.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "auto_examples/02_decoding/plot_oasis_vbm_space_net.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_02_decoding_plot_oasis_vbm_space_net.py: Voxel-Based Morphometry on Oasis dataset with Space-Net prior ============================================================= Predicting age from gray-matter concentration maps from OASIS dataset. Note that age is a continuous variable, we use the regressor here, and not the classification object. See also the documentation: :ref:`space_net`. .. GENERATED FROM PYTHON SOURCE LINES 14-16 Load the Oasis :term:`VBM` dataset ---------------------------------- .. GENERATED FROM PYTHON SOURCE LINES 16-44 .. code-block:: Python import numpy as np from nilearn import datasets n_subjects = 200 # increase this number if you have more RAM on your box dataset_files = datasets.fetch_oasis_vbm( n_subjects=n_subjects, legacy_format=False ) age = dataset_files.ext_vars["age"].astype(float) age = np.array(age) gm_imgs = np.array(dataset_files.gray_matter_maps) # Split data into training set and test set from sklearn.model_selection import train_test_split from sklearn.utils import check_random_state rng = check_random_state(42) gm_imgs_train, gm_imgs_test, age_train, age_test = train_test_split( gm_imgs, age, train_size=0.6, random_state=rng ) # Sort test data for better visualization (trend, etc.) perm = np.argsort(age_test)[::-1] age_test = age_test[perm] gm_imgs_test = gm_imgs_test[perm] .. GENERATED FROM PYTHON SOURCE LINES 45-55 Fit the SpaceNet and predict with it ------------------------------------ To save time (because these are anat images with many voxels), we include only the 5-percent voxels most correlated with the age variable to fit. Also, we set memory_level=2 so that more of the intermediate computations are cached. We used a graph-net penalty here but more beautiful results can be obtained using the TV-l1 penalty, at the expense of longer runtimes. Also, you may pass and n_jobs= to the SpaceNetRegressor class, to take advantage of a multi-core system. .. GENERATED FROM PYTHON SOURCE LINES 55-72 .. code-block:: Python from nilearn.decoding import SpaceNetRegressor decoder = SpaceNetRegressor( memory="nilearn_cache", penalty="graph-net", screening_percentile=5.0, memory_level=2, standardize="zscore_sample", n_jobs=2, ) decoder.fit(gm_imgs_train, age_train) # fit coef_img = decoder.coef_img_ y_pred = decoder.predict(gm_imgs_test).ravel() # predict mse = np.mean(np.abs(age_test - y_pred)) print(f"Mean square error (MSE) on the predicted age: {mse:.2f}") .. rst-class:: sphx-glr-script-out .. code-block:: none [NiftiMasker.fit] Loading data from [/home/himanshu/nilearn_data/oasis1/OAS1_0003_MR1/mwrc1OAS1_0003_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/himanshu/nilearn_data/oasis1/OAS1_0086_MR1/mwrc1OAS1_0086_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/himanshu/nilearn_data/oasis1/OAS1_0052_MR1/mwrc1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/himanshu/nilearn_data/oasis1/OAS1_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz, 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/home/himanshu/nilearn_data/oasis1/OAS1_0079_MR1/mwrc1OAS1_0079_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/himanshu/nilearn_data/oasis1/OAS1_0119_MR1/mwrc1OAS1_0119_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/himanshu/nilearn_data/oasis1/OAS1_0016_MR1/mwrc1OAS1_0016_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/himanshu/nilearn_data/oasis1/OAS1_0104_MR1/mwrc1OAS1_0104_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/himanshu/nilearn_data/oasis1/OAS1_0203_MR1/mwrc1OAS1_0203_MR1_mpr_anon_fslswapdim_bet.nii.gz, /home/himanshu/nilearn_data/oasis1/OAS1_0114_MR1/mwrc1OAS1_0114_MR1_mpr_anon_fslswapdim_bet.nii.gz] [NiftiMasker.fit] Computing the mask [NiftiMasker.fit] Resampling mask [NiftiMasker.transform_single_imgs] Loading data from Nifti1Image( shape=(91, 109, 91, 120), affine=array([[ -2., 0., 0., 90.], [ 0., 2., 0., -126.], [ 0., 0., 2., -72.], [ 0., 0., 0., 1.]]) ) [NiftiMasker.transform_single_imgs] Extracting region signals [NiftiMasker.transform_single_imgs] Cleaning extracted signals /home/himanshu/Desktop/nilearn_work/nilearn/examples/02_decoding/plot_oasis_vbm_space_net.py:65: UserWarning: Brain mask is bigger than the volume of a standard human brain. This object is probably not tuned to be used on such data. [Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers. [Parallel(n_jobs=2)]: Done 8 out of 8 | elapsed: 3.0min finished Time Elapsed: 199.63837957382202 seconds, 3.327306326230367 minutes. [NiftiMasker.transform_single_imgs] Loading data from Nifti1Image( shape=(91, 109, 91, 80), affine=array([[ -2., 0., 0., 90.], [ 0., 2., 0., -126.], [ 0., 0., 2., -72.], [ 0., 0., 0., 1.]]) ) [NiftiMasker.transform_single_imgs] Extracting region signals [NiftiMasker.transform_single_imgs] Cleaning extracted signals Mean square error (MSE) on the predicted age: 12.33 .. GENERATED FROM PYTHON SOURCE LINES 73-76 Visualize the decoding maps and quality of predictions ------------------------------------------------------ Visualize the resulting maps .. GENERATED FROM PYTHON SOURCE LINES 76-107 .. code-block:: Python from nilearn.plotting import plot_stat_map, show # weights map background_img = gm_imgs[0] plot_stat_map( coef_img, background_img, title="graph-net weights", display_mode="z", cut_coords=1, ) # Plot the prediction errors. import matplotlib.pyplot as plt plt.figure() plt.suptitle(f"graph-net: Mean Absolute Error {mse:.2f} years") linewidth = 3 ax1 = plt.subplot(211) ax1.plot(age_test, label="True age", linewidth=linewidth) ax1.plot(y_pred, "--", c="g", label="Predicted age", linewidth=linewidth) ax1.set_ylabel("age") plt.legend(loc="best") ax2 = plt.subplot(212) ax2.plot( age_test - y_pred, label="True age - predicted age", linewidth=linewidth ) ax2.set_xlabel("subject") plt.legend(loc="best") show() .. rst-class:: sphx-glr-horizontal * .. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_001.png :alt: plot oasis vbm space net :srcset: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_001.png :class: sphx-glr-multi-img * .. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_002.png :alt: graph-net: Mean Absolute Error 12.33 years :srcset: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_space_net_002.png :class: sphx-glr-multi-img .. rst-class:: sphx-glr-timing **Total running time of the script:** (3 minutes 27.822 seconds) **Estimated memory usage:** 2901 MB .. _sphx_glr_download_auto_examples_02_decoding_plot_oasis_vbm_space_net.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/main?urlpath=lab/tree/notebooks/auto_examples/02_decoding/plot_oasis_vbm_space_net.ipynb :alt: Launch binder :width: 150 px .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: plot_oasis_vbm_space_net.ipynb ` .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: plot_oasis_vbm_space_net.py ` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_