convert_feat-img_surface.py

"""Convert NiMARE-generated feature maps to surfaces and save to a Pickle file."""
import os.path as op
import pickle
from glob import glob

import nibabel as nib
import numpy as np
from nilearn import surface
from nilearn.datasets import fetch_surf_fsaverage, load_mni152_brain_mask

image_dir = "/home/data/nbc/misc-projects/meta-gradients/code/feature_maps"
out_dir = "/home/data/nbc/misc-projects/meta-gradients/code"

mask = load_mni152_brain_mask()
feature_images = sorted(glob(op.join(image_dir, "*_association-test_z.nii.gz")))

surf_dict = {}

for img in feature_images:

    tmp_feat_name = op.basename(img).split("_")[0]
    print(tmp_feat_name)
    fsaverage = fetch_surf_fsaverage(mesh="fsaverage5")
    surf_lh = surface.vol_to_surf(
        nib.load(img),
        fsaverage.pial_left,
        radius=6.0,
        interpolation="nearest",
        kind="ball",
        n_samples=None,
        mask_img=mask,
    )
    surf_rh = surface.vol_to_surf(
        nib.load(img),
        fsaverage.pial_right,
        radius=6.0,
        interpolation="nearest",
        kind="ball",
        n_samples=None,
        mask_img=mask,
    )
    surf = np.transpose(np.hstack((surf_lh, surf_rh)))

    surf_dict[tmp_feat_name] = surf

with open(op.join(out_dir, "ns_feature_maps_surface.pkl.gz"), "wb") as fo:
    pickle.dump(surf_dict, fo)