Here we smooth a mean EPI image and plot the result
As we vary the smoothing FWHM, note how we decrease the amount of noise, but also loose spatial details. In general, the best amount of smoothing for a given analysis depends on the spatial extent of the effects that are expected.
First subject functional nifti image (4D) are located at: /home/parietal/gvaroqua/nilearn_data/adhd/data/0010042/0010042_rest_tshift_RPI_voreg_mni.nii.gz
from nilearn import datasets, plotting, image data = datasets.fetch_adhd(n_subjects=1) # Print basic information on the dataset print('First subject functional nifti image (4D) are located at: %s' % data.func) first_epi_file = data.func # First the compute the mean image, from the 4D series of image mean_func = image.mean_img(first_epi_file) # Then we smooth, with a varying amount of smoothing, from none to 20mm # by increments of 5mm for smoothing in range(0, 25, 5): smoothed_img = image.smooth_img(mean_func, smoothing) plotting.plot_epi(smoothed_img, title="Smoothing %imm" % smoothing) plotting.show()
Total running time of the script: ( 0 minutes 4.566 seconds)