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}")
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 /home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0022_MR1/mwrc1OAS1_0022_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0212_MR1/mwrc1OAS1_0212_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0079_MR1/mwrc1OAS1_0079_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0119_MR1/mwrc1OAS1_0119_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0016_MR1/mwrc1OAS1_0016_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0104_MR1/mwrc1OAS1_0104_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0203_MR1/mwrc1OAS1_0203_MR1_mpr_anon_fslswapdim_bet.nii.gz,
 /home/runner/work/nilearn/nilearn/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/runner/work/nilearn/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:  1.4min finished
Time Elapsed: 102.87477660179138 seconds, 1.7145796100298563 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()
  • plot oasis vbm space net
  • graph-net: Mean Absolute Error 12.33 years

Total running time of the script: (1 minutes 50.363 seconds)

Estimated memory usage: 2902 MB

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