Note
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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.[1])
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.[2] and Fischl et al.[3]). For this example we use the time series of a single subject’s left hemisphere.
The Destrieux parcellation (Destrieux et al.[4]) 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.
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']}"
)
[get_dataset_dir] Dataset found in
/home/runner/nilearn_data/nki_enhanced_surface
Resting state data of the first subjects on the fsaverag5 surface left hemisphere is at: /home/runner/nilearn_data/nki_enhanced_surface/A00028185/A00028185_left_preprocessed_fwhm6.gii
[get_dataset_dir] Dataset found in /home/runner/nilearn_data/destrieux_surface
Fsaverage5 pial surface of left hemisphere is at: /home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/datasets/data/fsaverage5/pial_left.gii.gz
Fsaverage5 flatten pial surface of left hemisphere is at: /home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/datasets/data/fsaverage5/flat_left.gii.gz
Fsaverage5 inflated surface of left hemisphere is at: /home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/datasets/data/fsaverage5/infl_left.gii.gz
Fsaverage5 sulcal depth map of left hemisphere is at: /home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/nilearn/datasets/data/fsaverage5/sulc_left.gii.gz
Fsaverage5 curvature map of left hemisphere is at: /home/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/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])
# Coercing to float is required to avoid errors withj scipy >= 0.14.0
timeseries = timeseries.astype(float)
# 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/runner/work/nilearn/nilearn/.tox/doc/lib/python3.9/site-packages/scipy/stats/_stats_py.py:4068: PearsonRConstantInputWarning:
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
<Figure size 470x500 with 2 Axes>
Many different options are available for plotting, for example thresholding, or using custom colormaps
<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=0.5,
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=0.5,
colorbar=True,
output_file=output_dir / "plot_surf_stat_map.png",
)
plotting.show()
Output will be saved to: /home/runner/work/nilearn/nilearn/examples/01_plotting/results/plot_surf_stat_map
References¶
Total running time of the script: (0 minutes 7.700 seconds)
Estimated memory usage: 505 MB