Note
Click here to download the full example code
Coordinate-based meta-analysis algorithms
A tour of CBMA algorithms in NiMARE.
This tutorial is intended to provide a brief description and example of each of the CBMA algorithms implemented in NiMARE. For a more detailed introduction to the elements of a coordinate-based meta-analysis, see other stuff.
Load Dataset
Note
The data used in this example come from a collection of NIDM-Results packs downloaded from Neurovault collection 1425, uploaded by Dr. Camille Maumet.
Creation of the Dataset from the NIDM-Results packs was done with custom code. The Results packs for collection 1425 are not completely NIDM-Results-compliant, so the nidmresults library could not be used to facilitate data extraction.
import os
from nilearn.plotting import plot_stat_map
from nimare.correct import FWECorrector
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)
# Some of the CBMA algorithms compare two Datasets,
# so we'll split this example Dataset in half.
dset1 = dset.slice(dset.ids[:10])
dset2 = dset.slice(dset.ids[10:])
Multilevel Kernel Density Analysis
from nimare.meta.cbma.mkda import MKDADensity
meta = MKDADensity()
results = meta.fit(dset)
corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1)
cres = corr.transform(results)
plot_stat_map(
results.get_map("z"),
cut_coords=[0, 0, -8],
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
plot_stat_map(
cres.get_map("z_level-voxel_corr-FWE_method-montecarlo"),
cut_coords=[0, 0, -8],
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
Out:
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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd0529228d0>
MKDA Chi-Squared
from nimare.meta.cbma.mkda import MKDAChi2
meta = MKDAChi2(kernel__r=10)
results = meta.fit(dset1, dset2)
corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1)
cres = corr.transform(results)
plot_stat_map(
results.get_map("z_desc-consistency"),
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
plot_stat_map(
cres.get_map("z_desc-consistencySize_level-cluster_corr-FWE_method-montecarlo"),
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
Out:
/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/0.0.13/nimare/meta/cbma/mkda.py:392: RuntimeWarning: invalid value encountered in true_divide
pFgA = pAgF * pF / pA
/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/0.0.13/nimare/meta/cbma/mkda.py:398: RuntimeWarning: invalid value encountered in true_divide
pFgA_prior = pAgF * self.prior / pAgF_prior
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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd052870850>
Kernel Density Analysis
from nimare.meta.cbma.mkda import KDA
meta = KDA()
results = meta.fit(dset)
corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1)
cres = corr.transform(results)
plot_stat_map(
results.get_map("z"),
cut_coords=[0, 0, -8],
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
plot_stat_map(
cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo"),
cut_coords=[0, 0, -8],
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
Out:
0%| | 0/10 [00:00<?, ?it/s]/home/docs/checkouts/readthedocs.org/user_builds/nimare/envs/0.0.13/lib/python3.7/site-packages/nilearn/masking.py:858: UserWarning: Data array used to create a new image contains 64-bit ints. This is likely due to creating the array with numpy and passing `int` as the `dtype`. Many tools such as FSL and SPM cannot deal with int64 in Nifti images, so for compatibility the data has been converted to int32.
return new_img_like(mask_img, unmasked, affine)
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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd05266e350>
Activation Likelihood Estimation
from nimare.meta.cbma.ale import ALE
meta = ALE()
results = meta.fit(dset)
corr = FWECorrector(method="montecarlo", n_iters=10, n_cores=1)
cres = corr.transform(results)
plot_stat_map(
results.get_map("z"),
cut_coords=[0, 0, -8],
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
plot_stat_map(
cres.get_map("z_desc-size_level-cluster_corr-FWE_method-montecarlo"),
cut_coords=[0, 0, -8],
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd052986e10>
Specific Co-Activation Likelihood Estimation
Important
The SCALE algorithm is very memory intensive, so we don’t run it within the documentation.
ALE-Based Subtraction Analysis
from nimare.meta.cbma.ale import ALESubtraction
meta = ALESubtraction(n_iters=10, n_cores=1)
results = meta.fit(dset1, dset2)
plot_stat_map(
results.get_map("z_desc-group1MinusGroup2"),
cut_coords=[0, 0, -8],
draw_cross=False,
cmap="RdBu_r",
threshold=0.1,
)
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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fd044b88610>
Total running time of the script: ( 1 minutes 41.879 seconds)