.. 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.py"
.. LINE NUMBERS ARE GIVEN BELOW.
.. 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_02_decoding_plot_oasis_vbm.py:
Voxel-Based Morphometry on Oasis dataset
========================================
This example uses Voxel-Based Morphometry (VBM) to study the relationship
between aging and gray matter density.
The data come from the `OASIS `_ project.
If you use it, you need to agree with the data usage agreement available
on the website.
It has been run through a standard VBM pipeline (using SPM8 and
NewSegment) to create VBM maps, which we study here.
Predictive modeling analysis: VBM bio-markers of aging?
--------------------------------------------------------
We run a standard SVM-ANOVA nilearn pipeline to predict age from the VBM
data. We use only 100 subjects from the OASIS dataset to limit the memory
usage.
Note that for an actual predictive modeling study of aging, the study
should be ran on the full set of subjects. Also, all parameters should be set
by cross-validation. This includes the smoothing applied to the data and the
number of features selected by the ANOVA step. Indeed, even these
data-preparation parameter impact significantly the prediction score.
Also, parameters such as the smoothing should be applied to the data and the
number of features selected by the ANOVA step should be set by nested
cross-validation, as they impact significantly the prediction score.
Brain mapping with mass univariate
-----------------------------------
SVM weights are very noisy, partly because heavy smoothing is detrimental
for the prediction here. A standard analysis using mass-univariate GLM
(here permuted to have exact correction for multiple comparisons) gives a
much clearer view of the important regions.
____
.. include:: ../../../examples/masker_note.rst
.. GENERATED FROM PYTHON SOURCE LINES 46-58
.. code-block:: default
# Authors: Elvis Dhomatob, , Apr. 2014
# Virgile Fritsch, , Apr 2014
# Gael Varoquaux, Apr 2014
# Andres Hoyos-Idrobo, Apr 2017
import numpy as np
import matplotlib.pyplot as plt
from nilearn import datasets
from nilearn.maskers import NiftiMasker
from nilearn.image import get_data
n_subjects = 100 # more subjects requires more memory
.. GENERATED FROM PYTHON SOURCE LINES 59-61
Load Oasis dataset
-------------------
.. GENERATED FROM PYTHON SOURCE LINES 61-78
.. code-block:: default
oasis_dataset = datasets.fetch_oasis_vbm(
n_subjects=n_subjects, legacy_format=False
)
gray_matter_map_filenames = oasis_dataset.gray_matter_maps
age = oasis_dataset.ext_vars['age'].values
# Split data into training set and test set
from sklearn.model_selection import train_test_split
gm_imgs_train, gm_imgs_test, age_train, age_test = train_test_split(
gray_matter_map_filenames, age, train_size=.6, random_state=0)
# print basic information on the dataset
print('First gray-matter anatomy image (3D) is located at: %s' %
oasis_dataset.gray_matter_maps[0]) # 3D data
print('First white-matter anatomy image (3D) is located at: %s' %
oasis_dataset.white_matter_maps[0]) # 3D data
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
First gray-matter anatomy image (3D) is located at: /home/nicolas/nilearn_data/oasis1/OAS1_0001_MR1/mwrc1OAS1_0001_MR1_mpr_anon_fslswapdim_bet.nii.gz
First white-matter anatomy image (3D) is located at: /home/nicolas/nilearn_data/oasis1/OAS1_0001_MR1/mwrc2OAS1_0001_MR1_mpr_anon_fslswapdim_bet.nii.gz
.. GENERATED FROM PYTHON SOURCE LINES 79-81
Preprocess data
----------------
.. GENERATED FROM PYTHON SOURCE LINES 81-97
.. code-block:: default
nifti_masker = NiftiMasker(
standardize=False,
smoothing_fwhm=2,
memory='nilearn_cache') # cache options
gm_maps_masked = nifti_masker.fit_transform(gm_imgs_train)
# The features with too low between-subject variance are removed using
# :class:`sklearn.feature_selection.VarianceThreshold`.
from sklearn.feature_selection import VarianceThreshold
variance_threshold = VarianceThreshold(threshold=.01)
gm_maps_thresholded = variance_threshold.fit_transform(gm_maps_masked)
# Then we convert the data back to the mask image in order to use it for
# decoding process
mask = nifti_masker.inverse_transform(variance_threshold.get_support())
.. GENERATED FROM PYTHON SOURCE LINES 98-100
Prediction pipeline with ANOVA and SVR using
:class:`nilearn.decoding.DecoderRegressor` Object
.. GENERATED FROM PYTHON SOURCE LINES 100-130
.. code-block:: default
# In nilearn we can benefit from the built-in DecoderRegressor object to
# do ANOVA with SVR instead of manually defining the whole pipeline.
# This estimator also uses Cross Validation to select best models and ensemble
# them. Furthermore, you can pass n_jobs= to the
# DecoderRegressor class to take advantage of a multi-core system.
# To save time (because these are anat images with many voxels), we include
# only the 1-percent voxels most correlated with the age variable to fit. We
# also want to set mask hyperparameter to be the mask we just obtained above.
from nilearn.decoding import DecoderRegressor
decoder = DecoderRegressor(estimator='svr', mask=mask,
scoring='neg_mean_absolute_error',
screening_percentile=1,
n_jobs=1)
# Fit and predict with the decoder
decoder.fit(gm_imgs_train, age_train)
# Sort test data for better visualization (trend, etc.)
perm = np.argsort(age_test)[::-1]
age_test = age_test[perm]
gm_imgs_test = np.array(gm_imgs_test)[perm]
age_pred = decoder.predict(gm_imgs_test)
prediction_score = -np.mean(decoder.cv_scores_['beta'])
print("=== DECODER ===")
print("explained variance for the cross-validation: %f" % prediction_score)
print("")
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
=== DECODER ===
explained variance for the cross-validation: 10.670598
.. GENERATED FROM PYTHON SOURCE LINES 131-133
Visualization
--------------
.. GENERATED FROM PYTHON SOURCE LINES 133-144
.. code-block:: default
weight_img = decoder.coef_img_['beta']
# Create the figure
from nilearn.plotting import plot_stat_map, show
bg_filename = gray_matter_map_filenames[0]
z_slice = 0
display = plot_stat_map(weight_img, bg_img=bg_filename,
display_mode='z', cut_coords=[z_slice])
display.title("SVM weights")
show()
.. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_001.png
:alt: plot oasis vbm
:srcset: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_001.png
:class: sphx-glr-single-img
.. GENERATED FROM PYTHON SOURCE LINES 145-147
Visualize the quality of predictions
-------------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 147-161
.. code-block:: default
plt.figure(figsize=(6, 4.5))
plt.suptitle("Decoder: Mean Absolute Error %.2f years" % prediction_score)
linewidth = 3
plt.plot(age_test, label="True age", linewidth=linewidth)
plt.plot(age_pred, '--', c="g", label="Predicted age", linewidth=linewidth)
plt.ylabel("age")
plt.xlabel("subject")
plt.legend(loc="best")
plt.figure(figsize=(6, 4.5))
plt.plot(age_test - age_pred, label="True age - predicted age",
linewidth=linewidth)
plt.xlabel("subject")
plt.legend(loc="best")
.. rst-class:: sphx-glr-horizontal
*
.. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_002.png
:alt: Decoder: Mean Absolute Error 10.67 years
:srcset: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_002.png
:class: sphx-glr-multi-img
*
.. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_003.png
:alt: plot oasis vbm
:srcset: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_003.png
:class: sphx-glr-multi-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
.. GENERATED FROM PYTHON SOURCE LINES 162-164
Inference with massively univariate model
-----------------------------------------
.. GENERATED FROM PYTHON SOURCE LINES 164-197
.. code-block:: default
print("Massively univariate model")
gm_maps_masked = NiftiMasker().fit_transform(gray_matter_map_filenames)
data = variance_threshold.fit_transform(gm_maps_masked)
# Statistical inference
from nilearn.mass_univariate import permuted_ols
neg_log_pvals, t_scores_original_data, _ = permuted_ols(
age, data, # + intercept as a covariate by default
n_perm=2000, # 1,000 in the interest of time; 10000 would be better
verbose=1, # display progress bar
n_jobs=1) # can be changed to use more CPUs
signed_neg_log_pvals = neg_log_pvals * np.sign(t_scores_original_data)
signed_neg_log_pvals_unmasked = nifti_masker.inverse_transform(
variance_threshold.inverse_transform(signed_neg_log_pvals))
# Show results
threshold = -np.log10(0.1) # 10% corrected
fig = plt.figure(figsize=(5.5, 7.5), facecolor='k')
display = plot_stat_map(signed_neg_log_pvals_unmasked, bg_img=bg_filename,
threshold=threshold, cmap=plt.cm.RdBu_r,
display_mode='z', cut_coords=[z_slice],
figure=fig)
title = ('Negative $\\log_{10}$ p-values'
'\n(Non-parametric + max-type correction)')
display.title(title, y=1.2)
n_detections = (get_data(signed_neg_log_pvals_unmasked) > threshold).sum()
print('\n%d detections' % n_detections)
show()
.. image-sg:: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_004.png
:alt: plot oasis vbm
:srcset: /auto_examples/02_decoding/images/sphx_glr_plot_oasis_vbm_004.png
:class: sphx-glr-single-img
.. rst-class:: sphx-glr-script-out
Out:
.. code-block:: none
Massively univariate model
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[Parallel(n_jobs=1)]: Done 1 out of 1 | elapsed: 33.1s finished
1997 detections
.. rst-class:: sphx-glr-timing
**Total running time of the script:** ( 1 minutes 1.202 seconds)
**Estimated memory usage:** 1939 MB
.. _sphx_glr_download_auto_examples_02_decoding_plot_oasis_vbm.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/02_decoding/plot_oasis_vbm.ipynb
:alt: Launch binder
:width: 150 px
.. container:: sphx-glr-download sphx-glr-download-python
:download:`Download Python source code: plot_oasis_vbm.py `
.. container:: sphx-glr-download sphx-glr-download-jupyter
:download:`Download Jupyter notebook: plot_oasis_vbm.ipynb `
.. only:: html
.. rst-class:: sphx-glr-signature
`Gallery generated by Sphinx-Gallery `_