Seed-based connectivity on the surface

The dataset that is a subset of the enhanced NKI Rockland sample (https://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.

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 https://doi.org/10.3389/fnins.2012.00152

Dale et al, (1999). Cortical surface-based analysis.I. Segmentation and surface reconstruction. Neuroimage 9. URL https://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. https://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 https://doi.org/10.1016/j.neuroimage.2010.06.010.

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 '
      f"fsaverag5 surface left hemisphere is at: {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: '
      f"{fsaverage['pial_left']}")
print('Fsaverage5 flatten pial surface of left hemisphere is at: '
      f"{fsaverage['flat_left']}")
print('Fsaverage5 inflated surface of left hemisphere is at: '
      f"{fsaverage['infl_left']}")
print('Fsaverage5 sulcal depth map of left hemisphere is at: '
      f"{fsaverage['sulc_left']}")
print('Fsaverage5 curvature map of left hemisphere is at: '
      f"{fsaverage['curv_left']}")

Resting state data of the first subjects on the fsaverag5 surface left hemisphere is at: /home/remi/nilearn_data/nki_enhanced_surface/A00028185/A00028185_left_preprocessed_fwhm6.gii
Fsaverage5 pial surface of left hemisphere is at: /home/remi/github/nilearn/nilearn/env/lib/python3.11/site-packages/nilearn/datasets/data/fsaverage5/pial_left.gii.gz
Fsaverage5 flatten pial surface of left hemisphere is at: /home/remi/github/nilearn/nilearn/env/lib/python3.11/site-packages/nilearn/datasets/data/fsaverage5/flat_left.gii.gz
Fsaverage5 inflated surface of left hemisphere is at: /home/remi/github/nilearn/nilearn/env/lib/python3.11/site-packages/nilearn/datasets/data/fsaverage5/infl_left.gii.gz
Fsaverage5 sulcal depth map of left hemisphere is at: /home/remi/github/nilearn/nilearn/env/lib/python3.11/site-packages/nilearn/datasets/data/fsaverage5/sulc_left.gii.gz
Fsaverage5 curvature map of left hemisphere is at: /home/remi/github/nilearn/nilearn/env/lib/python3.11/site-packages/nilearn/datasets/data/fsaverage5/curv_left.gii.gz

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)

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

/home/remi/github/nilearn/nilearn/examples/01_plotting/plot_surf_stat_map.py:114: ConstantInputWarning:

An input array is constant; the correlation coefficient is not defined.

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'
)

<Figure size 400x500 with 1 Axes>

Using a flat mesh can be useful in order to easily locate the area of interest on the cortex. To make this plot easier to read, we use the mesh curvature as a background map.

bg_map = np.sign(surface.load_surf_data(fsaverage['curv_left']))
# np.sign yields values in [-1, 1]. We rescale the background map
# such that values are in [0.25, 0.75], resulting in a nicer looking plot.
bg_map_rescaled = (bg_map + 1) / 4 + 0.25

plotting.plot_surf_roi(
    fsaverage['flat_left'],
    roi_map=pcc_map,
    hemi='left',
    view='dorsal',
    bg_map=bg_map_rescaled,
    bg_on_data=True,
    title='PCC Seed'
)

<Figure size 400x500 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')

<Figure size 470x500 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')

<Figure size 470x500 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')

<Figure size 470x500 with 2 Axes>

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

from pathlib import Path

output_dir = Path.cwd() / "results" / "plot_surf_stat_map"
output_dir.mkdir(exist_ok=True, parents=True)
print(f"Output will be saved to: {output_dir}")

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=output_dir / 'plot_surf_stat_map.png')

plotting.show()

Output will be saved to: /home/remi/github/nilearn/nilearn/examples/01_plotting/results/plot_surf_stat_map

Estimated memory usage: 277 MB