nilearn

import warnings warnings.filterwarnings("ignore", message="The provided image has no sform in its header.") from nilearn import datasets, plotting, maskers # 1. Fetch a sample fMRI dataset (Haxby dataset) haxby_dataset = datasets.fetch_haxby(subjects=1, fetch_stimuli=False) fmri_filename = haxby_dataset.func[0] anat_filename = haxby_dataset.anat[0] mask_filename = haxby_dataset.mask_vt[0] # 2. Extract signals using NiftiMasker # The mask_strategy='epi' is often more robust for EPI images masker = maskers.NiftiMasker(mask_img=mask_filename, smoothing_fwhm=6, standardize='zscore_sample') fmri_data_masked = masker.fit_transform(fmri_filename) # 3. Plot the anatomical image and overlay the mask print(f"Shape of masked fMRI data: {fmri_data_masked.shape}") # Plotting the anatomical image with the mask overlay plotting.plot_anat( anat_filename, title="Anatomical image with mask overlay", display_mode='ortho', cut_coords=(0, 0, 0), output_file=None # Change to a filename like 'anat_with_mask.png' to save ) # Plotting a single volume from the fMRI data first_fmri_volume = image.index_img(fmri_filename, 0) plotting.plot_img( first_fmri_volume, bg_img=anat_filename, title="First fMRI volume", display_mode='ortho', cut_coords=(0, 0, 0), output_file=None # Change to a filename like 'first_fmri_volume.png' to save ) # You can also show the plots (usually at the end of a script or in an interactive session) # plotting.show()