Note

Go to the end to download the full example code. or to run this example in your browser via Binder

FREM on Jimura et al “mixed gambles” dataset

In this example, we use fast ensembling of regularized models (FREM) to solve a regression problem, predicting the gain level corresponding to each beta maps regressed from mixed gambles experiment. FREM uses an implicit spatial regularization through fast clustering and aggregates a high number of estimators trained on various splits of the training set, thus returning a very robust decoder at a lower computational cost than other spatially regularized methods.

To have more details, see: FREM: fast ensembling of regularized models for robust decoding.

See the dataset description for more information on the data used in this example.

Load the data from the Jimura mixed-gamble experiment

from nilearn.datasets import fetch_mixed_gambles

data = fetch_mixed_gambles(n_subjects=16)

zmap_filenames = data.zmaps
behavioral_target = data.gain.to_numpy().ravel()
mask_filename = data.mask_img

[fetch_mixed_gambles] Dataset created in
/home/runner/nilearn_data/jimura_poldrack_2012_zmaps
[fetch_mixed_gambles] Downloading data from
https://www.nitrc.org/frs/download.php/7229/jimura_poldrack_2012_zmaps.zip ...
[fetch_mixed_gambles] Downloaded 28164096 of 104293434 bytes (27.0%%,    2.7s
remaining)
[fetch_mixed_gambles] Downloaded 56991744 of 104293434 bytes (54.6%%,    1.7s
remaining)
[fetch_mixed_gambles] Downloaded 86089728 of 104293434 bytes (82.5%%,    0.6s
remaining)
[fetch_mixed_gambles]  ...done. (4 seconds, 0 min)

[fetch_mixed_gambles] Extracting data from
/home/runner/nilearn_data/jimura_poldrack_2012_zmaps/a4c8868ab5c651b8594da6f3204
ded3a/jimura_poldrack_2012_zmaps.zip...
[fetch_mixed_gambles] .. done.

Fit FREM

We compare both of these models to a pipeline ensembling many models

import warnings

from sklearn.exceptions import ConvergenceWarning

from nilearn.decoding import FREMRegressor

frem = FREMRegressor("svr", cv=10, standardize="zscore_sample", verbose=1)

with warnings.catch_warnings():
    warnings.filterwarnings(action="ignore", category=ConvergenceWarning)
    frem.fit(zmap_filenames, behavioral_target)

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[FREMRegressor.fit] Computing mask
[FREMRegressor.fit] Resamping mask
[FREMRegressor.fit] Finished fit
[FREMRegressor.fit] Loading data from <nibabel.nifti1.Nifti1Image object at
0x7f91f1a2acd0>
[FREMRegressor.fit] Extracting region signals
[FREMRegressor.fit] Cleaning extracted signals
/home/runner/work/nilearn/nilearn/examples/02_decoding/plot_mixed_gambles_frem.py:46: UserWarning: Brain mask is bigger than the standard human brain. This object is probably not tuned to be used on such data.
  frem.fit(zmap_filenames, behavioral_target)
[FREMRegressor.fit] Mask volume = 2.12333e+06mm^3 = 2123.33cm^3
[FREMRegressor.fit] Standard brain volume = 1.82724e+06mm^3
[FREMRegressor.fit] Original screening-percentile: 20
[FREMRegressor.fit] Corrected screening-percentile: 17.2111
[Parallel(n_jobs=1)]: Using backend SequentialBackend with 1 concurrent workers.
[Parallel(n_jobs=1)]: Done   1 out of   1 | elapsed:    2.1s remaining:    0.0s
[Parallel(n_jobs=1)]: Done  10 out of  10 | elapsed:   19.6s finished
[FREMRegressor.fit] Computing image from signals
[FREMRegressor.fit] Computing image from signals

Visualize FREM weights

from nilearn.plotting import plot_stat_map, show

plot_stat_map(
    frem.coef_img_["beta"],
    title="FREM",
    display_mode="yz",
    cut_coords=[20, -2],
    threshold=0.2,
)

show()
plot mixed gambles frem

We can observe that the coefficients map learnt by FREM is structured, due to the spatial regularity imposed by working on clusters and model ensembling. Although these maps have been thresholded for display, they are not sparse (i.e. almost all voxels have non-zero coefficients).

See also

other example using FREM, and related section of user guide.

Example use of TV-L1 SpaceNet

SpaceNet is another method available in Nilearn to decode with spatially sparse models. Depending on the penalty that is used, it yields either very structured maps (TV-L1) or unstructured maps (graph_net). Because of their heavy computational costs, these methods are not demonstrated on this example but you can try them easily if you have a few minutes. Example code is included below.

from nilearn.decoding import SpaceNetRegressor

# We use the regressor object since the task is to predict a continuous
# variable (gain of the gamble).

tv_l1 = SpaceNetRegressor(
    mask=mask_filename,
    penalty="tv-l1",
    eps=1e-1,  # prefer large alphas
    memory="nilearn_cache",
    n_jobs=2,
)
# tv_l1.fit(zmap_filenames, behavioral_target)
# plot_stat_map(tv_l1.coef_img_, title="TV-L1", display_mode="yz",
#               cut_coords=[20, -2])

Total running time of the script: (0 minutes 31.561 seconds)

Estimated memory usage: 2770 MB

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