Note
Go to the end to download the full example code.
Use NeuroVault statistical maps in NiMARE
Download statistical maps from NeuroVault, then use them in a meta-analysis, with NiMARE.
import matplotlib.pyplot as plt
from nilearn.plotting import plot_stat_map
Conversion of Statistical Maps
Some of the statistical maps are T statistics and others are Z statistics.
To perform a Fisher’s meta analysis, we need all Z maps.
Thoughtfully, NiMARE has a class named ImageTransformer that will
help us.
from nimare.transforms import ImageTransformer
# Not all studies have Z maps!
dset.images[["z"]]
z_transformer = ImageTransformer(target="z")
dset = z_transformer.transform(dset)
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning: imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
warnings.warn(
[NiftiMasker.wrapped] Resampling images
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/image/resampling.py:668: RuntimeWarning: NaNs or infinite values are present in the data passed to resample. This is a bad thing as they make resampling ill-defined and much slower.
_resample_one_img(
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning: imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
warnings.warn(
[NiftiMasker.wrapped] Resampling images
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning: imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
warnings.warn(
[NiftiMasker.wrapped] Resampling images
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/maskers/nifti_masker.py:114: UserWarning: imgs are being resampled to the mask_img resolution. This process is memory intensive. You might want to provide a target_affine that is equal to the affine of the imgs or resample the mask beforehand to save memory and computation time.
warnings.warn(
[NiftiMasker.wrapped] Resampling images
All studies now have Z maps!
dset.images[["z"]]
Run a Meta-Analysis
With the missing Z maps filled in, we can run a Meta-Analysis and plot our results
from nimare.meta.ibma import Fishers
# The default template has a slightly different, but completely compatible,
# affine than the NeuroVault images, so we allow the Estimator to resample
# images during the fitting process.
meta = Fishers(resample=True)
meta_res = meta.fit(dset)
fig, ax = plt.subplots()
display = plot_stat_map(meta_res.get_map("z"), threshold=3.3, axes=ax, figure=fig)
fig.show()
# The result may look questionable, but this code provides
# a template on how to use neurovault in your meta analysis.
/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/stable/nimare/meta/ibma.py:118: FutureWarning: 'force_resample' will be set to 'True' by default in Nilearn 0.13.0.
Use 'force_resample=True' to suppress this warning.
else resample_to_img(nib.load(img), mask_img, **self._resample_kwargs)
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/image/resampling.py:805: FutureWarning: From release 0.13.0 onwards, this function will, by default, copy the header of the input image to the output. Currently, the header is reset to the default Nifti1Header. To suppress this warning and use the new behavior, set `copy_header=True`.
return resample_img(
/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/stable/nimare/meta/ibma.py:118: FutureWarning: 'force_resample' will be set to 'True' by default in Nilearn 0.13.0.
Use 'force_resample=True' to suppress this warning.
else resample_to_img(nib.load(img), mask_img, **self._resample_kwargs)
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/image/resampling.py:805: FutureWarning: From release 0.13.0 onwards, this function will, by default, copy the header of the input image to the output. Currently, the header is reset to the default Nifti1Header. To suppress this warning and use the new behavior, set `copy_header=True`.
return resample_img(
/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/stable/nimare/meta/ibma.py:118: FutureWarning: 'force_resample' will be set to 'True' by default in Nilearn 0.13.0.
Use 'force_resample=True' to suppress this warning.
else resample_to_img(nib.load(img), mask_img, **self._resample_kwargs)
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/image/resampling.py:805: FutureWarning: From release 0.13.0 onwards, this function will, by default, copy the header of the input image to the output. Currently, the header is reset to the default Nifti1Header. To suppress this warning and use the new behavior, set `copy_header=True`.
return resample_img(
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/numpy/_core/numeric.py:362: RuntimeWarning: invalid value encountered in cast
multiarray.copyto(a, fill_value, casting='unsafe')
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/stable/lib/python3.9/site-packages/nilearn/plotting/img_plotting.py:1416: UserWarning: Non-finite values detected. These values will be replaced with zeros.
safe_get_data(stat_map_img, ensure_finite=True),
Total running time of the script: (0 minutes 12.941 seconds)