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

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.

See also

For more information see the dataset description.

import numpy as np

from nilearn.datasets import fetch_oasis_vbm
from nilearn.image import get_data
from nilearn.maskers import NiftiMasker

n_subjects = 200  # more subjects requires more memory

Load Oasis dataset

oasis_dataset = fetch_oasis_vbm(n_subjects=n_subjects)
gray_matter_map_filenames = oasis_dataset.gray_matter_maps
age = oasis_dataset.ext_vars["age"].to_numpy()

# 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=0.6, random_state=0
)

# print basic information on the dataset
print(
    "First gray-matter anatomy image (3D) is located at: "
    f"{oasis_dataset.gray_matter_maps[0]}"
)
print(
    "First white-matter anatomy image (3D) is located at: "
    f"{oasis_dataset.white_matter_maps[0]}"
)

[fetch_oasis_vbm] Dataset directory found:
/home/runner/work/nilearn/nilearn/nilearn_data/oasis1
First gray-matter anatomy image (3D) is located at: /home/runner/work/nilearn/nilearn/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/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0001_MR1/mwrc2OAS1_0001_MR1_mpr_anon_fslswapdim_bet.nii.gz

Preprocess data

The features with too low between-subject variance are removed using sklearn.feature_selection.VarianceThreshold. Then we convert the data back to the mask image in order to use it for decoding process

nifti_masker = NiftiMasker(
    standardize=False, smoothing_fwhm=2, memory="nilearn_cache", verbose=1
)  # cache options
gm_maps_masked = nifti_masker.fit_transform(gm_imgs_train)

from sklearn.feature_selection import VarianceThreshold

variance_threshold = VarianceThreshold(threshold=0.01)
variance_threshold.fit_transform(gm_maps_masked)

mask = nifti_masker.inverse_transform(variance_threshold.get_support())

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work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0214_MR1/mwrc1OAS1_0214_MR1_mpr_an
on_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis
1/OAS1_0044_MR1/mwrc1OAS1_0044_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runne
r/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0065_MR1/mwrc1OAS1_0065_MR1_mpr_
anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oas
is1/OAS1_0158_MR1/mwrc1OAS1_0158_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/run
ner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0099_MR1/mwrc1OAS1_0099_MR1_mp
r_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/o
asis1/OAS1_0078_MR1/mwrc1OAS1_0078_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/r
unner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0098_MR1/mwrc1OAS1_0098_MR1_
mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data
/oasis1/OAS1_0041_MR1/mwrc1OAS1_0041_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home
/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0023_MR1/mwrc1OAS1_0023_MR
1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_da
ta/oasis1/OAS1_0011_MR1/mwrc1OAS1_0011_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/ho
me/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0115_MR1/mwrc1OAS1_0115_
MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_
data/oasis1/OAS1_0075_MR1/mwrc1OAS1_0075_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/
home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0217_MR1/mwrc1OAS1_021
7_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilear
n_data/oasis1/OAS1_0132_MR1/mwrc1OAS1_0132_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0053_MR1/mwrc1OAS1_0
053_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nile
arn_data/oasis1/OAS1_0195_MR1/mwrc1OAS1_0195_MR1_mpr_anon_fslswapdim_bet.nii.gz'
]
\[NiftiMasker.wrapped] Computing mask
________________________________________________________________________________
[Memory] Calling nilearn.masking.compute_background_mask...
compute_background_mask([ '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0052_MR1/mwrc1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0155_MR1/mwrc1OAS1_0155_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0221_MR1/mwrc1OAS1_0221_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0121_MR1/mwrc1OAS1_0121_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0070_MR1/mwrc1OAS1_0070..., verbose=0)
__________________________________________compute_background_mask - 1.6s, 0.0min
\[NiftiMasker.wrapped] Resampling mask
________________________________________________________________________________
[Memory] Calling nilearn.image.resampling.resample_img...
resample_img(<nibabel.nifti1.Nifti1Image object at 0x7f2b27ff37f0>, target_affine=None, target_shape=None, copy=False, interpolation='nearest')
_____________________________________________________resample_img - 0.0s, 0.0min
\[NiftiMasker.wrapped] Finished fit
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_oasis_vbm.py:94: FutureWarning:

boolean values for 'standardize' will be deprecated in nilearn 0.15.0.
Use 'zscore_sample' instead of 'True' or use 'None' instead of 'False'.

________________________________________________________________________________
[Memory] Calling nilearn.maskers.nifti_masker.filter_and_mask...
filter_and_mask([ '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0052_MR1/mwrc1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0155_MR1/mwrc1OAS1_0155_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0221_MR1/mwrc1OAS1_0221_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0121_MR1/mwrc1OAS1_0121_MR1_mpr_anon_fslswapdim_bet.nii.gz',
  '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0070_MR1/mwrc1OAS1_0070...,
<nibabel.nifti1.Nifti1Image object at 0x7f2b27ff37f0>, { 'clean_args': None,
  'clean_kwargs': {},
  'cmap': 'gray',
  'detrend': False,
  'dtype': None,
  'high_pass': None,
  'high_variance_confounds': False,
  'low_pass': None,
  'reports': True,
  'runs': None,
  'smoothing_fwhm': 2,
  'standardize': False,
  'standardize_confounds': True,
  't_r': None,
  'target_affine': None,
  'target_shape': None}, memory_level=1, memory=Memory(location=nilearn_cache/joblib), verbose=1, confounds=None, sample_mask=None, copy=True, sklearn_output_config=None)
\[NiftiMasker.wrapped] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f2afe2e3280>
\[NiftiMasker.wrapped] Smoothing images
\[NiftiMasker.wrapped] Extracting region signals
\[NiftiMasker.wrapped] Cleaning extracted signals
__________________________________________________filter_and_mask - 5.9s, 0.1min
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_oasis_vbm.py:101: UserWarning:

Casting boolean input to <class 'numpy.int32'>

\[NiftiMasker.inverse_transform] Computing image from signals
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([0, ..., 0], dtype=int32), <nibabel.nifti1.Nifti1Image object at 0x7f2b27ff37f0>)
___________________________________________________________unmask - 0.0s, 0.0min

Prediction pipeline with ANOVA and SVR using DecoderRegressor Object

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=<some_high_value> 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=2,
    verbose=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(f"explained variance for the cross-validation: {prediction_score:f}")
print()

\[DecoderRegressor.fit] Mask volume = 1.21634e+06mm^3 = 1216.34cm^3
\[DecoderRegressor.fit] Standard brain volume = 1.88299e+06mm^3
\[DecoderRegressor.fit] Original screening-percentile: 1
\[DecoderRegressor.fit] Corrected screening-percentile: 1.54807
\[DecoderRegressor.fit] The decoding model will be trained on 1520 features.
\[DecoderRegressor.fit] The decoding model will be trained on 1520 features.
[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=2)]: Done  10 out of  10 | elapsed:    2.5s finished
=== DECODER ===
explained variance for the cross-validation: 11.774939

Visualization

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],
    title="SVM weights",
)
show()
plot oasis vbm

Visualize the quality of predictions

import matplotlib.pyplot as plt

plt.figure(figsize=(6, 4.5))
plt.suptitle(f"Decoder: Mean Absolute Error {prediction_score:.2f} years")
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")
  • Decoder: Mean Absolute Error 11.77 years
  • plot oasis vbm
<matplotlib.legend.Legend object at 0x7f2afe2e1810>

Inference with massively univariate model

print("Massively univariate model")

gm_maps_masked = NiftiMasker(verbose=1).fit_transform(
    gray_matter_map_filenames
)
data = variance_threshold.fit_transform(gm_maps_masked)

# Statistical inference
from nilearn.mass_univariate import permuted_ols

# This can be changed to use more CPUs.
output = 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=2,
)
neg_log_pvals = output["logp_max_t"]
t_scores_original_data = output["t"]
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

n_detections = (get_data(signed_neg_log_pvals_unmasked) > threshold).sum()

title = (
    "Negative $\\log_{10}$ p-values\n(Non-parametric + max-type correction)"
    f"\n{int(n_detections)} detections"
)

plot_stat_map(
    signed_neg_log_pvals_unmasked,
    bg_img=bg_filename,
    threshold=threshold,
    display_mode="z",
    cut_coords=[z_slice],
    figure=plt.figure(figsize=(5.5, 7.5), facecolor="k"),
    title=title,
)

show()
plot oasis vbm
Massively univariate model
\[NiftiMasker.wrapped] Loading data from ['/home/runner/work/nilearn/nilearn/nil
earn_data/oasis1/OAS1_0001_MR1/mwrc1OAS1_0001_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0002_MR1/mwrc1OAS
1_0002_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/n
ilearn_data/oasis1/OAS1_0003_MR1/mwrc1OAS1_0003_MR1_mpr_anon_fslswapdim_bet.nii.
gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0004_MR1/mwrc1O
AS1_0004_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn
/nilearn_data/oasis1/OAS1_0005_MR1/mwrc1OAS1_0005_MR1_mpr_anon_fslswapdim_bet.ni
i.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0006_MR1/mwrc
1OAS1_0006_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilea
rn/nilearn_data/oasis1/OAS1_0007_MR1/mwrc1OAS1_0007_MR1_mpr_anon_fslswapdim_bet.
nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0009_MR1/mw
rc1OAS1_0009_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nil
earn/nilearn_data/oasis1/OAS1_0010_MR1/mwrc1OAS1_0010_MR1_mpr_anon_fslswapdim_be
t.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0011_MR1/
mwrc1OAS1_0011_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/n
ilearn/nilearn_data/oasis1/OAS1_0012_MR1/mwrc1OAS1_0012_MR1_mpr_anon_fslswapdim_
bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0013_MR
1/mwrc1OAS1_0013_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn
/nilearn/nilearn_data/oasis1/OAS1_0014_MR1/mwrc1OAS1_0014_MR1_mpr_anon_fslswapdi
m_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0015_
MR1/mwrc1OAS1_0015_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilea
rn/nilearn/nilearn_data/oasis1/OAS1_0016_MR1/mwrc1OAS1_0016_MR1_mpr_anon_fslswap
dim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_001
7_MR1/mwrc1OAS1_0017_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nil
earn/nilearn/nilearn_data/oasis1/OAS1_0018_MR1/mwrc1OAS1_0018_MR1_mpr_anon_fslsw
apdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0
019_MR1/mwrc1OAS1_0019_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/n
ilearn/nilearn/nilearn_data/oasis1/OAS1_0020_MR1/mwrc1OAS1_0020_MR1_mpr_anon_fsl
swapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1
_0021_MR1/mwrc1OAS1_0021_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work
/nilearn/nilearn/nilearn_data/oasis1/OAS1_0022_MR1/mwrc1OAS1_0022_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OA
S1_0023_MR1/mwrc1OAS1_0023_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/wo
rk/nilearn/nilearn/nilearn_data/oasis1/OAS1_0025_MR1/mwrc1OAS1_0025_MR1_mpr_anon
_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/
OAS1_0026_MR1/mwrc1OAS1_0026_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/
work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0028_MR1/mwrc1OAS1_0028_MR1_mpr_an
on_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis
1/OAS1_0029_MR1/mwrc1OAS1_0029_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runne
r/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0030_MR1/mwrc1OAS1_0030_MR1_mpr_
anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oas
is1/OAS1_0031_MR1/mwrc1OAS1_0031_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/run
ner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0032_MR1/mwrc1OAS1_0032_MR1_mp
r_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/o
asis1/OAS1_0033_MR1/mwrc1OAS1_0033_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/r
unner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0034_MR1/mwrc1OAS1_0034_MR1_
mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data
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/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0037_MR1/mwrc1OAS1_0037_MR
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ta/oasis1/OAS1_0038_MR1/mwrc1OAS1_0038_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/ho
me/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0039_MR1/mwrc1OAS1_0039_
MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_
data/oasis1/OAS1_0040_MR1/mwrc1OAS1_0040_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/
home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0041_MR1/mwrc1OAS1_004
1_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilear
n_data/oasis1/OAS1_0042_MR1/mwrc1OAS1_0042_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0043_MR1/mwrc1OAS1_0
043_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nile
arn_data/oasis1/OAS1_0044_MR1/mwrc1OAS1_0044_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0045_MR1/mwrc1OAS1
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learn_data/oasis1/OAS1_0046_MR1/mwrc1OAS1_0046_MR1_mpr_anon_fslswapdim_bet.nii.g
z', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0047_MR1/mwrc1OA
S1_0047_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilearn/
nilearn_data/oasis1/OAS1_0049_MR1/mwrc1OAS1_0049_MR1_mpr_anon_fslswapdim_bet.nii
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OAS1_0050_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nilear
n/nilearn_data/oasis1/OAS1_0051_MR1/mwrc1OAS1_0051_MR1_mpr_anon_fslswapdim_bet.n
ii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0052_MR1/mwr
c1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/nile
arn/nilearn_data/oasis1/OAS1_0053_MR1/mwrc1OAS1_0053_MR1_mpr_anon_fslswapdim_bet
.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0054_MR1/m
wrc1OAS1_0054_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/ni
learn/nilearn_data/oasis1/OAS1_0055_MR1/mwrc1OAS1_0055_MR1_mpr_anon_fslswapdim_b
et.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0056_MR1
/mwrc1OAS1_0056_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/work/nilearn/
nilearn/nilearn_data/oasis1/OAS1_0058_MR1/mwrc1OAS1_0058_MR1_mpr_anon_fslswapdim
_bet.nii.gz', '/home/runner/work/nilearn/nilearn/nilearn_data/oasis1/OAS1_0059_M
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n/nilearn/nilearn_data/oasis1/OAS1_0060_MR1/mwrc1OAS1_0060_MR1_mpr_anon_fslswapd
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arn/nilearn/nilearn_data/oasis1/OAS1_0062_MR1/mwrc1OAS1_0062_MR1_mpr_anon_fslswa
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\[NiftiMasker.wrapped] Computing mask
\[NiftiMasker.wrapped] Resampling mask
\[NiftiMasker.wrapped] Finished fit
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_oasis_vbm.py:185: FutureWarning:

boolean values for 'standardize' will be deprecated in nilearn 0.15.0.
Use 'zscore_sample' instead of 'True' or use 'None' instead of 'False'.

\[NiftiMasker.wrapped] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f2b1b515ea0>
\[NiftiMasker.wrapped] Extracting region signals
\[NiftiMasker.wrapped] Cleaning extracted signals
[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:   36.4s remaining:    0.0s
[Parallel(n_jobs=2)]: Done   2 out of   2 | elapsed:   36.4s finished
\[NiftiMasker.inverse_transform] Computing image from signals
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([[0., ..., 0.]]), <nibabel.nifti1.Nifti1Image object at 0x7f2b27ff37f0>)
___________________________________________________________unmask - 0.1s, 0.0min

Total running time of the script: (1 minutes 28.612 seconds)

Estimated memory usage: 6669 MB

Gallery generated by Sphinx-Gallery