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
/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/latest/lib/python3.7/site-packages/nilearn/image/resampling.py:616: 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. fill_value=fill_value)
All studies now have Z maps!
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/envs/latest/lib/python3.7/site-packages/nilearn/plotting/img_plotting.py:300: FutureWarning: Default resolution of the MNI template will change from 2mm to 1mm in version 0.10.0 anat_img = load_mni152_template() /home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/latest/lib/python3.7/site-packages/nilearn/_utils/niimg.py:64: UserWarning: Non-finite values detected. These values will be replaced with zeros. "Non-finite values detected. "
Total running time of the script: ( 0 minutes 37.822 seconds)