.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_01_plotting_plot_surf_stat_map.py>` to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_01_plotting_plot_surf_stat_map.py:
Seed-based connectivity on the surface
=======================================
The dataset that is a subset of the enhanced NKI Rockland sample
(http://fcon_1000.projects.nitrc.org/indi/enhanced/, Nooner et al, 2012)
Resting state fMRI scans (TR=645ms) of 102 subjects were preprocessed
(https://github.com/fliem/nki_nilearn) and projected onto the Freesurfer
fsaverage5 template (Dale et al, 1999, Fischl et al, 1999). For this example
we use the time series of a single subject's left hemisphere.
The Destrieux parcellation (Destrieux et al, 2010) in fsaverage5 space as
distributed with Freesurfer is used to select a seed region in the posterior
cingulate cortex.
Functional connectivity of the seed region to all other cortical nodes in the
same hemisphere is calculated using Pearson product-moment correlation
coefficient.
The :func:`nilearn.plotting.plot_surf_stat_map` function is used
to plot the resulting statistical map on the (inflated) pial surface.
See also :ref:`for a similar example but using volumetric input data
<sphx_glr_auto_examples_03_connectivity_plot_seed_to_voxel_correlation.py>`.
See :ref:`plotting` for more details on plotting tools.
References
----------
Nooner et al, (2012). The NKI-Rockland Sample: A model for accelerating the
pace of discovery science in psychiatry. Frontiers in neuroscience 6, 152.
URL http://dx.doi.org/10.3389/fnins.2012.00152
Dale et al, (1999). Cortical surface-based analysis.I. Segmentation and
surface reconstruction. Neuroimage 9.
URL http://dx.doi.org/10.1006/nimg.1998.0395
Fischl et al, (1999). Cortical surface-based analysis. II: Inflation,
flattening, and a surface-based coordinate system. Neuroimage 9.
http://dx.doi.org/10.1006/nimg.1998.0396
Destrieux et al, (2010). Automatic parcellation of human cortical gyri and
sulci using standard anatomical nomenclature. NeuroImage, 53, 1.
URL http://dx.doi.org/10.1016/j.neuroimage.2010.06.010.
Retrieving the data
-------------------
.. code-block:: default
# NKI resting state data from nilearn
from nilearn import datasets
nki_dataset = datasets.fetch_surf_nki_enhanced(n_subjects=1)
# The nki dictionary contains file names for the data
# of all downloaded subjects.
print(('Resting state data of the first subjects on the '
'fsaverag5 surface left hemisphere is at: %s' %
nki_dataset['func_left'][0]))
# Destrieux parcellation for left hemisphere in fsaverage5 space
destrieux_atlas = datasets.fetch_atlas_surf_destrieux()
parcellation = destrieux_atlas['map_left']
labels = destrieux_atlas['labels']
# Fsaverage5 surface template
fsaverage = datasets.fetch_surf_fsaverage()
# The fsaverage dataset contains file names pointing to
# the file locations
print('Fsaverage5 pial surface of left hemisphere is at: %s' %
fsaverage['pial_left'])
print('Fsaverage5 inflated surface of left hemisphere is at: %s' %
fsaverage['infl_left'])
print('Fsaverage5 sulcal depth map of left hemisphere is at: %s' %
fsaverage['sulc_left'])
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Resting state data of the first subjects on the fsaverag5 surface left hemisphere is at: /home/nicolas/nilearn_data/nki_enhanced_surface/A00028185/A00028185_left_preprocessed_fwhm6.gii
Fsaverage5 pial surface of left hemisphere is at: /home/nicolas/GitRepos/nilearn-fork/nilearn/datasets/data/fsaverage5/pial_left.gii.gz
Fsaverage5 inflated surface of left hemisphere is at: /home/nicolas/GitRepos/nilearn-fork/nilearn/datasets/data/fsaverage5/infl_left.gii.gz
Fsaverage5 sulcal depth map of left hemisphere is at: /home/nicolas/GitRepos/nilearn-fork/nilearn/datasets/data/fsaverage5/sulc_left.gii.gz
Extracting the seed time series
--------------------------------
.. code-block:: default
# Load resting state time series from nilearn
from nilearn import surface
timeseries = surface.load_surf_data(nki_dataset['func_left'][0])
# Extract seed region via label
pcc_region = b'G_cingul-Post-dorsal'
import numpy as np
pcc_labels = np.where(parcellation == labels.index(pcc_region))[0]
# Extract time series from seed region
seed_timeseries = np.mean(timeseries[pcc_labels], axis=0)
Calculating seed-based functional connectivity
----------------------------------------------
.. code-block:: default
# Calculate Pearson product-moment correlation coefficient between seed
# time series and timeseries of all cortical nodes of the hemisphere
from scipy import stats
stat_map = np.zeros(timeseries.shape[0])
for i in range(timeseries.shape[0]):
stat_map[i] = stats.pearsonr(seed_timeseries, timeseries[i])[0]
# Re-mask previously masked nodes (medial wall)
stat_map[np.where(np.mean(timeseries, axis=1) == 0)] = 0
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
/home/nicolas/anaconda3/envs/nilearn/lib/python3.8/site-packages/scipy/stats/stats.py:3913: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.
warnings.warn(PearsonRConstantInputWarning())
Display ROI on surface
.. code-block:: default
# Transform ROI indices in ROI map
pcc_map = np.zeros(parcellation.shape[0], dtype=int)
pcc_map[pcc_labels] = 1
from nilearn import plotting
plotting.plot_surf_roi(fsaverage['pial_left'], roi_map=pcc_map,
hemi='left', view='medial',
bg_map=fsaverage['sulc_left'], bg_on_data=True,
title='PCC Seed')
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_surf_stat_map_001.png
:alt: PCC Seed
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<Figure size 400x400 with 1 Axes>
Display unthresholded stat map with a slightly dimmed background
.. code-block:: default
plotting.plot_surf_stat_map(fsaverage['pial_left'], stat_map=stat_map,
hemi='left', view='medial', colorbar=True,
bg_map=fsaverage['sulc_left'], bg_on_data=True,
darkness=.3, title='Correlation map')
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_surf_stat_map_002.png
:alt: Correlation map
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<Figure size 470x400 with 2 Axes>
Many different options are available for plotting, for example thresholding,
or using custom colormaps
.. code-block:: default
plotting.plot_surf_stat_map(fsaverage['pial_left'], stat_map=stat_map,
hemi='left', view='medial', colorbar=True,
bg_map=fsaverage['sulc_left'], bg_on_data=True,
cmap='Spectral', threshold=.5,
title='Threshold and colormap')
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_surf_stat_map_003.png
:alt: Threshold and colormap
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<Figure size 470x400 with 2 Axes>
Here the surface is plotted in a lateral view without a background map.
To capture 3D structure without depth information, the default is to plot a
half transparent surface.
Note that you can also control the transparency with a background map using
the alpha parameter.
.. code-block:: default
plotting.plot_surf_stat_map(fsaverage['pial_left'], stat_map=stat_map,
hemi='left', view='lateral', colorbar=True,
cmap='Spectral', threshold=.5,
title='Plotting without background')
.. image:: /auto_examples/01_plotting/images/sphx_glr_plot_surf_stat_map_004.png
:alt: Plotting without background
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
<Figure size 470x400 with 2 Axes>
The plots can be saved to file, in which case the display is closed after
creating the figure
.. code-block:: default
plotting.plot_surf_stat_map(fsaverage['infl_left'], stat_map=stat_map,
hemi='left', bg_map=fsaverage['sulc_left'],
bg_on_data=True, threshold=.5, colorbar=True,
output_file='plot_surf_stat_map.png')
plotting.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 7.386 seconds)
.. _sphx_glr_download_auto_examples_01_plotting_plot_surf_stat_map.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/01_plotting/plot_surf_stat_map.ipynb
:alt: Launch binder
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_surf_stat_map.py <plot_surf_stat_map.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_surf_stat_map.ipynb <plot_surf_stat_map.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_