Independent component analysis of resting-state fMRI

An example applying ICA to resting-state data.

Python source code: plot_ica_resting_state.py

### Load nyu_rest dataset #####################################################
from nilearn import datasets
# Here we use only 3 subjects to get faster-running code. For better
# results, simply increase this number
# XXX: must get the code to run for more than 1 subject
nyu_dataset = datasets.fetch_nyu_rest(n_subjects=1)
func_filename = nyu_dataset.func[0]

# print basic information on the dataset
print('First subject anatomical nifti image (3D) is at: %s' %
      nyu_dataset.anat_anon[0])
print('First subject functional nifti image (4D) is at: %s' %
      nyu_dataset.func[0])  # 4D data

### Preprocess ################################################################
from nilearn.input_data import NiftiMasker

# This is resting-state data: the background has not been removed yet,
# thus we need to use mask_strategy='epi' to compute the mask from the
# EPI images
masker = NiftiMasker(smoothing_fwhm=8, memory='nilearn_cache', memory_level=1,
                     mask_strategy='epi', standardize=False)
data_masked = masker.fit_transform(func_filename)

# Concatenate all the subjects
# fmri_data = np.concatenate(data_masked, axis=1)
fmri_data = data_masked


### Apply ICA #################################################################

from sklearn.decomposition import FastICA
n_components = 20
ica = FastICA(n_components=n_components, random_state=42)
components_masked = ica.fit_transform(data_masked.T).T

# Normalize estimated components, for thresholding to make sense
components_masked -= components_masked.mean(axis=0)
components_masked /= components_masked.std(axis=0)
# Threshold
components_masked[components_masked < .8] = 0

# Now invert the masking operation, going back to a full 3D
# representation
component_img = masker.inverse_transform(components_masked)

### Visualize the results #####################################################
# Show some interesting components
import matplotlib.pyplot as plt
from nilearn import image
from nilearn.plotting import plot_stat_map

# Use the mean as a background
mean_img = image.mean_img(func_filename)

plot_stat_map(image.index_img(component_img, 5), mean_img)

plot_stat_map(image.index_img(component_img, 12), mean_img)

plt.show()

Total running time of the example: 0.00 seconds ( 0 minutes 0.00 seconds)