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 found in /home/runner/nilearn_data/oasis1
First gray-matter anatomy image (3D) is located at: /home/runner/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/nilearn_data/oasis1/OAS1_0001_MR1/mwrc2OAS1_0001_MR1_mpr_anon_fslswapdim_bet.nii.gz
Preprocess data¶
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)
# 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=0.01)
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())
\[NiftiMasker.wrapped] Loading data from ['/home/runner/nilearn_data/oasis1/OAS1
_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0052_MR1/mwrc1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0155_MR1/mwrc1OAS1_0155_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0221_MR1/mwrc1OAS1_
0221_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0121_MR1/mwrc1OAS1_0121_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0070_MR1/mwrc1OAS1_0070_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0003_MR1/mwrc1OAS1_0003_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0067_MR1/mwrc1OAS1_
0067_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0152_MR1/mwrc1OAS1_0152_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0224_MR1/mwrc1OAS1_0224_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0049_MR1/mwrc1OAS1_0049_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0012_MR1/mwrc1OAS1_
0012_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0220_MR1/mwrc1OAS1_0220_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0081_MR1/mwrc1OAS1_0081_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0223_MR1/mwrc1OAS1_0223_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0202_MR1/mwrc1OAS1_
0202_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0199_MR1/mwrc1OAS1_0199_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0141_MR1/mwrc1OAS1_0141_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0105_MR1/mwrc1OAS1_0105_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0126_MR1/mwrc1OAS1_
0126_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0179_MR1/mwrc1OAS1_0179_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0216_MR1/mwrc1OAS1_0216_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0056_MR1/mwrc1OAS1_0056_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0001_MR1/mwrc1OAS1_
0001_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0106_MR1/mwrc1OAS1_0106_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0124_MR1/mwrc1OAS1_0124_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0107_MR1/mwrc1OAS1_0107_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0072_MR1/mwrc1OAS1_
0072_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0189_MR1/mwrc1OAS1_0189_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0046_MR1/mwrc1OAS1_0046_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0077_MR1/mwrc1OAS1_0077_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0055_MR1/mwrc1OAS1_
0055_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0054_MR1/mwrc1OAS1_0054_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0096_MR1/mwrc1OAS1_0096_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0015_MR1/mwrc1OAS1_0015_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0182_MR1/mwrc1OAS1_
0182_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0026_MR1/mwrc1OAS1_0026_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0210_MR1/mwrc1OAS1_0210_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0151_MR1/mwrc1OAS1_0151_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0022_MR1/mwrc1OAS1_
0022_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0017_MR1/mwrc1OAS1_0017_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0087_MR1/mwrc1OAS1_0087_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0116_MR1/mwrc1OAS1_0116_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0059_MR1/mwrc1OAS1_
0059_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0112_MR1/mwrc1OAS1_0112_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0085_MR1/mwrc1OAS1_0085_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0004_MR1/mwrc1OAS1_0004_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0131_MR1/mwrc1OAS1_
0131_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0185_MR1/mwrc1OAS1_0185_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0226_MR1/mwrc1OAS1_0226_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0007_MR1/mwrc1OAS1_0007_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0076_MR1/mwrc1OAS1_
0076_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0095_MR1/mwrc1OAS1_0095_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0136_MR1/mwrc1OAS1_0136_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0176_MR1/mwrc1OAS1_0176_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0193_MR1/mwrc1OAS1_
0193_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0177_MR1/mwrc1OAS1_0177_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0103_MR1/mwrc1OAS1_0103_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0227_MR1/mwrc1OAS1_0227_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0013_MR1/mwrc1OAS1_
0013_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0134_MR1/mwrc1OAS1_0134_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0114_MR1/mwrc1OAS1_0114_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0040_MR1/mwrc1OAS1_0040_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0064_MR1/mwrc1OAS1_
0064_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0073_MR1/mwrc1OAS1_0073_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0002_MR1/mwrc1OAS1_0002_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0135_MR1/mwrc1OAS1_0135_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0183_MR1/mwrc1OAS1_
0183_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0047_MR1/mwrc1OAS1_0047_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0117_MR1/mwrc1OAS1_0117_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0147_MR1/mwrc1OAS1_0147_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0197_MR1/mwrc1OAS1_
0197_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0019_MR1/mwrc1OAS1_0019_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0043_MR1/mwrc1OAS1_0043_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0148_MR1/mwrc1OAS1_0148_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0060_MR1/mwrc1OAS1_
0060_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
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arn_data/oasis1/OAS1_0143_MR1/mwrc1OAS1_0143_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0039_MR1/mwrc1OAS1_0039_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0032_MR1/mwrc1OAS1_
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arn_data/oasis1/OAS1_0169_MR1/mwrc1OAS1_0169_MR1_mpr_anon_fslswapdim_bet.nii.gz'
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lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0188_MR1/mwrc1OAS1_
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arn_data/oasis1/OAS1_0200_MR1/mwrc1OAS1_0200_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0037_MR1/mwrc1OAS1_0037_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0160_MR1/mwrc1OAS1_
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arn_data/oasis1/OAS1_0165_MR1/mwrc1OAS1_0165_MR1_mpr_anon_fslswapdim_bet.nii.gz'
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arn_data/oasis1/OAS1_0088_MR1/mwrc1OAS1_0088_MR1_mpr_anon_fslswapdim_bet.nii.gz'
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arn_data/oasis1/OAS1_0080_MR1/mwrc1OAS1_0080_MR1_mpr_anon_fslswapdim_bet.nii.gz'
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arn_data/oasis1/OAS1_0091_MR1/mwrc1OAS1_0091_MR1_mpr_anon_fslswapdim_bet.nii.gz'
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arn_data/oasis1/OAS1_0044_MR1/mwrc1OAS1_0044_MR1_mpr_anon_fslswapdim_bet.nii.gz'
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arn_data/oasis1/OAS1_0078_MR1/mwrc1OAS1_0078_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0098_MR1/mwrc1OAS1_0098_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0041_MR1/mwrc1OAS1_
0041_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
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arn_data/oasis1/OAS1_0011_MR1/mwrc1OAS1_0011_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0115_MR1/mwrc1OAS1_0115_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0075_MR1/mwrc1OAS1_
0075_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1
_0217_MR1/mwrc1OAS1_0217_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nile
arn_data/oasis1/OAS1_0132_MR1/mwrc1OAS1_0132_MR1_mpr_anon_fslswapdim_bet.nii.gz'
, '/home/runner/nilearn_data/oasis1/OAS1_0053_MR1/mwrc1OAS1_0053_MR1_mpr_anon_fs
lswapdim_bet.nii.gz', '/home/runner/nilearn_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/nilearn_data/oasis1/OAS1_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0052_MR1/mwrc1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0155_MR1/mwrc1OAS1_0155_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0221_MR1/mwrc1OAS1_0221_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0121_MR1/mwrc1OAS1_0121_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0070_MR1/mwrc1OAS1_0070_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0003_MR1/mwrc1OAS1_0003_MR1_mpr_anon_fslswapdim..., verbose=0)
__________________________________________compute_background_mask - 1.5s, 0.0min
\[NiftiMasker.wrapped] Resampling mask
________________________________________________________________________________
[Memory] Calling nilearn.image.resampling.resample_img...
resample_img(<nibabel.nifti1.Nifti1Image object at 0x7fc317c2cca0>, 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:89: 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/nilearn_data/oasis1/OAS1_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0052_MR1/mwrc1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0155_MR1/mwrc1OAS1_0155_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0221_MR1/mwrc1OAS1_0221_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0121_MR1/mwrc1OAS1_0121_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0070_MR1/mwrc1OAS1_0070_MR1_mpr_anon_fslswapdim_bet.nii.gz',
'/home/runner/nilearn_data/oasis1/OAS1_0003_MR1/mwrc1OAS1_0003_MR1_mpr_anon_fslswapdim...,
<nibabel.nifti1.Nifti1Image object at 0x7fc317c2cca0>, { '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, dtype=None, sklearn_output_config=None)
\[NiftiMasker.wrapped] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7fc33f0678e0>
\[NiftiMasker.wrapped] Smoothing images
\[NiftiMasker.wrapped] Extracting region signals
\[NiftiMasker.wrapped] Cleaning extracted signals
__________________________________________________filter_and_mask - 5.7s, 0.1min
\[NiftiMasker.inverse_transform] Computing image from signals
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([False, ..., False]), <nibabel.nifti1.Nifti1Image object at 0x7fc317c2cca0>)
___________________________________________________________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] Loading mask from <nibabel.nifti1.Nifti1Image object at
0x7fc366db0ee0>
\[DecoderRegressor.fit] Loading data from ['/home/runner/nilearn_data/oasis1/OAS
1_0211_MR1/mwrc1OAS1_0211_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0052_MR1/mwrc1OAS1_0052_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0155_MR1/mwrc1OAS1_0155_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0221_MR1/mwrc1OAS1
_0221_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS
1_0121_MR1/mwrc1OAS1_0121_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0070_MR1/mwrc1OAS1_0070_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0003_MR1/mwrc1OAS1_0003_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0067_MR1/mwrc1OAS1
_0067_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS
1_0152_MR1/mwrc1OAS1_0152_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0224_MR1/mwrc1OAS1_0224_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0049_MR1/mwrc1OAS1_0049_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0012_MR1/mwrc1OAS1
_0012_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS
1_0220_MR1/mwrc1OAS1_0220_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0081_MR1/mwrc1OAS1_0081_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0223_MR1/mwrc1OAS1_0223_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0202_MR1/mwrc1OAS1
_0202_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS
1_0199_MR1/mwrc1OAS1_0199_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0141_MR1/mwrc1OAS1_0141_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0105_MR1/mwrc1OAS1_0105_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0126_MR1/mwrc1OAS1
_0126_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS
1_0179_MR1/mwrc1OAS1_0179_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0216_MR1/mwrc1OAS1_0216_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0056_MR1/mwrc1OAS1_0056_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0001_MR1/mwrc1OAS1
_0001_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS
1_0106_MR1/mwrc1OAS1_0106_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0124_MR1/mwrc1OAS1_0124_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0107_MR1/mwrc1OAS1_0107_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0072_MR1/mwrc1OAS1
_0072_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS
1_0189_MR1/mwrc1OAS1_0189_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0046_MR1/mwrc1OAS1_0046_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0077_MR1/mwrc1OAS1_0077_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0055_MR1/mwrc1OAS1
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1_0054_MR1/mwrc1OAS1_0054_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0096_MR1/mwrc1OAS1_0096_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0015_MR1/mwrc1OAS1_0015_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0182_MR1/mwrc1OAS1
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1_0026_MR1/mwrc1OAS1_0026_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0210_MR1/mwrc1OAS1_0210_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0151_MR1/mwrc1OAS1_0151_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0022_MR1/mwrc1OAS1
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1_0017_MR1/mwrc1OAS1_0017_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0087_MR1/mwrc1OAS1_0087_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0116_MR1/mwrc1OAS1_0116_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0059_MR1/mwrc1OAS1
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1_0112_MR1/mwrc1OAS1_0112_MR1_mpr_anon_fslswapdim_bet.nii.gz', '/home/runner/nil
earn_data/oasis1/OAS1_0085_MR1/mwrc1OAS1_0085_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0004_MR1/mwrc1OAS1_0004_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0131_MR1/mwrc1OAS1
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earn_data/oasis1/OAS1_0226_MR1/mwrc1OAS1_0226_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0007_MR1/mwrc1OAS1_0007_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0076_MR1/mwrc1OAS1
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earn_data/oasis1/OAS1_0136_MR1/mwrc1OAS1_0136_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0176_MR1/mwrc1OAS1_0176_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0193_MR1/mwrc1OAS1
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earn_data/oasis1/OAS1_0103_MR1/mwrc1OAS1_0103_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0227_MR1/mwrc1OAS1_0227_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0013_MR1/mwrc1OAS1
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earn_data/oasis1/OAS1_0114_MR1/mwrc1OAS1_0114_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0040_MR1/mwrc1OAS1_0040_MR1_mpr_anon_f
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', '/home/runner/nilearn_data/oasis1/OAS1_0135_MR1/mwrc1OAS1_0135_MR1_mpr_anon_f
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earn_data/oasis1/OAS1_0117_MR1/mwrc1OAS1_0117_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0147_MR1/mwrc1OAS1_0147_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0197_MR1/mwrc1OAS1
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earn_data/oasis1/OAS1_0043_MR1/mwrc1OAS1_0043_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0148_MR1/mwrc1OAS1_0148_MR1_mpr_anon_f
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earn_data/oasis1/OAS1_0143_MR1/mwrc1OAS1_0143_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0039_MR1/mwrc1OAS1_0039_MR1_mpr_anon_f
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earn_data/oasis1/OAS1_0169_MR1/mwrc1OAS1_0169_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0035_MR1/mwrc1OAS1_0035_MR1_mpr_anon_f
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earn_data/oasis1/OAS1_0200_MR1/mwrc1OAS1_0200_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0037_MR1/mwrc1OAS1_0037_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0160_MR1/mwrc1OAS1
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earn_data/oasis1/OAS1_0165_MR1/mwrc1OAS1_0165_MR1_mpr_anon_fslswapdim_bet.nii.gz
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earn_data/oasis1/OAS1_0088_MR1/mwrc1OAS1_0088_MR1_mpr_anon_fslswapdim_bet.nii.gz
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slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0166_MR1/mwrc1OAS1
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earn_data/oasis1/OAS1_0080_MR1/mwrc1OAS1_0080_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0086_MR1/mwrc1OAS1_0086_MR1_mpr_anon_f
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', '/home/runner/nilearn_data/oasis1/OAS1_0212_MR1/mwrc1OAS1_0212_MR1_mpr_anon_f
slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0198_MR1/mwrc1OAS1
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earn_data/oasis1/OAS1_0044_MR1/mwrc1OAS1_0044_MR1_mpr_anon_fslswapdim_bet.nii.gz
', '/home/runner/nilearn_data/oasis1/OAS1_0065_MR1/mwrc1OAS1_0065_MR1_mpr_anon_f
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slswapdim_bet.nii.gz', '/home/runner/nilearn_data/oasis1/OAS1_0041_MR1/mwrc1OAS1
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/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_oasis_vbm.py:126: UserWarning:
[NiftiMasker.fit] Generation of a mask has been requested (imgs != None) while a mask was given at masker creation. Given mask will be used.
\[DecoderRegressor.fit] Resampling mask
\[DecoderRegressor.fit] Finished fit
\[DecoderRegressor.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7fc34d18cc10>
\[DecoderRegressor.fit] Extracting region signals
\[DecoderRegressor.fit] Cleaning extracted signals
\[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.
[Parallel(n_jobs=2)]: Using backend LokyBackend with 2 concurrent workers.
[Parallel(n_jobs=2)]: Done 10 out of 10 | elapsed: 2.3s finished
\[DecoderRegressor.fit] Computing image from signals
\[DecoderRegressor.fit] Computing image from signals
\[DecoderRegressor.predict] Loading data from <nibabel.nifti1.Nifti1Image object
at 0x7fc340562320>
\[DecoderRegressor.predict] Extracting region signals
\[DecoderRegressor.predict] Cleaning extracted signals
=== 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()
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")
<matplotlib.legend.Legend object at 0x7fc3225059c0>
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()
Massively univariate model
\[NiftiMasker.wrapped] Loading data from ['/home/runner/nilearn_data/oasis1/OAS1
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arn_data/oasis1/OAS1_0006_MR1/mwrc1OAS1_0006_MR1_mpr_anon_fslswapdim_bet.nii.gz'
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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:184: 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
0x7fc2ffd129b0>
\[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: 32.0s remaining: 0.0s
[Parallel(n_jobs=2)]: Done 2 out of 2 | elapsed: 32.0s finished
\[NiftiMasker.inverse_transform] Computing image from signals
________________________________________________________________________________
[Memory] Calling nilearn.masking.unmask...
unmask(array([[0., ..., 0.]]), <nibabel.nifti1.Nifti1Image object at 0x7fc317c2cca0>)
___________________________________________________________unmask - 0.1s, 0.0min
Total running time of the script: (1 minutes 13.117 seconds)
Estimated memory usage: 5203 MB
