.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "auto_examples/07_advanced/plot_ica_neurovault.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. only:: html
.. note::
:class: sphx-glr-download-link-note
Click :ref:`here <sphx_glr_download_auto_examples_07_advanced_plot_ica_neurovault.py>`
to download the full example code or to run this example in your browser via Binder
.. rst-class:: sphx-glr-example-title
.. _sphx_glr_auto_examples_07_advanced_plot_ica_neurovault.py:
NeuroVault cross-study ICA maps.
================================
This example shows how to download statistical maps from
NeuroVault, label them with NeuroSynth terms,
and compute :term:`ICA` components across all the maps.
See :func:`nilearn.datasets.fetch_neurovault`
documentation for more details.
.. include:: ../../../examples/masker_note.rst
.. GENERATED FROM PYTHON SOURCE LINES 15-34
.. code-block:: default
# Author: Ben Cipollini
# License: BSD
# Ported from code authored by Chris Filo Gorgolewski, Gael Varoquaux
# https://github.com/NeuroVault/neurovault_analysis
import warnings
import numpy as np
from scipy import stats
from sklearn.decomposition import FastICA
from nilearn.datasets import fetch_neurovault
from nilearn.image import smooth_img
from nilearn.datasets import load_mni152_brain_mask
from nilearn.maskers import NiftiMasker
from nilearn import plotting
.. GENERATED FROM PYTHON SOURCE LINES 35-37
Get image and term data
-----------------------
.. GENERATED FROM PYTHON SOURCE LINES 37-64
.. code-block:: default
# 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])
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Fetching Neurovault images; if you haven't downloaded any Neurovault data before this will take several minutes.
Reading local neurovault data.
Already fetched 1 image
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30 images found on local disk.
Computing word features.
Computing word features done; vocabulary size: 1307
Top 10 neurosynth terms from downloaded images:
superior temporal
auditory
anterior insula
task
temporale
planum temporale
planum
temporal sulcus
posterior superior
superior
.. GENERATED FROM PYTHON SOURCE LINES 65-67
Reshape and mask images
-----------------------
.. GENERATED FROM PYTHON SOURCE LINES 67-104
.. code-block:: default
print("\nReshaping and masking images.\n")
with warnings.catch_warnings():
warnings.simplefilter('ignore', UserWarning)
warnings.simplefilter('ignore', DeprecationWarning)
mask_img = load_mni152_brain_mask()
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("Failed to mask/reshape image: id: {0}; "
"name: '{1}'; collection: {2}; error: {3}".format(
meta.get('id'), meta.get('name'),
meta.get('collection_id'), 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, :]
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Reshaping and masking images.
.. GENERATED FROM PYTHON SOURCE LINES 105-107
Run ICA and map components to terms
-----------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 107-120
.. code-block:: default
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.')
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Running ICA; may take time...
/home/nicolas/GitRepos/scikit-learn-fork/sklearn/decomposition/_fastica.py:484: FutureWarning:
From version 1.3 whiten='unit-variance' will be used by default.
Done, plotting results.
.. GENERATED FROM PYTHON SOURCE LINES 121-123
Generate figures
----------------
.. GENERATED FROM PYTHON SOURCE LINES 123-144
.. code-block:: default
with warnings.catch_warnings():
warnings.simplefilter('ignore', DeprecationWarning)
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 = 'IC%i %s' % (index, ', '.join(important_terms[::-1]))
plotting.plot_stat_map(
ic_img, threshold=ic_threshold, colorbar=False,
title=title)
.. rst-class:: sphx-glr-horizontal
*
.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_001.png
:alt: plot ica neurovault
:srcset: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_001.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_002.png
:alt: plot ica neurovault
:srcset: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_002.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_003.png
:alt: plot ica neurovault
:srcset: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_003.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_004.png
:alt: plot ica neurovault
:srcset: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_004.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_005.png
:alt: plot ica neurovault
:srcset: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_005.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_006.png
:alt: plot ica neurovault
:srcset: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_006.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_007.png
:alt: plot ica neurovault
:srcset: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_007.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_008.png
:alt: plot ica neurovault
:srcset: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_008.png
:class: sphx-glr-multi-img
.. GENERATED FROM PYTHON SOURCE LINES 145-148
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
.. GENERATED FROM PYTHON SOURCE LINES 148-151
.. code-block:: default
# Done.
plotting.show()
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 0 minutes 50.054 seconds)
**Estimated memory usage:** 117 MB
.. _sphx_glr_download_auto_examples_07_advanced_plot_ica_neurovault.py:
.. only :: html
.. container:: sphx-glr-footer
:class: sphx-glr-footer-example
.. container:: binder-badge
.. image:: images/binder_badge_logo.svg
:target: https://mybinder.org/v2/gh/nilearn/nilearn.github.io/main?filepath=examples/auto_examples/07_advanced/plot_ica_neurovault.ipynb
:alt: Launch binder
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_ica_neurovault.py <plot_ica_neurovault.py>`
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_ica_neurovault.ipynb <plot_ica_neurovault.ipynb>`
.. only:: html
.. rst-class:: sphx-glr-signature
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