Nilearn教程系列(3)-ICA静息功能磁共振成像的分组分析:CanICA

from nilearn import datasets

adhd_dataset = datasets.fetch_adhd(n_subjects=30)
func_filenames = adhd_dataset.func  # list of 4D nifti files for each subject

# 数据集基本信息
print('First functional nifti image (4D) is at: %s' %
      func_filenames[0])  # 4D data

from nilearn.decomposition import CanICA

canica = CanICA(n_components=20, smoothing_fwhm=6.,
                memory="nilearn_cache", memory_level=2,
                threshold=3., verbose=10, random_state=0)
canica.fit(func_filenames)

# 检索大脑空间中的独立成分。可通过属性components_img_直接访问。
components_img = canica.components_img_
#components_img是一个Nifti Image对象，可以使用以下行保存到文件中：
components_img.to_filename('canica_resting_state.nii.gz')

from nilearn.plotting import plot_prob_atlas
import warnings
warnings.filterwarnings("ignore")
# Plot all ICA components together
plot_prob_atlas(components_img, title='All ICA components')

from nilearn.image import iter_img
from nilearn.plotting import plot_stat_map, show
import warnings
warnings.filterwarnings("ignore")
for i, cur_img in enumerate(iter_img(components_img)):
    plot_stat_map(cur_img, display_mode="z", title="IC %d" % i,
                  cut_coords=1, colorbar=False)

show()