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,
)
  • 01 plot cbma
  • 01 plot cbma

Out:

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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fba23932750>

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,
)
  • 01 plot cbma
  • 01 plot cbma

Out:

/home/docs/checkouts/readthedocs.org/user_builds/nimare/checkouts/latest/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/latest/nimare/meta/cbma/mkda.py:398: RuntimeWarning: invalid value encountered in true_divide
  pFgA_prior = pAgF * self.prior / pAgF_prior

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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,
)
  • 01 plot cbma
  • 01 plot cbma

Out:

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  return new_img_like(mask_img, unmasked, affine)

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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,
)
  • 01 plot cbma
  • 01 plot cbma

Out:

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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fba22c2d510>

Specific Co-Activation Likelihood Estimation

Important

The SCALE algorithm is very memory intensive, so we don’t run it within the documentation.

import numpy as np

from nimare.meta.cbma.ale import SCALE
from nimare.utils import vox2mm

xyz = vox2mm(
    np.vstack(np.where(dset.masker.mask_img.get_fdata())).T,
    dset.masker.mask_img.affine,
)

meta = SCALE(xyz=xyz, n_iters=10)
results = meta.fit(dset)

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,
)
01 plot cbma

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<nilearn.plotting.displays._slicers.OrthoSlicer object at 0x7fba235b08d0>

Total running time of the script: ( 1 minutes 19.802 seconds)

Gallery generated by Sphinx-Gallery