Note
Go to the end to download the full example code or to run this example in your browser via Binder
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: SpaceNet: decoding with spatial structure for better maps.
Load the Oasis VBM dataset#
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]
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=<some_high_value> to the SpaceNetRegressor class, to take advantage of a multi-core system.
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}")
[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,
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/home/himanshu/nilearn_data/oasis1/OAS1_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz,
/home/himanshu/nilearn_data/oasis1/OAS1_0216_MR1/mwrc1OAS1_0216_MR1_mpr_anon_fslswapdim_bet.nii.gz,
/home/himanshu/nilearn_data/oasis1/OAS1_0096_MR1/mwrc1OAS1_0096_MR1_mpr_anon_fslswapdim_bet.nii.gz,
/home/himanshu/nilearn_data/oasis1/OAS1_0182_MR1/mwrc1OAS1_0182_MR1_mpr_anon_fslswapdim_bet.nii.gz,
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/home/himanshu/nilearn_data/oasis1/OAS1_0069_MR1/mwrc1OAS1_0069_MR1_mpr_anon_fslswapdim_bet.nii.gz,
/home/himanshu/nilearn_data/oasis1/OAS1_0025_MR1/mwrc1OAS1_0025_MR1_mpr_anon_fslswapdim_bet.nii.gz,
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/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
Visualize the decoding maps and quality of predictions#
Visualize the resulting maps
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()
Total running time of the script: (3 minutes 27.822 seconds)
Estimated memory usage: 2901 MB
