.. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` 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 ICA components across all the maps. See :func:`nilearn.datasets.fetch_neurovault` documentation for more details. .. 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.input_data import NiftiMasker from nilearn import plotting Get image and term data ----------------------- .. 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 Already fetched 2 images Already fetched 3 images Already fetched 4 images Already fetched 5 images Already fetched 6 images Already fetched 7 images Already fetched 8 images Already fetched 9 images Already fetched 10 images Already fetched 11 images Already fetched 12 images Already fetched 13 images Already fetched 14 images Already fetched 15 images Already fetched 16 images Already fetched 17 images Already fetched 18 images Already fetched 19 images Already fetched 20 images Already fetched 21 images Already fetched 22 images Already fetched 23 images Already fetched 24 images Already fetched 25 images Already fetched 26 images Already fetched 27 images Already fetched 28 images Already fetched 29 images Already fetched 30 images 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 task planum temporale temporale planum superior anterior insula parietal posterior superior Reshape and mask images ----------------------- .. 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. Run ICA and map components to terms ----------------------------------- .. 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... Done, plotting results. Generate figures ---------------- .. 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:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_001.png :alt: plot ica neurovault :class: sphx-glr-multi-img * .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_002.png :alt: plot ica neurovault :class: sphx-glr-multi-img * .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_003.png :alt: plot ica neurovault :class: sphx-glr-multi-img * .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_004.png :alt: plot ica neurovault :class: sphx-glr-multi-img * .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_005.png :alt: plot ica neurovault :class: sphx-glr-multi-img * .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_006.png :alt: plot ica neurovault :class: sphx-glr-multi-img * .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_007.png :alt: plot ica neurovault :class: sphx-glr-multi-img * .. image:: /auto_examples/07_advanced/images/sphx_glr_plot_ica_neurovault_008.png :alt: plot ica neurovault :class: sphx-glr-multi-img 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 .. code-block:: default # Done. plotting.show() .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 0 minutes 43.785 seconds) .. _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 ` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_ica_neurovault.ipynb ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_