def download_data():
"""
Download and save 21 pain studies from NeuroVault to test IBMA functions.
"""
nidm_path = download_nidm_pain()
pytest.dset_dir = nidm_path
return nidm_path
The Corrector class =================== Here we take a look at multiple comparisons correction in meta-analyses. """ import matplotlib.pyplot as plt import seaborn as sns from nilearn.plotting import plot_stat_map ############################################################################### # Download data # ----------------------------------------------------------------------------- from nimare.extract import download_nidm_pain dset_dir = download_nidm_pain() ############################################################################### # Load Dataset # ----------------------------------------------------------------------------- import os from nimare.dataset import Dataset from nimare.utils import get_resource_path dset_file = os.path.join(get_resource_path(), "nidm_pain_dset.json") dset = Dataset(dset_file) dset.update_path(dset_dir) mask_img = dset.masker.mask_img
# It is worth noting that the non-likelihood-based estimators (i.e., DerSimonian-Laird and Hedges) have a closed-form solution, and are implemented in an extremely efficient way in NiMARE (i.e., computation is performed on all voxels in parallel).
# However, these estimators also produce more biased estimates under typical conditions (e.g., when sample sizes are very small), implying a tradeoff from the user's perspective.
#
# Alternatively, when users only have access to contrast maps and associated sample sizes, they can use the supported **Sample Size-Based Likelihood** estimator, which assumes that within-study variance is constant across studies, and uses maximum-likelihood or restricted maximum-likelihood to estimate between-study variance, as described in {cite:t}`Sangnawakij2019-mq`.
# When users have access only to contrast maps, they can use the **Permuted OLS** estimator, which uses ordinary least squares and employs a max-type permutation scheme for family-wise error correction {cite:p}`Freedman1983-ld,Anderson2001-uc` that has been validated on neuroimaging data {cite:p}`Winkler2014-wh` and relies on the nilearn library.
#
# Finally, when users only have access to z-score maps, they can use either the **Fisher's** {cite:p}`Fisher1925-zh` or the **Stouffer's** {cite:p}`Riley1949-uz` estimators.
# When sample size information is available, users may incorporate that information into the Stouffer's method, via the method described in {cite:t}`Zaykin2011-fs`.
# Given the paucity of image-based meta-analytic datasets, we have included the tools to build a Dataset from a NeuroVault collection of 21 pain studies, originally described in {cite:t}`Maumet2016-rr`.
# In[2]:
from nimare import dataset, extract, utils
dset_dir = extract.download_nidm_pain(data_dir=data_path, overwrite=False)
dset_file = os.path.join(utils.get_resource_path(), "nidm_pain_dset.json")
img_dset = dataset.Dataset(dset_file)
# Point the Dataset toward the images we've downloaded
img_dset.update_path(dset_dir)
# ## Transforming images
#
# Researchers may share their statistical maps in many forms, some of which are direct transformations of one another.
# For example, researchers may share test statistic maps with z-statistics or t-statistics, and, as long as we know the degrees of freedom associated with the t-test, we can convert between the two easily. To that end, NiMARE includes a class, {py:class}`~nimare.transforms.ImageTransformer`, which will calculate target image types from available ones, as long as the available images are compatible with said transformation.
#
# Here, we use `ImageTransformer` to calculate z-statistic and variance maps for all studies with compatible images.
# This allows us to apply more image-based meta-analysis algorithms to the `Dataset`.
# In[3]: