001d4c4f0790b5.json
desc-neutral_correlation.svg
result__atlas_corr_node0.pklz
desc-neutral_correlation.tsv
_report/
report.rst
_atlas_corr_node1/
_node.pklz
_inputs.pklz
desc-congruent_correlation.svg
desc-congruent_correlation.tsv
_0x1eb37634cc1215d3f9502337a0b0415f.json
result__atlas_corr_node1.pklz
_report/
report.rst
d52fbffc20dfb329279857aaad08e9be07dd2b1a/
ds_correlation_matrix/
_node.pklz
result_ds_correlation_matrix.pklz
_inputs.pklz
_0x9f3e51300e36f74e730de3b5b00e70b4.json
_report/
report.rst
mapflow/
_ds_correlation_matrix0/
_node.pklz
result__ds_correlation_matrix0.pklz
_inputs.pklz
_0x0c78fbc70b32a1c67f867f756e252582.json
_report/
report.rst
_ds_correlation_matrix1/
_node.pklz
_inputs.pklz
_0x507aac1c553a0d7a70062ff20d573927.json
result__ds_correlation_matrix1.pklz
_report/
report.rst
_ds_correlation_matrix2/
_0xe322ae421120b2de9a08a61623c5361a.json
_node.pklz
_inputs.pklz
result__ds_correlation_matrix2.pklz
_report/
report.rst
ds_correlation_fig/
_node.pklz
_inputs.pklz
_0x0276e16321946a906511afcfef30d69b.json
result_ds_correlation_fig.pklz
_report/
report.rst
mapflow/
_ds_correlation_fig0/
_node.pklz
_inputs.pklz
result__ds_correlation_fig0.pklz
_0x8698e461612a95bd75a73697b9deb4f3.json
_report/
report.rst
_ds_correlation_fig2/
_node.pklz
_0x9cef37255d1fc1c0ba2207620f1631fd.json
_inputs.pklz
result__ds_correlation_fig2.pklz
_report/
report.rst
_ds_correlation_fig1/
_node.pklz
_inputs.pklz
result__ds_correlation_fig1.pklz
_0x69f539975c9fe9cf96e2054644512b1c.json
_report/
report.rst
ds_betaseries_file/
_node.pklz
_0xdc5388f09d4be0fe6f328fbc74dd59d3.json
_inputs.pklz
result_ds_betaseries_file.pklz
_report/
report.rst
mapflow/
_ds_betaseries_file1/
_0x1bd5627596c2238171c9c2012999aa85.json
_node.pklz
_inputs.pklz
result__ds_betaseries_file1.pklz
_report/
report.rst
_ds_betaseries_file0/
_node.pklz
_0x493b3add4b10ba440cbbac650af956fd.json
_inputs.pklz
result__ds_betaseries_file0.pklz
_report/
report.rst
_ds_betaseries_file2/
_0x6c8afe2f8c85542bf82357af846f88ca.json
_node.pklz
_inputs.pklz
result__ds_betaseries_file2.pklz
_report/
report.rst
betaseries_wf/
d52fbffc20dfb329279857aaad08e9be07dd2b1a/
betaseries_node/
desc-neutral_betaseries.nii.gz
_node.pklz
_inputs.pklz
_0xebb22c93d24ec2be47295a382dce1f8e.json
result_betaseries_node.pklz
desc-congruent_betaseries.nii.gz
desc-incongruent_betaseries.nii.gz
_report/
report.rst
fmriprep/
dataset_description.json
sub-001/
func/
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-preproc_bold.nii.gz
sub-001_task-stroop_desc-confounds_regressors.tsv
sub-001_task-stroop_space-MNI152NLin2009cAsym_desc-brain_mask.nii.gz
sub-001/
anat/
sub-001_T1w.nii.gz
func/
sub-001_task-stroop_bold.nii.gz
sub-001_task-stroop_events.tsv
Collect results¶
corr_mat_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}_correlation.tsv')
pd_dict = {}
for trial_type in trial_types:
file_path = os.path.join(corr_mat_path, filename_template.format(trial_type=trial_type))
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 0.094015 ... 0.103498 0.017534
LH_Vis_2 0.094015 NaN ... -0.113993 -0.003621
LH_Vis_3 0.026549 0.214111 ... 0.199526 0.170387
LH_Vis_4 0.086884 -0.091846 ... -0.028499 -0.073733
LH_Vis_5 0.308264 0.380810 ... -0.249458 0.001683
[5 rows x 100 columns]
Graph the 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/v0.4.1/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 "
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: ( 2 minutes 31.938 seconds)
