9.2.16. 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 nilearn.plotting.plot_surf_stat_map function is used to plot the resulting statistical map on the (inflated) pial surface.

See also for a similar example but using volumetric input data.

See Plotting brain images for more details on plotting tools.

9.2.16.1. 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.

9.2.16.2. Retrieving the data

# 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'])

Out:

Dataset created in /home/varoquau/nilearn_data/nki_enhanced_surface

Downloading data from https://www.nitrc.org/frs/download.php/8470/pheno_nki_nilearn.csv ...
 ...done. (1 seconds, 0 min)
Downloading data from https://www.nitrc.org/frs/download.php/8261/A00028185_rh_preprocessed_fsaverage5_fwhm6.gii ...

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Resting state data of the first subjects on the fsaverag5 surface left hemisphere is at: /home/varoquau/nilearn_data/nki_enhanced_surface/A00028185/A00028185_left_preprocessed_fwhm6.gii
Fsaverage5 pial surface of left hemisphere is at: /home/varoquau/dev/nilearn/nilearn/datasets/data/fsaverage5/pial.left.gii.gz
Fsaverage5 inflated surface of left hemisphere is at: /home/varoquau/dev/nilearn/nilearn/datasets/data/fsaverage5/pial_inflated.left.gii.gz
Fsaverage5 sulcal depth map of left hemisphere is at: /home/varoquau/dev/nilearn/nilearn/datasets/data/fsaverage5/sulc.left.gii.gz

9.2.16.3. Extracting the seed time series

# 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)

9.2.16.4. Calculating seed-based functional connectivity

# 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

Out:

/usr/lib/python3/dist-packages/scipy/stats/stats.py:3399: PearsonRConstantInputWarning: An input array is constant; the correlation coefficent is not defined.
  warnings.warn(PearsonRConstantInputWarning())

Display ROI on surface

# 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')

Out:

<Figure size 640x480 with 1 Axes>

Display unthresholded stat map with a slightly dimmed background

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')

Out:

<Figure size 640x480 with 2 Axes>

Many different options are available for plotting, for example thresholding, or using custom colormaps

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')

Out:

<Figure size 640x480 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.

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')

Out:

<Figure size 640x480 with 2 Axes>

The plots can be saved to file, in which case the display is closed after creating the figure

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()