Simple example of NiftiMasker use

Here is a simple example of automatic mask computation using the nifti masker. The mask is computed and visualized.

Python source code: plot_nifti_simple.py

### Load nyu_rest dataset #####################################################

from nilearn import datasets
from nilearn.input_data import NiftiMasker
nyu_dataset = datasets.fetch_nyu_rest(n_subjects=1)

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

### Compute the mask ##########################################################

# As this is raw resting-state EPI, the background is noisy and we cannot
# rely on the 'background' masking strategy. We need to use the 'epi' one
nifti_masker = NiftiMasker(standardize=False, mask_strategy='epi',
                           memory="nilearn_cache", memory_level=2)
func_filename = nyu_dataset.func[0]
nifti_masker.fit(func_filename)
mask_img = nifti_masker.mask_img_

### Visualize the mask ########################################################
import matplotlib.pyplot as plt
from nilearn.plotting import plot_roi
from nilearn.image.image import mean_img

# calculate mean image for the background
mean_func_img = mean_img(func_filename)

plot_roi(mask_img, mean_func_img, display_mode='y', cut_coords=4, title="Mask")


### Preprocess data ###########################################################
nifti_masker.fit(func_filename)
fmri_masked = nifti_masker.transform(func_filename)

### Run an algorithm ##########################################################
from sklearn.decomposition import FastICA
n_components = 20
ica = FastICA(n_components=n_components, random_state=42)
components_masked = ica.fit_transform(fmri_masked.T).T

### Reverse masking ###########################################################
components = nifti_masker.inverse_transform(components_masked)

### Show results ##############################################################
from nilearn.plotting import plot_stat_map
from nilearn.image import index_img

plot_stat_map(index_img(components, 0), mean_func_img, display_mode='y',
              cut_coords=4, title="Component 0")

plt.show()

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