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/qoqbiya1ou7vi78/ds000164-test_v1.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/tmp4dilf97d

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:

tmp4dilf97d/
    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/
                sub-001/
                    func/
                        sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_brainmask.nii.gz
                        sub-001_task-stroop_bold_confounds.tsv
                        sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc.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 coordinate 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 \
-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 -w /tmp/tmp4dilf97d/ds000164/derivatives/work -a /tmp/tmp4dilf97d/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order_FSLMNI152_2mm.nii.gz -l /tmp/tmp4dilf97d/ds000164/derivatives/data/Schaefer2018_100Parcels_7Networks_order.tsv /tmp/tmp4dilf97d/ds000164 fmriprep /tmp/tmp4dilf97d/ds000164/derivatives participant

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b'190905-21:16:46,49 nipype.workflow INFO:\n'
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b'\t [Node] Running "_atlas_corr_node0" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'190905-21:16:48,538 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node2" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/atlas_corr_node/mapflow/_atlas_corr_node2".\n'
b'190905-21:16:48,541 nipype.workflow INFO:\n'
b'\t [Node] Running "_atlas_corr_node2" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'190905-21:16:48,545 nipype.workflow INFO:\n'
b'\t [Node] Running "_atlas_corr_node1" ("nibetaseries.interfaces.nilearn.AtlasConnectivity")\n'
b'190905-21:16:50,513 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/tmp4dilf97d/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/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/betaseries_node/betaseries_trialtyp\n'
b'[NiftiLabelsMasker.transform_single_imgs] Extracting region signals\n'
b'[NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals\n'
b'190905-21:17:01,808 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node1".\n'
b'[NiftiLabelsMasker.fit_transform] loading data from /tmp/tmp4dilf97d/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/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/betaseries_node/betaseries_trialtyp\n'
b'[NiftiLabelsMasker.transform_single_imgs] Extracting region signals\n'
b'[NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals\n'
b'190905-21:17:02,67 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node2".\n'
b'[NiftiLabelsMasker.fit_transform] loading data from /tmp/tmp4dilf97d/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/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/betaseries_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/betaseries_node/betaseries_trialtyp\n'
b'[NiftiLabelsMasker.transform_single_imgs] Extracting region signals\n'
b'[NiftiLabelsMasker.transform_single_imgs] Cleaning extracted signals\n'
b'190905-21:17:02,300 nipype.workflow INFO:\n'
b'\t [Node] Finished "_atlas_corr_node0".\n'
b'190905-21:17:02,526 nipype.workflow INFO:\n'
b'\t [Job 5] Completed (_atlas_corr_node0).\n'
b'190905-21:17:02,527 nipype.workflow INFO:\n'
b'\t [Job 6] Completed (_atlas_corr_node1).\n'
b'190905-21:17:02,528 nipype.workflow INFO:\n'
b'\t [Job 7] Completed (_atlas_corr_node2).\n'
b'190905-21:17:02,529 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'190905-21:17:02,553 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/atlas_corr_node".\n'
b'190905-21:17:02,556 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node0" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/atlas_corr_node/mapflow/_atlas_corr_node0".\n'
b'190905-21:17:02,557 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node0" - collecting precomputed outputs\n'
b'190905-21:17:02,558 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node0" found cached.\n'
b'190905-21:17:02,558 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node1" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/atlas_corr_node/mapflow/_atlas_corr_node1".\n'
b'190905-21:17:02,560 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node1" - collecting precomputed outputs\n'
b'190905-21:17:02,560 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node1" found cached.\n'
b'190905-21:17:02,561 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_atlas_corr_node2" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/atlas_corr_node/mapflow/_atlas_corr_node2".\n'
b'190905-21:17:02,561 nipype.workflow INFO:\n'
b'\t [Node] Cached "_atlas_corr_node2" - collecting precomputed outputs\n'
b'190905-21:17:02,562 nipype.workflow INFO:\n'
b'\t [Node] "_atlas_corr_node2" found cached.\n'
b'190905-21:17:02,564 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node".\n'
b'190905-21:17:04,529 nipype.workflow INFO:\n'
b'\t [Job 1] Completed (nibetaseries_participant_wf.single_subject001_wf.correlation_wf.atlas_corr_node).\n'
b'190905-21:17:04,531 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'190905-21:17:06,531 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'190905-21:17:06,560 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig0" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_fig/mapflow/_ds_correlation_fig0".\n'
b'190905-21:17:06,562 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:06,562 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig1" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_fig/mapflow/_ds_correlation_fig1".\n'
b'190905-21:17:06,565 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:06,568 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig0".\n'
b'190905-21:17:06,572 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig1".\n'
b'190905-21:17:06,573 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig2" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_fig/mapflow/_ds_correlation_fig2".\n'
b'190905-21:17:06,575 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:06,581 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig2".\n'
b'190905-21:17:06,584 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_rename_matrix_node0" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/rename_matrix_node/mapflow/_rename_matrix_node0".\n'
b'190905-21:17:06,586 nipype.workflow INFO:\n'
b'\t [Node] Running "_rename_matrix_node0" ("nipype.interfaces.utility.wrappers.Function")\n'
b'190905-21:17:06,590 nipype.workflow INFO:\n'
b'\t [Node] Finished "_rename_matrix_node0".\n'
b'190905-21:17:08,533 nipype.workflow INFO:\n'
b'\t [Job 8] Completed (_ds_correlation_fig0).\n'
b'190905-21:17:08,533 nipype.workflow INFO:\n'
b'\t [Job 9] Completed (_ds_correlation_fig1).\n'
b'190905-21:17:08,534 nipype.workflow INFO:\n'
b'\t [Job 10] Completed (_ds_correlation_fig2).\n'
b'190905-21:17:08,535 nipype.workflow INFO:\n'
b'\t [Job 11] Completed (_rename_matrix_node0).\n'
b'190905-21:17:08,536 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'190905-21:17:08,565 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_fig".\n'
b'190905-21:17:08,567 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_rename_matrix_node1" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/rename_matrix_node/mapflow/_rename_matrix_node1".\n'
b'190905-21:17:08,568 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_rename_matrix_node2" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/rename_matrix_node/mapflow/_rename_matrix_node2".\n'
b'190905-21:17:08,569 nipype.workflow INFO:\n'
b'\t [Node] Running "_rename_matrix_node1" ("nipype.interfaces.utility.wrappers.Function")\n'
b'190905-21:17:08,570 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig0" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_fig/mapflow/_ds_correlation_fig0".\n'
b'190905-21:17:08,570 nipype.workflow INFO:\n'
b'\t [Node] Running "_rename_matrix_node2" ("nipype.interfaces.utility.wrappers.Function")\n'
b'190905-21:17:08,572 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:08,574 nipype.workflow INFO:\n'
b'\t [Node] Finished "_rename_matrix_node1".\n'
b'190905-21:17:08,575 nipype.workflow INFO:\n'
b'\t [Node] Finished "_rename_matrix_node2".\n'
b'190905-21:17:08,580 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig0".\n'
b'190905-21:17:08,581 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig1" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_fig/mapflow/_ds_correlation_fig1".\n'
b'190905-21:17:08,584 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:08,591 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig1".\n'
b'190905-21:17:08,592 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_fig2" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_fig/mapflow/_ds_correlation_fig2".\n'
b'190905-21:17:08,594 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_fig2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:08,601 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_fig2".\n'
b'190905-21:17:08,603 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig".\n'
b'190905-21:17:10,533 nipype.workflow INFO:\n'
b'\t [Job 2] Completed (nibetaseries_participant_wf.single_subject001_wf.ds_correlation_fig).\n'
b'190905-21:17:10,535 nipype.workflow INFO:\n'
b'\t [Job 12] Completed (_rename_matrix_node1).\n'
b'190905-21:17:10,536 nipype.workflow INFO:\n'
b'\t [Job 13] Completed (_rename_matrix_node2).\n'
b'190905-21:17:10,538 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'190905-21:17:10,565 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.correlation_wf.rename_matrix_node" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/rename_matrix_node".\n'
b'190905-21:17:10,568 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_rename_matrix_node0" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/rename_matrix_node/mapflow/_rename_matrix_node0".\n'
b'190905-21:17:10,569 nipype.workflow INFO:\n'
b'\t [Node] Cached "_rename_matrix_node0" - collecting precomputed outputs\n'
b'190905-21:17:10,570 nipype.workflow INFO:\n'
b'\t [Node] "_rename_matrix_node0" found cached.\n'
b'190905-21:17:10,571 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_rename_matrix_node1" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/rename_matrix_node/mapflow/_rename_matrix_node1".\n'
b'190905-21:17:10,572 nipype.workflow INFO:\n'
b'\t [Node] Cached "_rename_matrix_node1" - collecting precomputed outputs\n'
b'190905-21:17:10,572 nipype.workflow INFO:\n'
b'\t [Node] "_rename_matrix_node1" found cached.\n'
b'190905-21:17:10,573 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_rename_matrix_node2" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/correlation_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/rename_matrix_node/mapflow/_rename_matrix_node2".\n'
b'190905-21:17:10,574 nipype.workflow INFO:\n'
b'\t [Node] Cached "_rename_matrix_node2" - collecting precomputed outputs\n'
b'190905-21:17:10,574 nipype.workflow INFO:\n'
b'\t [Node] "_rename_matrix_node2" found cached.\n'
b'190905-21:17:10,576 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.correlation_wf.rename_matrix_node".\n'
b'190905-21:17:12,534 nipype.workflow INFO:\n'
b'\t [Job 3] Completed (nibetaseries_participant_wf.single_subject001_wf.correlation_wf.rename_matrix_node).\n'
b'190905-21:17:12,536 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'190905-21:17:14,536 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'190905-21:17:14,563 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix0" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_matrix/mapflow/_ds_correlation_matrix0".\n'
b'190905-21:17:14,565 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix1" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_matrix/mapflow/_ds_correlation_matrix1".\n'
b'190905-21:17:14,566 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:14,566 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:14,570 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix1".\n'
b'190905-21:17:14,570 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix0".\n'
b'190905-21:17:14,571 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix2" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_matrix/mapflow/_ds_correlation_matrix2".\n'
b'190905-21:17:14,573 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:14,577 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix2".\n'
b'190905-21:17:16,537 nipype.workflow INFO:\n'
b'\t [Job 14] Completed (_ds_correlation_matrix0).\n'
b'190905-21:17:16,538 nipype.workflow INFO:\n'
b'\t [Job 15] Completed (_ds_correlation_matrix1).\n'
b'190905-21:17:16,539 nipype.workflow INFO:\n'
b'\t [Job 16] Completed (_ds_correlation_matrix2).\n'
b'190905-21:17:16,540 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'190905-21:17:16,565 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_matrix".\n'
b'190905-21:17:16,569 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix0" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_matrix/mapflow/_ds_correlation_matrix0".\n'
b'190905-21:17:16,572 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix0" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:16,576 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix0".\n'
b'190905-21:17:16,577 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix1" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_matrix/mapflow/_ds_correlation_matrix1".\n'
b'190905-21:17:16,580 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix1" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:16,583 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix1".\n'
b'190905-21:17:16,584 nipype.workflow INFO:\n'
b'\t [Node] Setting-up "_ds_correlation_matrix2" in "/tmp/tmp4dilf97d/ds000164/derivatives/work/NiBetaSeries_work/nibetaseries_participant_wf/single_subject001_wf/d1f1604a1450487e43daa4f7cb17b12d839462dd/ds_correlation_matrix/mapflow/_ds_correlation_matrix2".\n'
b'190905-21:17:16,587 nipype.workflow INFO:\n'
b'\t [Node] Running "_ds_correlation_matrix2" ("nibetaseries.interfaces.bids.DerivativesDataSink")\n'
b'190905-21:17:16,590 nipype.workflow INFO:\n'
b'\t [Node] Finished "_ds_correlation_matrix2".\n'
b'190905-21:17:16,592 nipype.workflow INFO:\n'
b'\t [Node] Finished "nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix".\n'
b'190905-21:17:18,540 nipype.workflow INFO:\n'
b'\t [Job 4] Completed (nibetaseries_participant_wf.single_subject001_wf.ds_correlation_matrix).\n'
b'190905-21:17:18,542 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'
b'/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.3.2/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:83: 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/v0.3.2/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:83: RuntimeWarning: invalid value encountered in greater\n'
b'  n_lines = int(np.sum(connmat > 0) / 2)\n'
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b'\n'
b'/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.3.2/lib/python3.7/site-packages/nibetaseries/interfaces/nilearn.py:83: RuntimeWarning: invalid value encountered in greater\n'
b'  n_lines = int(np.sum(connmat > 0) / 2)\n'

Observe generated outputs

list_files(data_dir)

Out:

tmp4dilf97d/
    ds000164/
        T1w.json
        README
        task-stroop_bold.json
        dataset_description.json
        task-stroop_events.json
        CHANGES
        derivatives/
            NiBetaSeries/
                nibetaseries/
                    sub-001/
                        func/
                            sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc_trialtype-congruent_matrix.tsv
                            sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc_trialtype-congruent_fig.svg
                            sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc_trialtype-neutral_fig.svg
                            sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc_trialtype-incongruent_fig.svg
                            sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc_trialtype-incongruent_matrix.tsv
                            sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc_trialtype-neutral_matrix.tsv
                logs/
            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/
                            d1f1604a1450487e43daa4f7cb17b12d839462dd/
                                ds_correlation_matrix/
                                    _node.pklz
                                    result_ds_correlation_matrix.pklz
                                    _inputs.pklz
                                    _0x802e65ed88bd53194cafb6c913025f26.json
                                    _report/
                                        report.rst
                                    mapflow/
                                        _ds_correlation_matrix0/
                                            _node.pklz
                                            _0x3a592e52a7bb4e4f430ce6adb4cc69d1.json
                                            result__ds_correlation_matrix0.pklz
                                            _inputs.pklz
                                            _report/
                                                report.rst
                                        _ds_correlation_matrix1/
                                            _node.pklz
                                            _inputs.pklz
                                            _0x7a00dffa204a30575fe9d6a9efb7edf1.json
                                            result__ds_correlation_matrix1.pklz
                                            _report/
                                                report.rst
                                        _ds_correlation_matrix2/
                                            _node.pklz
                                            _inputs.pklz
                                            _0x739f516a87fec521d05d02a2ec4e147e.json
                                            result__ds_correlation_matrix2.pklz
                                            _report/
                                                report.rst
                                ds_correlation_fig/
                                    _node.pklz
                                    _inputs.pklz
                                    result_ds_correlation_fig.pklz
                                    _0x47d3b3554f8c346db6c6b01a81a7cc87.json
                                    _report/
                                        report.rst
                                    mapflow/
                                        _ds_correlation_fig0/
                                            _0x9d867bac3047c6fb3967ce9faf2d92db.json
                                            _node.pklz
                                            _inputs.pklz
                                            result__ds_correlation_fig0.pklz
                                            _report/
                                                report.rst
                                        _ds_correlation_fig2/
                                            _node.pklz
                                            _inputs.pklz
                                            result__ds_correlation_fig2.pklz
                                            _0x35dfcb28f6e6c034bc18b18947812361.json
                                            _report/
                                                report.rst
                                        _ds_correlation_fig1/
                                            _node.pklz
                                            _0x3dd83d04568af1e50cac891a236b88fe.json
                                            _inputs.pklz
                                            result__ds_correlation_fig1.pklz
                                            _report/
                                                report.rst
                            correlation_wf/
                                d1f1604a1450487e43daa4f7cb17b12d839462dd/
                                    atlas_corr_node/
                                        _node.pklz
                                        result_atlas_corr_node.pklz
                                        _inputs.pklz
                                        _0x659f01ae2cdba76638dbef368ea77216.json
                                        _report/
                                            report.rst
                                        mapflow/
                                            _atlas_corr_node2/
                                                fisher_z_correlation.tsv
                                                _node.pklz
                                                _inputs.pklz
                                                result__atlas_corr_node2.pklz
                                                incongruent.svg
                                                _0xf91b38727ba2c6c31230bb8e31cbf674.json
                                                _report/
                                                    report.rst
                                            _atlas_corr_node0/
                                                fisher_z_correlation.tsv
                                                _node.pklz
                                                _inputs.pklz
                                                neutral.svg
                                                result__atlas_corr_node0.pklz
                                                _0x99aa6049041623e06087cf34d76257a1.json
                                                _report/
                                                    report.rst
                                            _atlas_corr_node1/
                                                fisher_z_correlation.tsv
                                                _node.pklz
                                                _inputs.pklz
                                                _0xb1a60152d93466e5c825bf33c73f32de.json
                                                result__atlas_corr_node1.pklz
                                                congruent.svg
                                                _report/
                                                    report.rst
                                    rename_matrix_node/
                                        _node.pklz
                                        result_rename_matrix_node.pklz
                                        _inputs.pklz
                                        _0x04f2117def5e033f96ecb8a011d746ae.json
                                        _report/
                                            report.rst
                                        mapflow/
                                            _rename_matrix_node1/
                                                _0xca8fb433043e58b48daacac40d010b5a.json
                                                _node.pklz
                                                _inputs.pklz
                                                result__rename_matrix_node1.pklz
                                                correlation-matrix_trialtype-congruent.tsv
                                                _report/
                                                    report.rst
                                            _rename_matrix_node2/
                                                _node.pklz
                                                _inputs.pklz
                                                correlation-matrix_trialtype-incongruent.tsv
                                                result__rename_matrix_node2.pklz
                                                _0x0c363bf60368852649316506b27dcd18.json
                                                _report/
                                                    report.rst
                                            _rename_matrix_node0/
                                                _node.pklz
                                                _inputs.pklz
                                                result__rename_matrix_node0.pklz
                                                _0xd0d88b9072061d919134c0e5e990af47.json
                                                correlation-matrix_trialtype-neutral.tsv
                                                _report/
                                                    report.rst
                            betaseries_wf/
                                d1f1604a1450487e43daa4f7cb17b12d839462dd/
                                    betaseries_node/
                                        _0x85373ba7c1b34de8a2deb0efc49969d2.json
                                        _node.pklz
                                        _inputs.pklz
                                        betaseries_trialtype-neutral.nii.gz
                                        betaseries_trialtype-congruent.nii.gz
                                        betaseries_trialtype-incongruent.nii.gz
                                        result_betaseries_node.pklz
                                        _report/
                                            report.rst
            fmriprep/
                sub-001/
                    func/
                        sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_brainmask.nii.gz
                        sub-001_task-stroop_bold_confounds.tsv
                        sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc.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", "nibetaseries", "sub-001", "func")
trial_types = ['congruent', 'incongruent', 'neutral']
filename_template = "sub-001_task-stroop_bold_space-MNI152NLin2009cAsym_preproc_trialtype-{trial_type}_matrix.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  0.092135  ...          0.095624          0.016799
LH_Vis_2  0.092135       NaN  ...         -0.119613         -0.007679
LH_Vis_3 -0.003990  0.216346  ...          0.202673          0.177828
LH_Vis_4  0.075498 -0.088788  ...         -0.019256         -0.034034
LH_Vis_5  0.314494  0.354525  ...         -0.235334          0.032317

[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()
../_images/sphx_glr_plot_run_nibetaseries_001.png

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nibetaseries/envs/v0.3.2/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 21.816 seconds)

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