Note
Go to the end to download the full example code. or to run this example in your browser via Binder
NeuroVault cross-study ICA maps¶
This example shows how to download statistical maps from NeuroVault, label them with NeuroSynth terms, and compute ICA components across all the maps.
See nilearn.datasets.fetch_neurovault
documentation for more details.
Note
If you are using Nilearn with a version older than 0.9.0,
then you should either upgrade your version or import maskers
from the input_data module instead of the maskers module.
That is, you should manually replace in the following example all occurrences of:
from nilearn.maskers import NiftiMasker
with:
from nilearn.input_data import NiftiMasker
import numpy as np
from scipy import stats
from sklearn.decomposition import FastICA
from nilearn import plotting
from nilearn.datasets import fetch_neurovault, load_mni152_brain_mask
from nilearn.image import smooth_img
from nilearn.maskers import NiftiMasker
Get image and term data¶
# Download images
# Here by default we only download 80 images to save time,
# but for better results I recommend using at least 200.
print(
"Fetching Neurovault images; "
"if you haven't downloaded any Neurovault data before "
"this will take several minutes."
)
nv_data = fetch_neurovault(max_images=30, fetch_neurosynth_words=True)
images = nv_data["images"]
term_weights = nv_data["word_frequencies"]
vocabulary = nv_data["vocabulary"]
if term_weights is None:
term_weights = np.ones((len(images), 2))
vocabulary = np.asarray(["Neurosynth is down", "Please try again later"])
# Clean and report term scores
term_weights[term_weights < 0] = 0
total_scores = np.mean(term_weights, axis=0)
print("\nTop 10 neurosynth terms from downloaded images:\n")
for term_idx in np.argsort(total_scores)[-10:][::-1]:
print(vocabulary[term_idx])
Fetching Neurovault images; if you haven't downloaded any Neurovault data before this will take several minutes.
[get_dataset_dir] Dataset found in /home/remi/nilearn_data/neurovault
[fetch_neurovault] Reading local neurovault data.
[fetch_neurovault] Already fetched 1 image
[fetch_neurovault] Already fetched 2 images
[fetch_neurovault] Already fetched 3 images
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[fetch_neurovault] Already fetched 29 images
[fetch_neurovault] Already fetched 30 images
[fetch_neurovault] 30 images found on local disk.
[neurosynth_words_vectorized] Computing word features.
[neurosynth_words_vectorized] Computing word features done; vocabulary size: 1307
Top 10 neurosynth terms from downloaded images:
superior temporal
auditory
task
planum temporale
temporale
planum
superior
anterior insula
parietal
posterior superior
Reshape and mask images¶
print("\nReshaping and masking images.\n")
mask_img = load_mni152_brain_mask(resolution=2)
masker = NiftiMasker(mask_img=mask_img, memory="nilearn_cache", memory_level=1)
masker = masker.fit()
# Images may fail to be transformed, and are of different shapes,
# so we need to transform one-by-one and keep track of failures.
X = []
is_usable = np.ones((len(images),), dtype=bool)
for index, image_path in enumerate(images):
# load image and remove nan and inf values.
# applying smooth_img to an image with fwhm=None simply cleans up
# non-finite values but otherwise doesn't modify the image.
image = smooth_img(image_path, fwhm=None)
try:
X.append(masker.transform(image))
except Exception as e:
meta = nv_data["images_meta"][index]
print(
f"Failed to mask/reshape image: id: {meta.get('id')}; "
f"name: '{meta.get('name')}'; "
f"collection: {meta.get('collection_id')}; error: {e}"
)
is_usable[index] = False
# Now reshape list into 2D matrix, and remove failed images from terms
X = np.vstack(X)
term_weights = term_weights[is_usable, :]
Reshaping and masking images.
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
/home/remi/github/nilearn/nilearn_doc_build/.tox/doc/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning:
imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
[NiftiMasker.wrapped] Resampling images
Run ICA and map components to terms¶
print("Running ICA; may take time...")
# We use a very small number of components as we have downloaded only 80
# images. For better results, increase the number of images downloaded
# and the number of components
n_components = 8
fast_ica = FastICA(n_components=n_components, random_state=0)
ica_maps = fast_ica.fit_transform(X.T).T
term_weights_for_components = np.dot(fast_ica.components_, term_weights)
print("Done, plotting results.")
Running ICA; may take time...
Done, plotting results.
Generate figures¶
for index, (ic_map, ic_terms) in enumerate(
zip(ica_maps, term_weights_for_components)
):
if -ic_map.min() > ic_map.max():
# Flip the map's sign for prettiness
ic_map = -ic_map
ic_terms = -ic_terms
ic_threshold = stats.scoreatpercentile(np.abs(ic_map), 90)
ic_img = masker.inverse_transform(ic_map)
important_terms = vocabulary[np.argsort(ic_terms)[-3:]]
title = f"IC{int(index)} {', '.join(important_terms[::-1])}"
plotting.plot_stat_map(
ic_img, threshold=ic_threshold, colorbar=False, title=title
)
As we can see, some of the components capture cognitive or neurological maps, while other capture noise in the database. More data, better filtering, and better cognitive labels would give better maps
# Done.
plotting.show()
Total running time of the script: (0 minutes 45.910 seconds)
Estimated memory usage: 387 MB
