.. 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 :ref:`Go to the end ` 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: Independent Component Analysis (ICA) of NeuroVault maps ======================================================== This example shows how to download statistical maps from `NeuroVault `_ with :func:`~nilearn.datasets.fetch_neurovault`, label them with `Neurosynth `_ terms, and then compute Independent Component Analysis (:term:`ICA`) components across all the downloaded statistical maps. .. note:: This example is modified from code originally authored by `Chris Gorgolewski `_ and `Gaël Varoquaux `_ and available at `neurovault_analysis `_. .. GENERATED FROM PYTHON SOURCE LINES 24-40 Get image and associated term data ---------------------------------- First, we download statistical images from :term:`NeuroVault`. To reduce computational time we download only 30 images, but note that using more images will provide better results. Each statistical image is associated with a set of `Neurosynth `_ terms (or ``vocabulary``) which describe the analysis. For example, a study may be associated with terms such as "motor" or "working memory." We will also download these terms for analysis. .. GENERATED FROM PYTHON SOURCE LINES 40-59 .. code-block:: Python import numpy as np from nilearn.datasets import fetch_neurovault nv_data = fetch_neurovault( max_images=30, fetch_neurosynth_words=True, timeout=30.0 ) images = nv_data["images"] term_weights = nv_data["word_frequencies"] vocabulary = nv_data["vocabulary"] # Neurosynth occasionally experiences stability issues ; # we aim to quickly alert if images cannot be downloaded. if term_weights is None: term_weights = np.ones((len(images), 2)) vocabulary = np.asarray(["Neurosynth is down", "Please try again later"]) .. rst-class:: sphx-glr-script-out .. code-block:: none [fetch_neurovault] Dataset directory found: /home/runner/work/nilearn/nilearn/nilearn_data/neurovault [fetch_neurovault] Reading local neurovault data. 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[fetch_neurovault] Computing word features. [fetch_neurovault] Computing word features done; vocabulary size: 1315 .. GENERATED FROM PYTHON SOURCE LINES 60-61 After downloading, clean and report term scores. .. GENERATED FROM PYTHON SOURCE LINES 61-69 .. code-block:: Python 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 .. code-block:: none Top 10 neurosynth terms from downloaded images: superior temporal task auditory posterior superior anterior insula superior parietal temporale planum temporale planum .. GENERATED FROM PYTHON SOURCE LINES 70-80 Reshape and mask images ----------------------- As each statistical image comes from a different study, we apply some light preprocessing to improve our ability to compare them. We use a :func:`~nilearn.maskers.NiftiMasker` to extract all image data within an :term:`MNI` brain mask, accessed via :func:`~nilearn.datasets.load_mni152_brain_mask`. .. GENERATED FROM PYTHON SOURCE LINES 80-118 .. code-block:: Python import warnings from nilearn.datasets import load_mni152_brain_mask from nilearn.image import smooth_img from nilearn.maskers import NiftiMasker 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: with warnings.catch_warnings(): warnings.simplefilter("ignore") 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, :] .. GENERATED FROM PYTHON SOURCE LINES 119-133 Run :term:`ICA` and map components to terms ------------------------------------------- Once we have all statistical images processed into a single 2D matrix, we can use :class:`sklearn.decomposition.FastICA` to extract :term:`ICA` components for this sample of images. In this example, we are using a very small number of images (i.e., only 30), so we explicitly pass ``n_components`` to solve for a small number of components. In real data analysis, we may want to instead set ``n_components=None`` to find as many components as the rank of the data. For more detail on :term:`ICA`, please refer to the :sklearn:`scikit-learn user guide `. .. GENERATED FROM PYTHON SOURCE LINES 133-142 .. code-block:: Python from sklearn.decomposition import FastICA n_components = 3 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) .. GENERATED FROM PYTHON SOURCE LINES 143-147 Generate figures ---------------- Finally, plot the generated :term:`ICA` maps and their loadings on the associated ``term_weights``. .. GENERATED FROM PYTHON SOURCE LINES 147-169 .. code-block:: Python from scipy import stats from nilearn.plotting import plot_stat_map, show for index, (ic_map, ic_terms) in enumerate( zip(ica_maps, term_weights_for_components, strict=False) ): 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])}" plot_stat_map(ic_img, threshold=ic_threshold, colorbar=False, title=title) show() .. 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 .. GENERATED FROM PYTHON SOURCE LINES 170-173 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. .. rst-class:: sphx-glr-timing **Total running time of the script:** (0 minutes 10.618 seconds) **Estimated memory usage:** 365 MB .. _sphx_glr_download_auto_examples_07_advanced_plot_ica_neurovault.py: .. only:: html .. container:: sphx-glr-footer sphx-glr-footer-example .. container:: binder-badge .. image:: images/binder_badge_logo.svg :target: https://mybinder.org/v2/gh/nilearn/nilearn/0.14.1?urlpath=lab/tree/notebooks/auto_examples/07_advanced/plot_ica_neurovault.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_ica_neurovault.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_ica_neurovault.py ` .. container:: sphx-glr-download sphx-glr-download-zip :download:`Download zipped: plot_ica_neurovault.zip ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_